GSD-Lite Worklog

# GSD-Lite Work Log

## 1. Current Understanding (Read First)

<current_mode>

execution

</current_mode>

<active_task>

Task: READER-001 - GSD-Lite Worklog Reader (Mobile-First)

Status: POC COMPLETE (READER-001a + READER-001b) - See LOG-048

Key insight: Purpose-built worklog browser with positional parsing (ID, TYPE only), collapsible outline, resizable sidebar overlay, desktop/mobile toggle.

Completed: Parser + HTML renderer with full UX

Remaining: READER-001c (Mermaid), READER-001d (scroll sync), READER-001e (CSS polish), READER-001f (E2E test)

Parked (resume after READER-001):

Task: TASK-EVAL-002 - Constitutional Evaluation Pipeline

Status: SCHEMA REFACTOR COMPLETE - See LOG-046

Next: TASK-EVAL-002d — Implement Layer 2 Vertex AI integration

</active_task>

<parked_tasks>

- TASK-EVAL-001: Build OpenCode session parser — SUPERSEDED by SQLite parser (LOG-045)

- TASK-CONST-002b: Write remaining rubrics (Pillar 1, 3, 4) — needed for full evaluation

- TASK-CI-L1-001: Implement Layer 1 structural checks — integrated into TASK-EVAL-002c

- RQ-3: Evaluate SKILLS.md pattern — defer until current architecture matures

</parked_tasks>

<vision>

GSD-Lite is a pair programming protocol where:

- Engineer (Driver): Owns decisions, curates what gets logged, stakes reputation on outcome

- Agent (Navigator): Proposes solutions, executes tasks, narrates reasoning

- Artifacts (Memory): Persist beyond sessions, become institutional knowledge

Core insight: Agents are brilliant but ephemeral. Engineers are permanent and must own/comprehend every decision.

Fork & resume workflow: Pair program → log rich findings → kill session when tokens rise → resume with fresh agent pointing at curated logs.

Distribution Goal: Unified "Hotel Model" — `gsd-lite install` (global) or `install --local` creates identical structure at different roots.

Token Budget: First-turn headroom is 10k tokens. Agent instructions must stay under this (currently 4,913) to leave capacity for actual work. See LOG-020.

</vision>

<decisions>

DECISION-004: Universal Onboarding in PROTOCOL.md

- Rationale: Fresh agents using direct workflow entry (e.g., discuss.md) lacked high-level context (PROJECT.md). Onboarding must be a universal boot sequence, not workflow-dependent.

DECISION-005: Decomission Sticky Note Protocol

- Rationale: Artifacts are now mature enough to be the source of truth. Repeating status in every turn adds noise/tokens without value.

DECISION-013: Unified Installation Model (LOG-013)

- Rationale: Single command structure for both global and local. Same output (opencode config + gsd-lite artifacts), different root. Smart check: skip user artifacts if exist, always overwrite templates. Simplifies mental model vs fragmented install/update/local/force flags.

DECISION-020a: Consolidate artifact documentation to agent instruction (LOG-020)

- Rationale: HTML comments in templates are invisible to grep-first onboarding. Agent instruction is ALWAYS loaded. Token budget allows: 4,913 current + 1,250 docs = ~6,163, under 10k headroom.

DECISION-020b: 10k token limit as CI gate (LOG-020)

- Rationale: Token count is deterministic and measurable. Use as fast gate before expensive LLM behavioral tests. Complements LOOP-001 (Intern Test).

DECISION-028a: Constitution-first approach for CI (LOG-028)

- Rationale: Distill immutable pillars into machine-auditable document. All other artifacts can evolve as long as they don't violate the Constitution. Decouples philosophy (stable) from implementation (evolving).

DECISION-028b: Three-layer CI architecture (LOG-028)

- Rationale: Order checks by cost — L1 structural (free), L2 constitutional (~50k tokens), L3 behavioral (~500k-1M tokens). Catch obvious breaks fast, reserve expensive LLM calls for philosophy/behavior testing.

DECISION-032a: Use OpenCode native JSON for eval data (LOG-032)

- Rationale: OpenCode already persists all session data to disk as JSON. Parse existing files instead of building custom instrumentation.

DECISION-032b: Vertex AI Gen AI Eval as primary judge (LOG-032) — SUPERSEDED BY DECISION-042b

- Rationale: Original decision based on "Bring Your Own Data" model. Superseded because Vertex AI trajectory metrics require reference_trajectory (golden path), but GSD-Lite Constitution defines behavioral patterns, not expected sequences.

DECISION-042a: Session as evaluation unit (LOG-042)

- Rationale: GSD-Lite is stateless by design. Each session is independent. Handoff (S1-H1) and onboarding (C3-H2) are session-scoped behaviors.

DECISION-042b: Promptfoo with llm-rubric as primary evaluation platform (LOG-042) — SUPERSEDED BY DECISION-043a

- Rationale: Original decision favored Promptfoo for YAML-native rubrics. Superseded because Vertex AI rubric-based metrics offer adaptive rubrics + custom guidelines parameter, and user is Google Cloud partner seeking hands-on experience.

DECISION-043a: Vertex AI rubric-based metrics for Layer 2 evaluation (LOG-043)

- Rationale: Vertex's `GENERAL_QUALITY` with `guidelines` parameter can evaluate Constitution behaviors. Adaptive rubrics dynamically generate pass/fail tests per prompt. Agent-specific metrics (`TOOL_USE_QUALITY`) align with GSD-Lite's tool-heavy workflow.

DECISION-043b: Hybrid architecture — Programmatic L1 + Vertex L2 (LOG-043)

- Rationale: Deterministic checks (handoff presence, grep-before-read sequence) are free and fast in Python. Qualitative checks (reasoning quality, challenge tone) benefit from Vertex's adaptive rubric intelligence.

DECISION-043c: Constitution as guidelines parameter (LOG-043)

- Rationale: Inject distilled Constitution (P2-H, S1-H, J4-H*) directly into Vertex's `guidelines` parameter. No rubric format translation needed.

DECISION-042c: Hybrid orchestration — Option C (LOG-042)

- Rationale: Batch extract to individual files, evaluate each session independently, aggregate into summary. Enables surgical debugging + CI gates + re-runnable evals.

DECISION-042d: Turn-structured output schema (LOG-042)

- Rationale: Current flat schema (concatenated prompts/responses) can't support turn-level evaluation. New schema adds turns[] array with per-turn tools for LLM-as-judge correlation.

DECISION-033a: Fingerprint sessions via fs-mcp tool call paths (LOG-033)

- Rationale: When user spawns OpenCode from home dir but connects to different fs-mcp servers per session, project identity is invisible to OpenCode metadata. Extract absolute paths from tool outputs to fingerprint which project a session touched.

DECISION-045a: Migrate eval_ingest.py to sqlmodel (SQLite) (LOG-045)

- Rationale: OpenCode migrated from JSON files to SQLite. `pathlib.glob` logic is dead. `sqlmodel` provides typesafe ORM for the new `opencode.db` schema.

DECISION-045b: Update eval_transform.py to preserve full context (LOG-045)

- Rationale: Truncating responses to the last paragraph destroyed context for Vertex rubric evaluation. Rubric metrics like `GENERAL_QUALITY` need the full reasoning chain to be effective.

DECISION-046a: Implement Vertex-native turn-structured schema in `eval_ingest.py` (LOG-046)

- Rationale: Layer 1 checks failed on flat schema (losing turn context). Vertex `MULTI_TURN_GENERAL_QUALITY` requires structured turns. Refactored ingest to output `request.contents[]` directly.

DECISION-046b: Decommission `eval_transform.py` (LOG-046)

- Rationale: Transform layer existed only to bridge flat schema to Vertex format. Since ingest now outputs Vertex-native, the transform step is redundant complexity.

</decisions>

<blockers>

None - POC complete and working.

</blockers>

<next_action>

Fork paths (choose one):

1. READER-001c → Integrate Mermaid CLI (pre-render diagrams to base64 PNG)

2. READER-001d → Add scroll sync breadcrumb (IntersectionObserver)

3. Pivot to evaluation → Resume TASK-EVAL-002d (Vertex AI integration)

4. Test on mobile → AirDrop worklog.html, review mobile UX thoroughly

</next_action>

## 2. Key Events Index (Project Foundation)

Log ID | Type | Task | Summary
--- | --- | --- | ---
LOG-012 | DISCOVERY | DIST-002 | Reverse-engineered get-shit-done-cc installer patterns
LOG-013 | DECISION | DIST-002 | Unified install model: same output, different location
LOG-016 | DECISION | PROTOCOL-STATELESS | Stateless-First Architecture: Every turn generates handoff packet
LOG-017 | VISION | HOUSEKEEPING | Housekeeping Agent: Automated Coherence Detection for Dense Worklogs
LOG-020 | DISCOVERY | PROTOCOL-DOCS | 10k token budget as CI gate; HTML comments invisible to grep-first
LOG-028 | DECISION | CI-FRAMEWORK | ⭐ Constitutional Knowledge + Three-Layer CI (Structural → Constitutional → Behavioral)

## 3. Atomic Session Log (Chronological)

### [LOG-001] - [DISCOVERY] - Vision Archaeology: Unearthing GSD-Lite's True Purpose from Artifacts and PRs - Task: BOOTSTRAP-001

Timestamp: 2026-02-03 14:30

Details:

This session performed "vision archaeology" — reconstructing the true purpose of GSD-Lite by examining its evolution through planning artifacts, merged PRs, and a production deployment. What emerged was not a "session handoff framework" but something deeper: a pair programming protocol that ensures engineers own and comprehend every decision, even when agents do the heavy lifting.

The Investigation: Three Sources of Truth

We examined three artifact layers to understand what GSD-Lite had become:

1. Original PROJECT.md (`.planning/PROJECT.md`)

- Started as "Data Engineering Copilot Patterns" — a documentation project

- Core value: "Maintain ownership of the reasoning process"

- This was the seed, but the plant had grown beyond it

2. Merged PRs (7 PRs from GitHub)

- PR #10: Echo-back onboarding — agents must prove understanding before executing

- PR #8: Summary in headers — logs are grep-scannable

- PR #4: Workflow decomposition — 929-line monolith → 5 focused files

- PR #2: INIT_PROMPT — 120 lines vs 300+ docs for activation

- Pattern: Every PR optimized for agent comprehension and token efficiency

3. Production Deployment (Meltano pipeline, `/workspaces/.../estrid-meltano-el`)

- 26 rich log entries demonstrating the pattern in action

- LOG-017 "Time Traveler Bug" — journalism narrative with analogy

- LOG-025 "Blank String Philosophy" — decision with "Silent Nod" metaphor

- PR_DATA_339.md — PR description generated from WORK.md logs

The Revelation: Fork & Resume as Core Mechanic

The production deployment revealed a workflow not explicitly documented:

    SESSION 1 (tokens: 0 → 60k)
    ├── Pair program with agent
    ├── Hit finding → "Log this with journalism narrative"
    ├── Agent writes rich log entry
    ├── Tokens rising toward 80k
    └── FORK: Kill session, keep artifacts

    SESSION 2 (tokens: 0 → fresh)
    ├── "Read LOG-017, continue from there"
    ├── Agent onboards from curated artifact
    ├── Continue pair programming
    └── FORK again when needed

The Insight User Emphasized:

> "Agents are ephemeral however brilliant. It is the engineers that has the final stake and should hone their skill learning from the best."

This reframed everything. GSD-Lite isn't about making agents more effective — it's about making engineers more effective by:

1. Capturing agent brilliance in permanent artifacts

2. Forcing engineers to curate what's worth preserving

3. Creating onboarding docs that teach reasoning, not just outcomes

The "Silent Nod" Analogy (from LOG-025):

To explain why this matters, consider this analogy from the production logs:

Imagine you ask a colleague, "Do you want coffee?"

- NULL Response: They stare blankly. You don't know if they heard you. (Unknown)

- Empty String Response: They give a Silent Nod. No words, but the message is clear.

In GSD-Lite terms:

- Chat history is like the colleague's spoken words — ephemeral, expensive to replay

- WORK.md logs are like written meeting notes — permanent, searchable, teachable

An engineer reading LOG-025 doesn't just learn that empty strings mean "local currency" — they learn the reasoning pattern for how to think about data semantics. They can apply that pattern to the next ambiguous field they encounter.

The Logging Standard (Captured in PROJECT.md):

User provided the explicit prompt pattern for rich logging:

> "Please include specific code snippet, reasoning, extended context in a journalism narrative style so that whoever with 0 context can onboard and pick up this key record of decision without ambiguity or friction. Add a cherry on top to include exact/synthesized example to explain the concepts/findings mentioned so that other weaker reasoning agents can grasp topic with little friction and ambiguity."

This is now documented in PROJECT.md under "The Logging Standard" with a table of required elements:

- Narrative framing (hook the reader)

- The symptom (what went wrong)

- The evidence (concrete proof)

- The root cause (why it happened)

- The analogy (ELI5 for onboarding)

- The decision (what we chose)

- Code snippet (executable proof)

Decisions Made:

Decision | Rationale
--- | ---
Core value = "engineer ownership of reasoning" | Session continuity is mechanism, not purpose. Engineers stake reputation on code.
Added "Philosophy" section to PROJECT.md | The "why" deserves explicit articulation, not just implied through mechanics.
Added "Pair Programming Model" section | Fork & resume workflow was implicit — now documented with ASCII diagram.
Added "Logging Standard" section | Journalism narrative style is teachable, should be codified.

Files Modified:

- `gsd-lite/PROJECT.md` — Complete rewrite with new sections: Philosophy, Pair Programming Model, Logging Standard

- `gsd-lite/WORK.md` — This log entry + Current Understanding updated for checkpoint

Cherry on Top: The "Driver vs Passenger" Mental Model

To help weaker agents (or future engineers) understand GSD-Lite's philosophy:

Imagine learning to drive:

- Passenger mode: You sit in the back, agent drives. You arrive at the destination but couldn't drive yourself.

- Driver mode (GSD-Lite): Agent is in the passenger seat with the map. They suggest "turn left here because of traffic." You make the turn AND understand why.

Six months later:

- Passenger: "Why did we go this route?" "I don't know, the agent chose it."

- Driver: "We avoided Main Street because it floods during rain. I learned that from the agent and now I always check weather before routing."

GSD-Lite ensures you're always in the driver seat — learning the "why" behind every decision, able to defend it, able to apply the pattern next time.

### [LOG-002] - [DISCOVERY] - Protocol: Mining User Patterns from Storage (Discarding Sub-Agents) - Task: PROTOCOL-001

Timestamp: 2026-02-03 15:15

Details:

This session established a rigorous protocol for mining user behavior from the `storage/` directory, specifically distinguishing between "pure" GSD-Lite sessions (manual fork) and multi-agent GSD sessions (programmatic fork).

The Discovery:

- Initial scan conflated `gsd-lite` sessions with `gsd` multi-agent sessions.

- `gsd` sessions use `Task()` calls to spawn sub-agents (`gsd-planner`, `gsd-executor`).

- `gsd-lite` sessions use manual session handoff ("Fork & Resume").

- Mixing these data points corrupts the insight into the user's manual workflow.

The Protocol for Future Agents (How to Mine Storage):

1. Discovery:

- List `storage/project` to find project contexts.

- Read project JSONs to identify relevant worktrees.

2. Filtering (CRITICAL):

- Read `storage/session/{proj_id}/*.json`.

- MUST filter for `agent: "gsd-lite"`.

- MUST discard `agent: "gsd-planner"`, `gsd-executor`, etc.

- Sort filtered sessions by date to find recent relevant work.

3. Reconstruction:

- Read `storage/message/{session_id}`.

- Read `storage/part/{message_id}` to get text content.

- Note: First message often contains the SYSTEM PROMPT injection in its text part. Look for the second message or distinct user parts for actual user input.

4. Insight Extraction:

- Look for "Fork" moments: Session Start → Read Context → Continue.

- Look for "Thinking Partner" moments: Agent challenging user assumptions vs just executing.

- Ignore "Spawn" moments: `Task()` tool calls are programmatic implementation details, not user behavior.

Why this matters:

Analyzing the wrong agent type leads to "optimizing the machine" instead of "optimizing the human". GSD-Lite is about the human workflow.

### [LOG-003] - [DISCOVERY] - The "Driver" Persona: Pattern Recognition from Storage Mining - Task: PROTOCOL-001

Timestamp: 2026-02-04 10:30

Details:

Mining the storage logs for `gsd-lite` sessions revealed a distinct "Driver/Navigator" usage pattern that defines this user's productivity flow. The user doesn't just "chat" — they execute a precise rigorous loop of context setting, validation, and Socratic inquiry.

The "Fork" Pattern (Token Optimization Strategy):

The most productive sessions start with a specific signature:

1. The Trigger: "Let's load protocol and continue..."

2. The Context: Explicit pointer to `WORK.md` or a specific file path.

3. The Intent: "I'm at this part... my question is..."

This is the manual fork. The user curated the artifacts in the previous session (the "Pit Stop") so the new agent (the "Fresh Driver") can start immediately at high speed without carrying the baggage (token cost) of the previous 50 turns.

The "Driver" Persona (User Behavior):

The user acts as the Lead Engineer (Driver) using the Agent as a Senior Technical Investigator (Navigator).

* Context-Heavy Prompts: Instead of "fix this," the user says "Look at `tmp/reference/.../streams.py`, I see X, but ticket says Y."

* Validation Queries: "Is it valueTextType or itemNumber?" — specific A/B testing of hypotheses.

* Mental Model Checks: "Does this mean we need to create a new plugin child?" — verifying understanding before coding.

The "Thinking Partner" Requirement:

The user explicitly prompted an agent to be a "Patient Coding Teacher" with "Socratic Methodology." They don't want silent fixes; they want to derive the answer.

Cherry on Top: The "Pit Stop" Analogy

To explain why this "Fork & Resume" pattern is so effective:

Imagine Formula 1 racing:

* The Car: The Project State (Code + Artifacts).

* The Driver: The Agent (Ephemeral).

* The Race Engineer: The User (Permanent).

In a long race (complex feature), tires wear out (context window fills up, reasoning degrades).

* Bad Strategy: Keep driving on bald tires. The driver gets confused, hallucinates, crashes.

* GSD-Lite Strategy: Pit Stop.

1. Box Box Box: User calls "checkpoint" or manual fork.

2. Tire Change: Old context window is dumped.

3. Fresh Tires: New agent starts, reads `WORK.md` (Telemetry), and exits the pit lane at full speed.

The car (project) is the same, but the driver (agent) is fresh and focused. The "Fork" is just a Pit Stop.

Code Snippet: The "Fork" Signature

Future agents can identify these high-value start points by looking for this pattern in the first user message:

    {
      "role": "user",
      "text": "let's load protocol and continue our work... I'm at [specific file path]..."
    }

Status: Discovery complete. Validated that questioning enhancement must support this "Driver" workflow (Technical Investigation & Mental Model Validation), not just high-level "Vision Extraction".

### [LOG-004] - [DECISION] - Storage Protocol v2: Direct Agent Filtering - Task: PROTOCOL-001

Timestamp: 2026-02-04 10:15

Context:

Refining the storage mining protocol from LOG-002 based on successful navigation.

The Finding:

- `storage/session/{id}.json` does NOT contain the `agent` field.

- `storage/message/{msg_id}.json` DOES contain `agent: "gsd-lite"`.

- `finish: "tool-calls"` in message JSON indicates no text output (silent execution).

- `finish: "stop"` indicates text output or completion.

Protocol v2 (The Efficient Path):

1. List Sessions: `ls storage/session/{proj_id}/` (sort by time if possible, or ID).

2. Sample First Message: Read `storage/message/{first_msg_id}.json`.

3. Check Agent: Filter for `agent: "gsd-lite"`.

4. Scan for Interaction:

- Look for messages with `role: "assistant"` AND `finish: "stop"`.

- Messages with `finish: "tool-calls"` are often silent execution loops.

- User patterns live in `role: "user"` message parts.

Why:

This saves token budget by avoiding reading full session contents for "ghost" sessions (sub-agents) or "silent" sessions (pure tool loops).

### [LOG-005] - [DISCOVERY] - User Pattern: The Socratic Engineer - Task: PROTOCOL-001

Context:

Analyzed deep session `ses_4011d8550...` where user pasted a "Patient Coding Teacher" persona.

The "Fingerprint" (How this user works):

1. Persona: "Patient Coding Teacher" / "Collaborative Mentor".

2. Core Mechanic: Socratic Inquiry. Don't just answer; ask questions to help derive the answer.

3. Decision Making: "The Menu Technique" — present numbered lists of options (Concept vs Code, Strategy A vs B).

4. Pacing: "Mandatory Confirmation Loops" — pause and verify before moving on.

5. Philosophy: "Concept Over Code" — establish mental models first.

Implication for GSD-Lite:

The "Questioning" enhancement isn't just about requirements gathering (OG GSD style). It must be about Coaching.

- Old Model: "What feature do you want?" -> "Here are the specs."

- New Model: "Why this feature?" -> "Have you considered X?" -> "Let's map the mental model."

Action: : New `questioning.md` reference will focus on "Thinking Partner" techniques, not just project scoping.

### [LOG-006] - [VISION] - The Questioning Enhancement: Learning Accelerator Through Socratic Pair Programming - Task: QUESTION-001

Timestamp: 2026-02-04

Context:

User initiated enhancement request: "The OG GSD framework has something great about its prompt engineering — the agent really asks the right questions." After analyzing OG GSD's `.claude/get-shit-done/references/questioning.md` and `.claude/commands/gsd/discuss-phase.md`, we interviewed the user to extract their specific vision for GSD-Lite.

The Core Discovery: GSD-Lite Is Not a Task Manager

Through Socratic dialogue, we derived that GSD-Lite's true purpose isn't task management or project planning — it's a learning accelerator. The artifacts (WORK.md, INBOX.md) aren't just logs; they're crystallized understanding that:

1. The user derived through dialogue (not received passively)

2. Are written so the user can explain them to anyone

3. Enable weaker agents (or future humans) to onboard without friction

The Golden Rule: "Always Ask WHY Before HOW"

This single behavioral instruction captures the essence of effective pair programming for this persona:

Situation | Without the Rule | With the Rule
--- | --- | ---
User says "add dark mode" | Agent starts implementing | "Why dark mode? User preference? Accessibility? Battery saving? This affects the approach."
Agent about to refactor | Just refactors | "I'm about to change X to Y. The WHY: [reason]. Does this match your mental model?"
Codebase uses unfamiliar pattern | Agent uses it silently | "I see the codebase uses [pattern]. Before I continue — want me to explain why this pattern exists here?"
User makes a decision | Agent accepts and executes | "You chose X over Y. I want to challenge that — have you considered [tradeoff]?"

The Pair Programming Model

    graph TD
        subgraph "The Triad"
            User["DRIVER (User)<br/>• Bring context<br/>• Make decisions<br/>• Own the reasoning<br/>• Curate what's logged"]
            Agent["NAVIGATOR (Agent)<br/>• Challenge assumptions<br/>• Teach concepts<br/>• Propose options with tradeoffs<br/>• Present plans before executing"]
            Artifacts["MEMORY (Artifacts)<br/>• Crystallized understanding<br/>• Zero-context onboarding<br/>• Weaker-agent-friendly"]
        end
        User <-->|Socratic Dialogue| Agent
        User -->|Curate| Artifacts
        Agent -->|Draft| Artifacts
        Artifacts -.->|Onboard| Agent

The 10-Star Experience (User's Own Words)

> "The 10-star experience was when I probed it to detour and go down a rabbit hole asking about a new concept that the codebase used. Asking it to explain, put on its teaching hat, explored the codebase and made the connections and distilled it down to layman level with concrete codebase snippets to solidify the concept for me."

This is the signature of a great session:

1. User notices something unfamiliar → pauses execution

2. Agent puts on teaching hat → explores, connects, distills

3. User gains understanding → now OWNS the knowledge

4. User prompts logging → journalism-style entry preserved

5. User forks session → fresh agent resumes with crystallized context

The Challenge Tone Protocol (Context-Dependent)

The user wants an agent that challenges assumptions — but the tone must match the situation:

Tone | When to Use | Trigger
--- | --- | ---
(A) Gentle Probe | User stated preference without reasoning. Early in discussion. | "Interesting — what draws you to X here?"
(B) Direct Challenge | High stakes, clear downside, trust established. | "I'd push back hard here. [Reason]. Let's do Y instead."
(C) Menu with Devil's Advocate | Genuine tradeoff, no obvious right answer. | "X (your instinct) vs Y (counterpoint). Tradeoffs: [list]. Which fits?"
(D) Socratic Counter-Question | User confident but has blind spot. Teaching moment. | "If we go with X, what happens when [edge case]?"

Decision Tree for Agent:

    flowchart TD
        Start([User states decision]) --> Why{Explained WHY?}
        Why -- No --> A[A: Gentle Probe]
        Why -- Yes --> Blind{Blind spot?}
        Blind -- Yes --> D[D: Socratic Counter-Question]
        Blind -- No --> Tradeoff{Genuine Tradeoff?}
        Tradeoff -- Yes --> C[C: Menu w/ Devil's Advocate]
        Tradeoff -- No --> Stakes{High Stakes?}
        Stakes -- Yes --> B[B: Direct Challenge]
        Stakes -- No --> Accept([Accept & Continue])

Key User Insight: "Writing Is Thinking"

> "The fastest way to learn anything for me is to get my hands dirty, to write out my thoughts, albeit to an agent. Writing is thinking. That is the most valuable thing I find about working projects with an agent helping me."

The agent isn't a coder-for-hire — it's a sparring partner for thinking. When the user writes their thoughts to the agent, they're clarifying their own mental model. When the agent challenges back, it sharpens further.

The Anti-Pattern: Eager Executor

The user described a recurring frustration: agents that skip the "pause and discuss" step. They execute ahead of their plan, making assumptions instead of asking. The user has to manually pause and ask "why that approach?" — then fork the session with the reasoning baked into WORK.md.

Solution: Workflows must instruct agents to present plans before executing, and pause when approaching decisions the user might care about.

Architectural Decision: Merge Moodboard + Whiteboard → Discuss

The current moodboard/whiteboard split assumes:

- User has fuzzy idea that needs extracting (moodboard)

- User needs formal plan presented (whiteboard)

- Sequential phases: Dream → Plan → Execute

But the user's actual pattern is:

- Context-rich starts ("I'm at this part, my question is...")

- Continuous dialogue where understanding and planning interweave

- Teaching moments emerge organically, not in a "moodboard phase"

Decision: Replace moodboard.md + whiteboard.md with unified `discuss.md` that adapts to context. Create `questioning.md` reference file with philosophy + techniques.

User Controls for Hyper-Focus Context Engineering

User explicitly wants gatekeeper control over artifact writes:

- Every write costs tokens and narrows context window

- Agent should ask "Want me to capture this?" not auto-write

- Logging is a deliberate act of curation, not automatic journaling

Next Actions:

1. Create `gsd-lite/template/references/questioning.md` — philosophy + techniques

2. Create `gsd-lite/template/workflows/discuss.md` — unified exploration workflow

3. Update `execution.md` — lighter, focused on doing (discuss handles thinking)

4. Remove or deprecate moodboard.md and whiteboard.md

Cherry on Top: The Journalism Prompt

User's exact prompt for logging discoveries (for agent reference):

> "Please include specific code snippet, reasoning, extended context in a journalism narrative style so that whoever with 0 context can onboard and pick up this key record of decision without ambiguity or friction. Add a cherry on top to include exact / synthesized example to explain the concepts / findings mentioned so that other weaker reasoning agents can grasp topic with little friction and ambiguity."

This is the standard for LOG entries in GSD-Lite.

### [LOG-007] - [DECISION] - The standard length and outline for optimal gsd-lite artifacts

#### Current GSD-Lite Artifact Sizes

Artifact | Lines | Est. Tokens | Purpose
--- | --- | --- | ---
OG GSD questioning.md | 142 | ~1,247 | Philosophy reference
OG GSD discuss-phase.md | 434 | ~3,323 | Full workflow
GSD-Lite PROTOCOL.md | 209 | ~2,252 | Router + golden rules
GSD-Lite moodboard.md | 406 | ~3,249 | Vision extraction
GSD-Lite whiteboard.md | ~280 | ~2,187 | Plan presentation
GSD-Lite execution.md | 414 | ~2,778 | Task execution

#### The Context Budget Reality

When an agent loads a workflow, here's what happens:

    ┌─────────────────────────────────────────────────────────────┐
    │  AGENT CONTEXT WINDOW (let's say 80k target budget)         │
    ├─────────────────────────────────────────────────────────────┤
    │                                                              │
    │  System prompt / PROTOCOL.md     ~2,200 tokens (always)     │
    │  WORK.md Current Understanding   ~500-1,000 tokens          │
    │  Workflow file                   ~2,500-3,500 tokens        │
    │  Reference files (if loaded)     ~1,000-2,000 tokens        │
    │  ─────────────────────────────────────────────────────────  │
    │  BASELINE OVERHEAD               ~6,200-8,700 tokens        │
    │                                                              │
    │  Remaining for:                                              │
    │  - Conversation turns                                        │
    │  - Code reading                                              │
    │  - Tool outputs                                              │
    │                                                              │
    └─────────────────────────────────────────────────────────────┘

The Pit Stop philosophy says: Minimize baseline overhead so agents have more runway before needing a fork.

#### 🎯 Recommended Artifact Specifications

##### 1. `questioning.md` (NEW — Reference File)

Purpose: Philosophy + techniques for Socratic pair programming. Referenced by workflows, not loaded every session.

Metric | Target | Rationale
--- | --- | ---
Lines | 100-150 | Matches OG GSD (142 lines)
Tokens | ~1,000-1,500 | Light enough to inline when needed
Sections | 5-6 max | Scannable, not exhaustive

Proposed Outline:

    # Questioning Guide (~120 lines)

    ## Philosophy (~15 lines)
    - "Thinking partner, not interviewer"
    - "Always Ask WHY Before HOW"
    - Learning accelerator, not requirements gathering

    ## The Challenge Tone Protocol (~25 lines)
    - Decision tree (A/B/C/D)
    - When to use each tone
    - Mermaid diagram

    ## Question Types (~20 lines)
    - Motivation (why this exists)
    - Concreteness (what it actually is)
    - Clarification (what they mean)
    - Success (how you'll know)

    ## Techniques (~25 lines)
    - Follow energy
    - Challenge vagueness
    - Make abstract concrete
    - Know when to stop

    ## Anti-Patterns (~15 lines)
    - Checklist walking
    - Eager executor
    - Shallow acceptance

    ## The Teaching Detour (~20 lines)
    - The 10-star experience pattern
    - When to offer: "Want me to explain this concept?"
    - How to: explore, connect, distill, example

Why this size: Small enough to inline into workflow preambles. Large enough to be complete. OG GSD proved 142 lines is sufficient.

##### 2. `discuss.md` (NEW — Unified Workflow, replaces moodboard + whiteboard)

Purpose: Adaptive exploration — handles both vision extraction AND plan presentation based on context.

Metric | Target | Rationale
--- | --- | ---
Lines | 250-300 | Smaller than moodboard (406) + whiteboard (280) combined
Tokens | ~2,000-2,500 | Same budget as current moodboard
Sections | 7-8 | Entry → Adapt → Explore → Confirm → Exit

Proposed Outline:

    # Discuss Workflow (~280 lines)

    ## Purpose (~10 lines)
    - Unified exploration: vision OR plan OR teaching
    - Adapts to what user brings

    ## Entry Conditions (~10 lines)
    - Default when no active execution task
    - User signals: "let's discuss", "I have a question", "help me understand"

    ## Context Reading (~20 lines)
    - Read WORK.md Current Understanding
    - Detect: fresh start vs mid-project vs blocked

    ## Adaptive Routing (~30 lines)
    - If fresh/fuzzy → Vision exploration mode
    - If mid-project + question → Teaching/clarification mode
    - If blocked → Unblocking mode
    - If ready to plan → Plan presentation mode

    ## Exploration Loop (~60 lines)
    - Reference questioning.md philosophy
    - 4 questions, then check pattern
    - Challenge tone decision tree
    - "Want me to capture this?" gates

    ## Teaching Detour Protocol (~40 lines)
    - Detect unfamiliar concepts
    - Offer: "Want me to explain [X]?"
    - Pattern: explore → connect → distill → example
    - Return to main thread

    ## Plan Presentation (~40 lines)
    - When understanding is sufficient
    - Present plan for approval
    - User confirms or requests changes

    ## Exit Conditions (~20 lines)
    - Transition to execution
    - Checkpoint if pausing
    - Capture decisions to WORK.md

    ## Anti-Patterns (~20 lines)
    - Eager executor (skip discuss, start coding)
    - Interrogation mode
    - Auto-writing without permission

    ## Sticky Note (~30 lines)
    - Required end-of-turn format

Why this size: Combines moodboard (406) + whiteboard (280) = 686 lines, but we're targeting ~280 because:

1. Redundancy removed (both had similar patterns)

2. Philosophy moved to `questioning.md` reference

3. Adaptive routing replaces rigid phases

##### 3. `execution.md` (UPDATE — Lighter)

Current: 414 lines, ~2,778 tokens

Proposed: 300-350 lines, ~2,200 tokens (trim ~15-20%)

What to trim:

- Move questioning/blocking logic to reference `questioning.md`

- Simplify — execution should be about DOING, not exploring

- Add: "When stuck, transition to discuss.md"

#### 📐 The Sizing Philosophy

Artifact Type | Target Lines | Target Tokens | Rationale
--- | --- | --- | ---
Reference files | 100-150 | ~1,000-1,500 | Inlineable, philosophy-focused
Workflow files | 250-350 | ~2,000-2,800 | Complete but not bloated
Router (PROTOCOL.md) | 200-250 | ~2,000-2,500 | Always loaded, must be lean

The Test: If a workflow exceeds 400 lines, ask: "What can be extracted to a reference file?"

### [LOG-008] - [EXEC] - Implemented Questioning Enhancement with "Mode-Based" Workflow - Task: QUESTION-001

Timestamp: 2026-02-04 11:30

Details:

Executed the plan from LOG-006 and LOG-007 to enhance GSD-Lite's questioning capability. This involved a significant architectural shift from "sequential phases" (Moodboard → Whiteboard → Execution) to "adaptive modes" (Discuss ↔ Execute) powered by a central questioning philosophy.

The shift:

- From: Rigid phases (`moodboard.md` for vision, `whiteboard.md` for planning)

- To: Unified `discuss.md` that adapts to context (Vision, Teaching, Unblocking, Planning)

Files Created/Updated:

1. `src/gsd_lite/template/references/questioning.md` (NEW - 143 lines)

- The "DNA" of Socratic pair programming.

- Contains: Golden Rule ("Why before How"), Challenge Tone Protocol, Teaching Detour.

2. `src/gsd_lite/template/workflows/discuss.md` (NEW - 289 lines)

- Replaces both `moodboard.md` and `whiteboard.md`.

- Adaptive entry: Reads `WORK.md` to decide if it's exploring vision, teaching a concept, or presenting a plan.

3. `src/gsd_lite/template/workflows/execution.md` (TRIMMED - 222 lines, down from 414)

- Focused purely on DOING.

- Trigger: "Let's pause and discuss" → forks to `discuss.md`.

Cherry on Top: The "Room vs Tool" Analogy

To explain why we moved to "Mode-Based" workflows:

Imagine a workshop:

- Phase-Based (Old): You have a "Thinking Room" and a "Building Room". To think, you must leave the building room, walk down the hall, and enter the thinking room. It's friction.

- Mode-Based (New): You are at the workbench (Execution). You put down the hammer and pick up the whiteboard marker (Discuss). You're in the same space, just switching tools.

In GSD-Lite:

- Old: "I'm stuck. Let me load `moodboard.md`..." (Context switch overhead)

- New: "I'm stuck. Let's discuss." (Seamless mode switch, same context)

Code Snippet: The Adaptive Routing Logic (from discuss.md)

This logic allows one workflow to handle multiple user needs:

    ## Context Reading

    Read WORK.md Current Understanding to detect state:

    | State Detected | Mode to Enter |
    |----------------|---------------|
    | No active phase, fuzzy vision | Vision Exploration |
    | Active phase, user has question | Teaching/Clarification |
    | Active task, user is blocked | Unblocking |
    | Understanding complete, ready to plan | Plan Presentation |

Status: Implementation complete. Ready to update router (PROTOCOL.md).

### [LOG-009] - [DECISION] - Protocol v2.1: Questioning DNA and Mode-Based Architecture - Task: PROTO-001

Timestamp: 2026-02-04 12:00

Context:

The GSD-Lite protocol was upgraded from a sequential phase-based model to an adaptive mode-based architecture. This shift reflects the user's "Driver/Navigator" persona, where learning and mental model validation are as important as task execution.

The Finding: Questioning is the DNA, Not a Phase

The previous architecture separated vision extraction (`moodboard.md`) and planning (`whiteboard.md`) into rigid sequences. However, real-world sessions showed that questioning, teaching, and unblocking are continuous needs that emerge organically during both planning and execution.

Decision: Inline Philosophy for Zero Friction

We decided to append the full `questioning.md` content (Philosophy, Challenge Tones, Teaching Detours) directly to `PROTOCOL.md`.

- Reasoning: 1.2k tokens is a minor cost to ensure the agent ALWAYS operates as a Socratic "Thinking Partner."

- Benefit: It eliminates the friction of an agent needing to remember to read a separate reference file, ensuring the "Golden Rule" (Why Before How) is applied to every turn.

Architectural Change: From Rooms to Tools

- Old (Sequential): Moodboard → Whiteboard → Execution (The "Moving Rooms" model).

- New (Adaptive): `discuss.md` ↔ `execution.md` (The "Workbench Tools" model).

- `discuss.md` now acts as a Swiss Army knife for exploration, teaching, and planning.

Cherry on Top: The "Socratic Workbench" Analogy

Imagine a craftsman's workshop:

- Sequential Model: To think about a design, you must leave the workbench and walk to a separate office. By the time you get back to the wood, the inspiration might be cold.

- Socratic Workbench (GSD-Lite v2.1): You are at the bench with the wood (Execution). You feel a knot in the grain (Unfamiliar Concept). Instead of walking away, you simply put down the chisel and pick up the measuring tape and pencil (Discuss). You analyze, teach yourself about the grain, and then pick the chisel back up with new understanding.

In this model, the Questioning DNA in the PROTOCOL is like the bright overhead lighting in the workshop — it doesn't matter which tool you're holding; you can always see the "Why" behind the work.

Synthesized Example: The "Mode Switch" in Action

Scenario: User is executing a task to refactor a Python class.

1. Execution Mode: Agent proposes a refactor using a `Protocol`.

2. User Question: "Why use `Protocol` here instead of `abc.AbstractBaseClass`?"

3. Mode Switch (Automatic): The agent identifies this as a Teaching Detour (from the DNA in PROTOCOL). It switches to Discuss Mode logic.

4. Teaching Detour:

- Explore: Shows `typing.Protocol` usage in the codebase.

- Connect: Relates it to "Duck Typing" which the user knows.

- Distill: "ABCs are 'is-a' relationships; Protocols are 'behaves-like' relationships."

- Example: `class Flyer(Protocol): def fly(self): ...`

5. Resume Execution: "Ready to proceed with the `Protocol` approach, or want to stick to ABCs?"

Status: PROTOCOL.md v2.1 committed. Moodboard and Whiteboard deprecated and removed. Architecture is now mode-based.

### [LOG-010] - [DECISION] - Universal Onboarding & The End of Sticky Notes - Task: PROTO-002

Timestamp: 2026-02-04 13:00

Context:

A critical onboarding gap was discovered in the "Fork & Resume" workflow. If a user forks a session and invokes `discuss.md` directly (e.g., "Let's discuss this loop"), the fresh agent would read `PROTOCOL.md` and `WORK.md` but miss `PROJECT.md` (Vision) and `ARCHITECTURE.md` (Tech Stack).

This created "Forwarder Agents" — agents that mechanically execute the workflow but lack the deep context to ask intelligent, grey-area probing questions (the core value of GSD).

The Solution: Universal Onboarding

We moved the onboarding responsibility from individual workflows (like `progress.md`) to the `PROTOCOL.md` router itself. Now, every fresh session begins with a mandatory boot sequence:

1. `PROTOCOL.md`

2. `PROJECT.md` (Vision - The Why)

3. `ARCHITECTURE.md` (Tech - The How)

4. `WORK.md` (State - The Where)

The Trade-off:

We centralized onboarding (DRY) rather than duplicating it in every workflow file. This ensures consistent "Smart Agent" behavior regardless of entry point.

The Cleanup: Decomissioning Sticky Notes

With artifacts now mature and the protocol robust, the "Sticky Note Protocol" (appending a status block to every turn) was deemed redundant noise.

- Why: The artifacts (`WORK.md`, `INBOX.md`) are the source of truth.

- Change: Removed `gsd-status` requirements from all 9 workflow templates.

- Result: Cleaner chat interface, fewer tokens used per turn.

Cherry on Top: The "Pilot's Checklist" Analogy

To explain why Universal Onboarding matters:

Imagine a pilot (Agent) taking over a plane mid-flight (The Project) from another pilot (Previous Session).

* Old Way (Workflow-Dependent):

* Pilot jumps in.

* If they check `progress.md`: They read the flight plan and weather.

* If they just grab the yoke (`discuss.md`): They start flying without knowing the destination or the aircraft type. They fly purely on instruments. It's safe, but they can't make strategic decisions.

* New Way (Universal Onboarding):

* Mandatory Pre-Flight: Before touching the controls, EVERY pilot reads:

1. PROJECT.md (Destination): "We are going to Hawaii."

2. ARCHITECTURE.md (Aircraft Manual): "This is a Boeing 747, not a Cessna."

3. WORK.md (Instruments): "We are currently at 30,000ft, autopilot on."

* Now, when the user says "Let's dive," the pilot knows "Wait, a 747 can't dive like a Cessna," instead of just executing the dive.

Code Snippet: The New Protocol Boot Sequence

`src/gsd_lite/template/PROTOCOL.md`:

    ## Session Start (Universal Onboarding)

    **Every fresh session follows this boot sequence — regardless of which workflow will run.**

    1. **Read PROTOCOL.md**
    2. **Read PROJECT.md** (if exists) — Understand the project vision and "why"
    3. **Read ARCHITECTURE.md** (if exists) — Understand the codebase structure
    4. **Read WORK.md Current Understanding** — Understand current state
    5. **Load appropriate workflow**

Status: `src/gsd_lite/template/` updated. Sticky notes removed. Protocol v2.2 active.

### [LOG-011] - [DECISION] - The Frictionless Install Paradox: Global vs Local Distribution - Task: DIST-001

Timestamp: 2026-02-04 14:00

Context:

The user requested a "frictionless" distribution model for `gsd-lite`, similar to `npx get-shit-done-cc`. The goal is to allow users to run `@gsd-lite` in any directory without manually scaffolding a `gsd-lite/` folder first. However, the `AGENTS.md` template (the brain) was hardcoded to look for workflows in a local project directory (`gsd-lite/template/workflows/`).

The Paradox:

- Local Mode (Eject): Project is self-contained. Agent reads `./gsd-lite/template/workflows/`. Good for customization, bad for friction (requires init).

- Global Mode (Binary): Zero setup. Agent reads `~/.config/opencode/command/gsd-lite/`. Good for friction, but breaks the static template.

The Decision: Dynamic Patching at Install Time

We chose the Global Binary model for the default experience. To solve the pathing issue, the installer CLI will dynamically patch the agent's instructions during installation.

- Source: `src/gsd_lite/template/AGENTS.md` (contains relative path placeholder).

- Destination: `~/.config/opencode/agents/gsd-lite.md` (contains absolute path).

Rationale:

This preserves the "Project Specific Protocol" philosophy (Option A) if a user wants to eject (they can manually copy files), but enables the "Frictionless" experience (Option B) by default. The agent is smart enough to follow the path it is given.

Cherry on Top: The "Suitcase vs Hotel" Analogy

To explain the difference:

- Local Mode (Eject): Like moving into an apartment. You buy furniture (workflows) and put it in your living room (`./gsd-lite`). It's yours, you can paint it, but moving in takes effort.

- Global Mode (Frictionless): Like staying at a hotel. The furniture (workflows) is provided by the hotel (`~/.config`). You just show up with your suitcase (Code) and start working.

We want `gsd-lite` to feel like a 5-star hotel: Instant check-in, everything provided.

Code Snippet: The Dynamic Patch

The installer will perform this transformation:

Before (Template):

    5. **Load appropriate workflow** — Based on current_mode in WORK.md
       (Reads from gsd-lite/template/workflows/)

After (Installed Agent):

    5. **Load appropriate workflow** — Based on current_mode in WORK.md
       (Reads from /Users/username/.config/opencode/command/gsd-lite/)

Next Action: Implement `gsd-lite install --opencode` command in `src/gsd_lite/__main__.py` that copies workflows and applies this patch.

### [LOG-012] - [DISCOVERY] - Reverse-Engineering get-shit-done-cc: The Anatomy of a Frictionless Installer - Task: DIST-002

Timestamp: 2026-02-04 15:30

The Story So Far

The user's current workflow to update gsd-lite after each iteration is painfully manual:

    # Step 1: Navigate to opencode config directory
    cd ~/.config/opencode

    # Step 2: Run CLI to scaffold template files (creates gsd-lite/ subdirectory)
    uvx --no-cache gsd-lite --update

    # Step 3: Manually copy the agent definition
    cp gsd-lite/template/AGENTS.md ./agents/gsd-lite.md

    # Step 4: Manually copy all workflow files
    cp -r gsd-lite/template/workflows/* ./command/gsd-lite/

This is the "IKEA furniture" experience: you get the parts, but assembly is on you. We want the "hotel concierge" experience: everything set up before you arrive.

The Reference Implementation: get-shit-done-cc

We cloned the upstream GSD installer (`get-shit-done-cc`) to `tmp/get-shit-done/` and reverse-engineered its installation logic. The key file is `bin/install.js` — a ~1500-line Node.js script that handles multi-runtime installation.

Critical Discovery #1: Directory Structure Varies by Runtime

Each runtime (Claude Code, OpenCode, Gemini) uses slightly different conventions:

Runtime | Config Directory | Agent Location | Command Location
--- | --- | --- | ---
OpenCode | `~/.config/opencode/` | `agents/gsd-lite.md` | `command/gsd-lite/` (singular)
Claude Code | `~/.claude/` | `agents/gsd-lite.md` | `commands/gsd-lite/` (plural!)
Gemini | `~/.gemini/` | `agents/gsd-lite.md` | `commands/gsd-lite/` (plural)

Why this matters: A naive installer that assumes `command/` everywhere will fail silently on Claude Code — the workflows land in the wrong directory and the agent can't find them.

Evidence from get-shit-done-cc source (`bin/install.js`, line ~1385):

    console.log(`  ${yellow}Which runtime(s) would you like to install for?${reset}\n\n
      ${cyan}1${reset}) Claude Code ${dim}(~/.claude)${reset}
      ${cyan}2${reset}) OpenCode    ${dim}(~/.config/opencode)${reset} - open source, free models
      ${cyan}3${reset}) Gemini      ${dim}(~/.gemini)${reset}
      ${cyan}4${reset}) All

Critical Discovery #2: OpenCode's Current Installation Structure

We inspected the user's live OpenCode config at `~/.config/opencode/` using the opencode-fs MCP tool:

    ~/.config/opencode/
    ├── agents/
    │   └── gsd-lite.md          # ← The agent brain (AGENTS.md renamed)
    ├── command/
    │   └── gsd-lite/            # ← Workflow files live here
    │       ├── checkpoint.md
    │       ├── discuss.md
    │       ├── execution.md
    │       ├── housekeeping.md
    │       ├── map-codebase.md
    │       ├── new-project.md
    │       └── progress.md
    ├── gsd-lite/                 # ← Scaffolded by current CLI (redundant for global install)
    │   └── template/
    │       └── ...
    ├── hooks/
    ├── opencode.json
    └── settings.json

Key insight: The `gsd-lite/` subdirectory inside `~/.config/opencode/` is an artifact of the current `--update` command, which scaffolds a full project structure. For global installation, we don't need this — we only need to populate `agents/` and `command/gsd-lite/`.

Critical Discovery #3: AGENTS.md is Pre-Built (No Dynamic Assembly)

The user clarified that `AGENTS.md` in the source tree (`src/gsd_lite/template/AGENTS.md`) is already the final artifact — it's `PROTOCOL.md` with a 22-line YAML frontmatter prepended. There's no build step needed; we simply copy it.

The frontmatter (first 16 lines of `src/gsd_lite/template/AGENTS.md`):

    ---
    description: Lightweight agent with minimal tool access
    tools:
      read: false
      edit: false
      bash: false
      grep: false
      glob: false
      list: false

    permission:
      task:
        "*": "deny"
    ---

This frontmatter is OpenCode-specific but works across all platforms (Claude and Gemini also support frontmatter).

#### The Scoped Plan: MVP Installer (OpenCode Global Only)

Scope for v1:

- ✅ OpenCode runtime only

- ✅ Global install to `~/.config/opencode/` only

- ❌ Claude Code (future)

- ❌ Gemini (future)

- ❌ Local install (future)

- ❌ Version checking / updates (future)

What the installer does:

    uvx gsd-lite install

    # Output:
    #   Installing gsd-lite to ~/.config/opencode/ ...
    #   ✓ Copied agents/gsd-lite.md
    #   ✓ Copied command/gsd-lite/ (7 workflows)
    #   Done! Run @gsd-lite in any project to start.

File Operations:

Source (in package) | Destination (on disk)
--- | ---
`src/gsd_lite/template/AGENTS.md` | `~/.config/opencode/agents/gsd-lite.md`
`src/gsd_lite/template/workflows/*.md` | `~/.config/opencode/command/gsd-lite/*.md`

Implementation Sketch:

    # src/gsd_lite/__main__.py (new install command)

    import shutil
    from pathlib import Path
    from importlib.resources import files

    def install():
        """Install gsd-lite globally to ~/.config/opencode/"""

        # 1. Resolve paths
        opencode_config = Path.home() / ".config" / "opencode"
        agents_dir = opencode_config / "agents"
        command_dir = opencode_config / "command" / "gsd-lite"

        # 2. Get package template location
        template_dir = files("gsd_lite") / "template"

        # 3. Ensure target directories exist
        agents_dir.mkdir(parents=True, exist_ok=True)
        command_dir.mkdir(parents=True, exist_ok=True)

        # 4. Copy agent definition
        shutil.copy(template_dir / "AGENTS.md", agents_dir / "gsd-lite.md")
        print("✓ Copied agents/gsd-lite.md")

        # 5. Copy all workflow files
        workflows_src = template_dir / "workflows"
        for workflow in workflows_src.iterdir():
            if workflow.suffix == ".md":
                shutil.copy(workflow, command_dir / workflow.name)
        print(f"✓ Copied command/gsd-lite/ ({len(list(command_dir.glob('*.md')))} workflows)")

        print("\nDone! Run @gsd-lite in any project to start.")

Open Question (Deferred): Dynamic Path Patching

LOG-011 mentioned patching `AGENTS.md` to replace relative workflow paths with absolute paths. After discussion, we realized:

1. The current `AGENTS.md` doesn't hardcode a workflow path — it says "Load appropriate workflow" generically.

2. OpenCode resolves `command/gsd-lite/` relative to its config directory automatically.

3. No patching needed for global install — the runtime handles path resolution.

If we later implement local install (`./.opencode/`), we may need patching. Deferred for now.

Cherry on Top: The "Concierge vs IKEA" Mental Model

Experience | What You Do | What Happens
--- | --- | ---
IKEA (Current) | `uvx gsd-lite --update` then manually copy files | You get boxes of parts, assembly required
Concierge (Goal) | `uvx gsd-lite install` | Everything placed in the right rooms automatically

The MVP delivers the concierge experience for OpenCode users. Multi-runtime support is the "international hotel chain expansion" — same service, different locations.

Next Action:

1. Switch to execution mode

2. Implement `install` command in `src/gsd_lite/__main__.py`

3. Test with `uvx --no-cache gsd-lite install`

4. Verify files land in correct locations

### [LOG-013] - [DECISION] - Unified Installation Model: Same Output, Different Location - Task: DIST-002

Timestamp: 2026-02-04 16:45

The Problem We Were Creating

Initial thinking led to a fragmented command structure:

- `install` for global brain only

- `--local` for project artifacts only

- `--update` for refreshing templates

- `--force` for overwriting

This created cognitive overhead: "Which command do I need?" and different outputs for different commands.

The Insight

User asked: "Are we just complicating ourselves? What if we unified everything like get-shit-done-cc does?"

The answer: Yes, we were overcomplicating.

The Unified Model

    uvx gsd-lite install           # → ~/.config/opencode/ + gsd-lite/
    uvx gsd-lite install --local   # → ./.opencode/ + ./gsd-lite/

Both commands create identical structure, just at different roots:

    {target}/
    ├── .opencode/                    # OpenCode config (or ~/.config/opencode/ for global)
    │   ├── agents/
    │   │   └── gsd-lite.md           # Agent definition (AGENTS.md)
    │   └── command/
    │       └── gsd-lite/             # Workflows
    │           ├── discuss.md
    │           ├── execution.md
    │           ├── checkpoint.md
    │           └── ...
    │
    └── gsd-lite/                     # Artifacts directory
        ├── WORK.md                   # ← SKIP if exists (preserve user data)
        ├── INBOX.md                  # ← SKIP if exists
        ├── PROJECT.md                # ← SKIP if exists
        ├── ARCHITECTURE.md           # ← SKIP if exists
        ├── HISTORY.md                # ← SKIP if exists
        └── template/                 # ← ALWAYS overwrite (that's how updates work)
            ├── PROTOCOL.md
            ├── workflows/
            ├── references/
            └── VERSION

The Smart Check Pattern (already exists in current code):

- User artifacts → Skip if exist, scaffold if new

- Template/config files → Always overwrite (enables updates)

Why This is Better

Aspect | Before (Complicated) | After (Unified)
--- | --- | ---
Commands | `install`, `--update`, `--local`, `--force` | `install`, `install --local`
Mental model | Different outputs per command | Same output, different root
Update flow | Separate `--update` flag | Just run `install` again

The "Global Brain + Local Memory" Mental Model

    GLOBAL INSTALL (~/.config/opencode/)
    ├── Brain: agents/ + command/gsd-lite/    ← Available to all projects
    └── Reference: gsd-lite/template/          ← For manual reference

    LOCAL INSTALL (./)
    ├── Brain: .opencode/agents/ + command/   ← Project-specific override
    └── Memory: gsd-lite/                      ← Active session artifacts

Typical User Journey:

1. `uvx gsd-lite install` — One-time global setup

2. Enter project, `uvx gsd-lite install --local` — Scaffold project artifacts

3. Start OpenCode, `@gsd-lite` — Global brain reads/writes local memory

4. Later: `uvx gsd-lite install` again — Updates global brain to latest version

### [LOG-014] - [MILESTONE] - Implemented Unified Installer Logic - Task: DIST-002

Timestamp: 2026-02-04 17:15

Observation

The previous CLI was a simple scaffolder that dumped everything into `./gsd-lite`. To support the "Concierge" experience, we needed logic that could target both the Global OpenCode config (`~/.config/opencode`) and a Local Project (`./`).

Resolution

Refactored `src/gsd_lite/__main__.py` to use `typer` subcommands.

- Global Install (`gsd-lite install`):

- Targets `~/.config/opencode/`

- Installs Agent → `agents/gsd-lite.md`

- Installs Workflows → `command/gsd-lite/`

- Installs Reference Templates → `gsd-lite/template/`

- Local Install (`gsd-lite install --local`):

- Targets `./`

- Creates `.opencode/agents/` and `.opencode/command/` (Brain)

- Scaffolds `gsd-lite/WORK.md` etc. (Memory)

Smart Overwrite Logic

- Templates/Workflows: ALWAYS overwritten (ensures updates apply)

- User Artifacts (`WORK.md`, `PROJECT.md`): SKIPPED if exist (preserves user data)

- Force Flag (`--force`): Overwrites EVERYTHING (nuclear option)

Key Code Snippet

    if local:
        base_root = Path.cwd()
        config_root = base_root / ".opencode"
    else:
        base_root = Path.home() / ".config" / "opencode"
        config_root = base_root

    # Unified Structure
    agents_dir = config_root / "agents"
    command_dir = config_root / "command" / "gsd-lite"
    artifacts_dir = base_root / "gsd-lite"

Next Steps

- Verify installation on live system

- Update documentation to reflect new `install` command usage

### [LOG-015] - [TOOLING] - Context Map: Solving the "Token Blindness" Problem in Large Artifacts - Task: TOOLING-001

Executive Summary:

We developed `analyze_context.py`, a utility that solves the "token blindness" problem when agents interact with massive Markdown artifacts (like `WORK.md`). Instead of reading the full 65k token file (which overflows context) or blindly grepping headers (which loses hierarchy), this tool generates a Context Map: a lightweight (~1k token) mirror of the document that preserves IDE navigability, visualizes section weights, and provides "HEAD/TAIL" previews for surgical reading.

#### 1. The Problem: "Token Blindness" in Large Artifacts

When an agent needs to understand the project state from a massive log file (e.g., `WORK.md` at 65,000+ tokens), it faces a trilemma:

1. Read Full File: Costs $0.50+ per turn, overflows context window, drowns reasoning in noise.

2. Blind Grep: `grep "^#"` reveals headers but hides the content weight. Is "Section 3" a 2-line note or a 5,000-token specification? The agent doesn't know until it commits to reading.

3. Table Summary: A standard table of contents breaks the visual hierarchy (indentation) and IDE collapsing, making it hard to "scan" the tree structure.

We needed a way to see the shape and weight of the forest before cutting down trees.

#### 2. The Research: Leveraging Source Maps

We investigated how the industry handles this. A search for "LLM context window management document chunking" revealed two key patterns:

* LangChain's `MarkdownHeaderTextSplitter`: Parses Markdown by headers (`#`, `##`) to preserve structural integrity when chunking for RAG.

"Offloading Large Tool Results": A pattern where agents see a reference and a preview* of large data, rather than the raw data itself.

Source:

> "MarkdownHeaderSplitter specifically designed for Markdown documents... divides content based on headers... preserving structural integrity." — LangChain Documentation [1]

We realized we could use `markdown-it-py` (a CommonMark-compliant parser) to access the Source Map (`token.map`) of each header, giving us precise line numbers and the ability to extract just the start (HEAD) and end (TAIL) of each section.

#### 3. The Solution: `analyze_context.py`

We built a script that transforms a massive Markdown file into a "Context Map".

Key Features:

1. Mirrored Hierarchy: Uses real Markdown headers (`#`, `##`) in the output, so the map is collapsible in your IDE just like the original file.

2. Weight visualization: Calculates token counts (using `tiktoken`) for every section, allowing the agent to budget its context usage.

3. Surgical Previews: Extracts the first 3 lines (HEAD) and last 2 lines (TAIL) of every section. The agent can read the preview and decide if it needs the full content.

Code Snippet: The Preview Logic

    def extract_preview(lines, head_count=3, tail_count=2):
        """
        Extract HEAD and TAIL preview from a list of lines.
        Returns (head_text, tail_text) as strings.
        """
        # Filter out empty lines for meaningful preview
        content_lines = [l.rstrip() for l in lines if l.strip()]

        if not content_lines:
            return ("(empty section)", "")

        # HEAD: first N content lines (skip the header line itself)
        head_lines = content_lines[1:head_count+1] if len(content_lines) > 1 else content_lines[:head_count]
        head_text = "\n".join(head_lines) if head_lines else "(no content after header)"

        # TAIL: last N content lines (avoid overlap with head)
        if len(content_lines) > head_count + tail_count + 1:
            tail_lines = content_lines[-tail_count:]
            tail_text = "\n".join(tail_lines)
        else:
            tail_text = ""  # Section too short, HEAD covers it

        return (head_text, tail_text)

#### 4. The Data Flow Diagram

    graph TD
        A[Input: WORK.md] -->|Read File| B(Raw Text)
        B -->|markdown-it-py| C{Token Stream}
        C -->|Identify Headers| D[Header Metadata]
        D -->|Get Line Ranges| E[Section Slicing]

        subgraph Analysis
        E -->|tiktoken| F[Token Count]
        E -->|Heuristics| G[Extract HEAD/TAIL]
        end

        F --> H[Context Map Generator]
        G --> H
        H -->|Format with # Headers| I[Output: context_map.md]

        style A fill:#f9f,stroke:#333
        style I fill:#9f9,stroke:#333

#### 5. Example Output (Synthesized)

Input: A 65,000 token `WORK.md`.

Output: A ~1,500 token `context_map.md` that looks like this:

    # Context Map: eval/gsd-lite/WORK.md
    **Total:** 254,551 chars | **~64,864 tokens**

    ## 🎯 Top Heavy Sections
    | Rank | Section | Tokens |
    |------|---------|--------|
    | 1 | `## 2.2 Layer 2: Semantic Layer` | 2,663 |

    ---

    # Document Structure

    ## 1. Current Understanding (Read First)
    > 📊 **900 tokens** | Lines 26-85

    **HEAD:**

current_mode: execution

active_task: TASK-042 - Implement cascading filters...

    **TAIL:**

...next_action: Complete filter persistence in localStorage.

This map allows an agent to say: "I see Section 1 is 900 tokens and relevant. I will read lines 26-85. I see Section 2.2 is 2,600 tokens and irrelevant. I will skip it."

#### 6. Citations & Sources

1. LangChain Documentation, "MarkdownHeaderTextSplitter". https://python.langchain.com/docs/modules/data_connection/document_transformers/markdown_header_metadata

2. markdown-it-py, "Token Stream and Source Maps". https://markdown-it-py.readthedocs.io/en/latest/tokens.html

3. Tiktoken, "OpenAI Tokenizer". https://github.com/openai/tiktoken

### [LOG-016] - [DECISION] - Stateless-First Architecture: Every Turn Generates a Handoff Packet - Task: PROTOCOL-STATELESS

Timestamp: 2026-02-06

Decision ID: DECISION-016

Status: APPROVED

#### Executive Summary

GSD-Lite adopts stateless-first architecture as a core philosophy. Every agent turn ends with a structured handoff packet that enables any future agent to continue with zero chat history. This embraces the user's micro-forking workflow as a first-class pattern, not a workaround.

The One-Liner: Treat every agent turn as its end-of-life. Generate a handoff so any future agent can pick up exactly where you left off.

#### Part 1: The Problem — Context Rot and Manual Curation

##### 1.1 The Production Evidence

This decision emerged from real production usage documented in `eval/gsd-lite/WORK.md`:

- Size: 254,551 characters | ~64,864 tokens | 5,552 lines

- Logs: 56 LOG entries (LOG-001 through LOG-056)

- Analysis: See `eval/gsd-lite/WORK_context_analysis.md` for token breakdown by section

Citation: File metadata from `mcp_tools_fs_get_file_info("eval/gsd-lite/WORK.md")`:

    Total Lines: 5,552
    Total Characters: 254,551
    Estimated Tokens: 63,637

##### 1.2 The Micro-Forking Workflow Discovered

The user discovered a powerful context management pattern through production use:

    flowchart LR
        subgraph SESSION1["SESSION 1 (tokens: 0 → 60k)"]
            A[Start] --> B[Discuss 2-3 turns]
            B --> C[Agent writes LOG-XXX]
            C --> D[Context rising...]
            D --> E[Hit 60-80k threshold]
        end

        E --> F["🔀 FORK back to turn 1-2"]

        subgraph SESSION2["SESSION 2 (tokens: 0 → fresh)"]
            F --> G["Paste: 'Read LOG-XXX'"]
            G --> H[Agent onboards from log]
            H --> I[Continue with optimal context]
            I --> J[Context rising...]
        end

        J --> K["🔀 FORK again"]
        K --> G

Why this works:

- LLMs perform optimally in the 20k-80k token range (user's empirical observation with Claude Opus 4.5 and Gemini Pro 3)

- Logs written by a strong reasoning model are better context than raw conversation — synthesized, polished, agent-optimized

- The micro-fork is a context upgrade, not a workaround

##### 1.3 The Friction Point

The problem at scale: At LOG-001, "go read LOG-001" is sufficient. At LOG-100, the fresh agent needs a curated path through the decision graph — not just "read the last one."

Prior attempt (LOG-045 in eval/gsd-lite/WORK.md, lines 4325-4398):

A META log entry with "Agent Onboarding Paths" organized by task type:

- Path A: Building a new page (~4k tokens)

- Path B: Understanding data architecture (~6k tokens)

- Path C: Resuming mid-task (~2k tokens)

- Path D: Adding filters (~3k tokens)

- Path E: Full context deep dive (~15k tokens)

Citation: `eval/gsd-lite/WORK.md` lines 4325-4398 (LOG-045)

Why LOG-045 is insufficient:

1. Static snapshot — Valid when written, stale as logs accumulate

2. Separate artifact — Another thing to remember and maintain

3. Manual trigger — User must remember to ask agent to read it

#### Part 2: The Solution — Stateless-First Handoff Packets

##### 2.1 Core Philosophy

Principle: Every agent turn is potentially its last. The agent must generate a handoff packet that enables any future agent to continue with zero chat history.

No exceptions: Even Turn 1. Even mid-discussion. The user owns context management via micro-forking.

Analogy — The Stateless Server:

Think of each agent turn like an HTTP request to a stateless server:

- Server doesn't remember previous requests

- Each request contains everything needed to process it

- Response contains everything needed for the next request

The handoff packet is like a JWT token — self-contained context that travels with the user across session boundaries.

##### 2.2 Two-Layer Structure

The handoff packet contains two layers of context:

Layer | Mental Model | Source | Who Maintains
--- | --- | --- | ---
Layer 1 — Local Context | This task's dependency chain | Agent traces backwards dynamically | Agent (computed each turn)
Layer 2 — Global Context | Project foundation decisions | Key Events Index in WORK.md | Human curates, agent reads

Why two layers:

- Layer 1 answers: "How do I continue this specific task?"

- Layer 2 answers: "How do I pivot to something completely different?"

    flowchart TB
        subgraph HANDOFF["📦 Stateless Handoff Packet"]
            L1["Layer 1: Local Context<br/>───────────────<br/>Last action: LOG-056<br/>Chain: LOG-056 ← LOG-055 ← LOG-052<br/>Next: Implement on /explore"]
            L2["Layer 2: Global Context<br/>───────────────<br/>Architecture: LOG-031, LOG-024<br/>Patterns: LOG-044, LOG-055<br/>Data Flow: LOG-038"]
            PATHS["Fork Paths<br/>───────────────<br/>• Continue → L1 only<br/>• Pivot → L2 + new topic"]
        end

        L1 --> PATHS
        L2 --> PATHS

##### 2.3 Layer 1 — Dependency Chain Tracing

The agent traces backwards from the current task to surface the decision lineage.

Example from eval/gsd-lite/WORK.md:

    Current task: Implement cascading filters on /explore (TASK-006)
                        ↓ depends on
    LOG-056: Implemented cascading filters (last execution)
                        ↓ depends on
    LOG-055: DECISION-020 — Cascading via run_inline_query (the pattern)
                        ↓ depends on
    LOG-052: DECISION-018 — Dual filter sections with mutex (the architecture)
                        ↓ depends on
    LOG-048: DECISION-016 — Dashboard-defined filters (the foundation)

In handoff format:

    → Dependency chain: LOG-056 ← LOG-055 ← LOG-052 ← LOG-048

##### 2.4 Layer 2 — Key Events Index as Source of Truth

The existing `## 2. Key Events Index` section in WORK.md becomes the canonical source for global context.

Reframe from: "Query Accelerator" (nice-to-have optimization)

Reframe to: "Project Foundation" (required for handoff packets)

New structure:

    ## 2. Key Events Index (Project Foundation)

    <!--
    CANONICAL source of truth for Layer 2 of stateless handoff packets.
    Agents MUST pull global context from here.
    Human-curated: agent proposes additions, human approves.
    -->

    ### Architecture Decisions
    - LOG-031: Two Galaxies — Single project, dual Looker models
    - LOG-024: Mirror Clone — Full data ownership via dataset cloning

    ### Pattern Decisions
    - LOG-044: Dashboard Config — Looker dashboards as first-class artifacts
    - LOG-055: Cascading Filters — Dynamic options via run_inline_query

    ### Data Flow Decisions
    - LOG-038: Source Divergence — App queries looker-partners, not bigquery-public-data

#### Part 3: The Canonical Handoff Format

##### 3.1 Full Format (Option C — Chosen)

After evaluating three format options (Minimal 3-4 lines, Structured 6-7 lines, Full 10-12 lines), the user chose Full Format because:

- Sessions are only 5-8 turns before forking anyway

- 10-12 lines × 8 turns = ~80-100 lines total — manageable

- Full context every turn means no guessing what's missing

The canonical format:

    ---
    📦 STATELESS HANDOFF

    **Layer 1 — Local Context:**
    → Last action: [LOG-XXX (brief description)]
    → Dependency chain: [LOG-XXX ← LOG-YYY ← LOG-ZZZ]
    → Next action: [specific next step]

    **Layer 2 — Global Context:**
    → Architecture: [from Key Events Index]
    → Patterns: [from Key Events Index]
    → Data Flow: [from Key Events Index]

    **Fork paths:**
    - Continue execution → [specific logs]
    - Discuss [topic] → [specific logs]
    - Pivot to new topic → [L2 refs] + state your question

##### 3.2 Turn-Type Variations

The structure stays rigid. The content adapts based on turn type:

Mid-Discussion (no decision yet):

    ---
    📦 STATELESS HANDOFF

    **Layer 1 — Local Context:**
    → Status: Discussing [topic] — no decision yet
    → Key refs from discussion: [LOG-XXX, LOG-YYY]
    → Resume: Restate your position on [open question]

    **Layer 2 — Global Context:**
    → Architecture: [from Key Events Index]
    → Patterns: [from Key Events Index]

    **Fork paths:**
    - Continue discussion → restate your question
    - Pivot to new topic → L2 + state your question

Post-Decision (DECISION logged):

    ---
    📦 STATELESS HANDOFF

    **Layer 1 — Local Context:**
    → Last action: LOG-XXX (DECISION-NNN: [title])
    → Dependency chain: LOG-XXX ← LOG-YYY ← LOG-ZZZ
    → Next action: [implementation step]

    **Layer 2 — Global Context:**
    → Architecture: [from Key Events Index]
    → Patterns: [from Key Events Index]

    **Fork paths:**
    - Continue execution → L1 only (~3 logs)
    - Discuss implementation → L1 + [relevant pattern log]
    - Pivot to new topic → L2 + state your question

Teaching Detour:

    ---
    📦 STATELESS HANDOFF

    **Layer 1 — Local Context:**
    → Status: Teaching detour on [concept]
    → Task paused at: LOG-XXX ([last exec])
    → Resume: [LOG refs] → [next action]

    **Layer 2 — Global Context:**
    → [Relevant sections from Key Events Index]

    **Fork paths:**
    - Resume execution → LOG-XXX + LOG-YYY
    - Continue learning → [concept-specific logs]
    - Pivot to new topic → L2 + state your question

First Turn (just forked in):

    ---
    📦 STATELESS HANDOFF

    **Layer 1 — Local Context:**
    → Onboarded via: [LOG-XXX (how you got here)]
    → Current action: [what you're doing this turn]
    → Will log as: LOG-YYY (on completion)

    **Layer 2 — Global Context:**
    → [Full Key Events Index — user just forked, may pivot]

    **Fork paths:**
    - Continue after this turn → await LOG-YYY completion
    - Pivot mid-task → L2 + state new direction

##### 3.3 The Rigid Rules

These aspects are locked down for consistency across providers (Claude Opus, Gemini Pro 3):

Rule | Specification
--- | ---
Delimiter | Always `---` followed by `📦 STATELESS HANDOFF`
Layer 1 | Always present. Describes local/task context.
Layer 2 | Always present. Pulled from Key Events Index.
Fork paths | Minimum 2 (continue + pivot). Maximum 4.
Log references | Always `LOG-XXX (brief description)` format.
No prose | Arrows `→` and bullets `-` only. No paragraphs.
Dependency chain | Uses `←` to show lineage (newest ← oldest).

#### Part 4: Key Events Index Maintenance

##### 4.1 When to Update

The Key Events Index should be updated when:

- User explicitly asks: "update the key events index"

- Agent notices a DECISION log with project-wide impact

- At checkpoint time (optional housekeeping step)

##### 4.2 How to Scan

    # Find all DECISION logs
    grep "^\(###\|####\) \[LOG-.*\[DECISION" WORK.md

    # Find superseded entries
    grep -i "supersede" WORK.md

    # Cross-reference against current Key Events Index
    # (Agent reads Section 2 and compares)

##### 4.3 Inclusion/Exclusion Criteria

Include if:

Criterion | Example
--- | ---
Decision affects multiple tasks/phases | "Two Galaxies" architecture spans all LookML work
Decision establishes a reusable pattern | "Dashboard config pattern" used by all pages
Decision changes data flow or ownership | "Mirror Clone" changed where data lives
Decision is frequently referenced | Check for `see LOG-XXX` mentions in other logs

Exclude if:

Criterion | Example
--- | ---
Decision is task-specific implementation detail | "Used flex instead of grid for this component"
Decision was superseded and context captured in successor | LOG-018 fully superseded by LOG-024
Decision is about process, not product | "Decided to pair program on this task"

##### 4.4 Update Proposal Format

Agent proposes, user approves:

    📋 KEY EVENTS INDEX UPDATE PROPOSAL

    **Add:**
    - LOG-055: Cascading filters via run_inline_query → Category: Patterns
      Reason: Establishes reusable pattern for all filter implementations

    **Remove:**
    - LOG-018: Pivot to Public Data (superseded by LOG-024)
      Reason: LOG-024 captures the final decision with full context

    **Recategorize:**
    - LOG-038: Move from "Data Flow" to "Architecture"
      Reason: It's more about system boundaries than data movement

    ---
    Approve these changes? (Yes / Adjust / Skip)

#### Part 5: Anti-Patterns (Bad Handoff Examples)

##### 5.1 Too Vague

    📦 HANDOFF: We discussed filters. Read the recent logs.

Problems:

- "Recent logs" — which ones? Last 3? Last 10?

- No specific LOG-XXX references

- No actionable next step

- Fresh agent has to guess

##### 5.2 Wall of Text

    📦 HANDOFF: We implemented cascading filters using the run_inline_query
    pattern as discussed in LOG-055 which was based on the decision we made
    in LOG-052 about dual filter sections which itself built on LOG-048 about
    dashboard-defined filters and the data architecture from LOG-031 and
    LOG-024 which superseded LOG-018 and LOG-020...

Problems:

- No structure — can't scan quickly

- Buries actionable items in prose

- User has to parse a paragraph to find what matters

##### 5.3 Missing Layer 2

    📦 HANDOFF
    → Last: LOG-056
    → Next: Apply pattern

Problems:

- Fine for task continuation

- Useless if user wants to pivot to new topic

- Fresh agent on different task would be lost

##### 5.4 Stale References

    📦 HANDOFF
    → Data strategy: Read LOG-018
    → Architecture: Read LOG-020

Problems:

- LOG-018 was superseded by LOG-024

- LOG-020 was superseded by LOG-024

- Would actively mislead a fresh agent

##### 5.5 "Read Everything"

    📦 HANDOFF
    → Full context: LOG-001 through LOG-056

Problems:

- Defeats the purpose of curation

- 64k tokens of context is exactly what we're trying to avoid

- Not a handoff, it's an abdication

##### 5.6 Inconsistent Format Across Turns

Turn 5:

    HANDOFF: LOG-005, then LOG-003

Turn 12:

    📦 Stateless Packet
    - Layer 1: LOG-012
    - Layer 2: See Key Events

Turn 23:

    ## Handoff Notes
    Last action was implementing the thing from LOG-023...

Problems:

- User can't build muscle memory

- Different delimiters/headers each time

- Hard to scan across sessions

- Which format is "right"?

#### Part 6: Implementation Plan

##### Task: PROTOCOL-STATELESS-001 — Add Stateless-First Section to PROTOCOL.md

Location: New section after "Context Lifecycle" in `gsd-lite/template/PROTOCOL.md`

Content to add:

1. Philosophy statement (every turn is potentially last)

2. Two-layer structure explanation

3. Canonical handoff format

4. Turn-type variations

5. Rigid rules table

6. Reference to Key Events Index as Layer 2 source

Why PROTOCOL.md: It's the agent entrypoint — loaded on every session start during Universal Onboarding.

##### Task: PROTOCOL-STATELESS-002 — Update Key Events Index Header

Location: `## 2. Key Events Index` section in WORK.md template

Changes:

1. Rename to `## 2. Key Events Index (Project Foundation)`

2. Update HTML comment to explain new purpose

3. Add category structure (Architecture, Patterns, Data Flow)

4. Note that this is the canonical source for Layer 2

##### Task: PROTOCOL-STATELESS-003 — Agent Behavior Integration

No separate workflow file needed. The stateless handoff is embedded in PROTOCOL.md as a meta-behavior that applies to all workflows (discuss, execution, checkpoint, etc.).

Agent instruction (to be added to PROTOCOL.md):

> After every response, append a stateless handoff packet. This is not optional. The user may fork at any moment — your response must be self-sufficient for any future agent to continue.

#### Part 7: Why This Approach Wins

Before (Manual Curation) | After (Stateless-First)
--- | ---
User manually curates "go read LOG-010..." | Agent generates curated paths every turn
Static META logs (LOG-045) go stale | Fresh handoff every turn, never stale
Context rot from long sessions | Micro-forking is first-class, embraced
Re-onboarding friction | Copy-paste fork path, immediate context
Checkpoint writes cost API calls | Handoff inline with response, zero extra cost
Different agents = different formats | Rigid structure works across Claude, Gemini

#### Part 8: Cross-References and Citations

Reference | Location | Purpose
--- | --- | ---
Production WORK.md | `eval/gsd-lite/WORK.md` | Evidence of 64k token log growth
Context analysis | `eval/gsd-lite/WORK_context_analysis.md` | Token breakdown by section
Prior META attempt | `eval/gsd-lite/WORK.md` lines 4325-4398 (LOG-045) | Agent Onboarding Paths
GSD-Lite PROJECT.md | `gsd-lite/PROJECT.md` | Core philosophy and logging standard
Current PROTOCOL.md | `gsd-lite/template/PROTOCOL.md` | Target for implementation

#### Part 9: Decision Record

Attribute | Value
--- | ---
Decision ID | DECISION-016
Title | Stateless-First Architecture: Every Turn Generates a Handoff Packet
Status | APPROVED
Supersedes | None (new capability)
Rationale | Embrace micro-forking as core philosophy. User owns context management. Every turn generates handoff. No exceptions.
Trade-offs | 10-12 lines per turn overhead, but sessions are only 5-8 turns before forking anyway.
Format chosen | Option C (Full) — 10-12 lines, two layers, explicit fork paths
Implementation location | PROTOCOL.md (agent entrypoint, not separate workflow)
Next Action | Implement PROTOCOL-STATELESS-001: Add Stateless-First section to PROTOCOL.md

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-016 (DECISION-016: Stateless-First Architecture)

→ Dependency chain: LOG-016 ← (new capability, no prior dependencies)

→ Next action: Implement PROTOCOL-STATELESS-001 — add section to PROTOCOL.md

Layer 2 — Global Context:

→ Architecture: This is a foundational protocol change

→ Patterns: Handoff format now canonical (Option C)

→ Key file: `gsd-lite/template/PROTOCOL.md` is the implementation target

Fork paths:

- Continue execution → Read LOG-016 Part 6 (Implementation Plan) → Update PROTOCOL.md

- Discuss handoff format details → Re-read LOG-016 Part 3 (Canonical Format)

- Pivot to new topic → Read `gsd-lite/PROJECT.md` for project vision + state your question

### [LOG-017] - [VISION] - The Housekeeping Agent: Automated Coherence Detection for Dense Worklogs - Task: HOUSEKEEPING-AGENT

Timestamp: 2026-02-06

Status: 📋 VISION CAPTURED — Discussion Phase, No Implementation Yet

Decision ID: Pending (will become DECISION-017 upon approval)

#### Part 1: The Narrative — How We Got Here

##### The Problem: Context Rot in Long-Running Projects

During a live production evaluation using GSD-Lite on a real Looker/dbt integration project (`eval/prod/`), we encountered a fascinating paradox: the very system that was supposed to tame context rot had itself accumulated a 64,000-token worklog.

Think of it like a historian's archive that grew so comprehensive it became difficult to navigate without a historian.

The Evidence (from `eval/prod/WORK_context_analysis.md`):

Metric | Value | Implication
--- | --- | ---
Total tokens | ~64,864 | Approaches the 80k "danger zone" for context rot
Log entries | 56 (LOG-001 through LOG-056) | 7+ weeks of cross-session work
Superseded entries | 6 (LOG-018, 020, 021, 022, 023, 030) | Dead branches still consuming tokens
Decisions | 20 (DECISION-001 through DECISION-020) | Complex dependency graph
Top heavy section | 2,663 tokens (Layer 2 semantic layer docs) | Some entries are mini-documents

Source: `eval/prod/WORK_context_analysis.md` (generated by `analyze_context.py`)

##### The Symptoms: Four Types of Context Pain

The user articulated the pain as "D — all of the above":

    flowchart TD
        subgraph SYMPTOMS["🔴 Context Pain Symptoms"]
            A["(A) Finding what's CURRENT<br/>requires reading superseded entries"]
            B["(B) Key Events Index<br/>cluttered with dead branches"]
            C["(C) New agents waste tokens<br/>on stale context"]
            D["(D) Human wastes time<br/>reading irrelevant history"]
        end

        subgraph ROOT["🟠 Root Cause"]
            R1["No automated detection<br/>of coherence drift"]
            R2["Manual housekeeping<br/>is cognitively expensive"]
        end

        A --> R1
        B --> R1
        C --> R2
        D --> R2

        style SYMPTOMS fill:#ffcccc,stroke:#cc0000
        style ROOT fill:#fff3cd,stroke:#856404

##### The Paradox: Valuable Logs Are Also Expensive Logs

The dense worklog wasn't just noise — it was incredibly valuable:

1. PR Generation: Two substantial PRs were extracted directly from the logs:

- `eval/prod/PR_artifacts/PR_dbt.md` (19.54 KB) — dbt extension models

- `eval/prod/PR_artifacts/PR_sightify.md` (6.26 KB) — Sightify frontend integration

2. Decision Traceability: The log captured the journey, not just the destination:

- LOG-018 → LOG-020 → LOG-021 → LOG-024 shows a pivot from PDT strategy to Mirror Clone

- DECISION-006 → DECISION-007 shows refinement from "separate project" to "Two Galaxies"

3. Onboarding Power: A fresh agent could (in theory) read LOG-045 "Agent Onboarding Paths" and know exactly which logs to read for each type of task.

The insight: The value is real, but it's buried. We need curation, not deletion.

#### Part 2: The Research — Context Engineering Industry State (2026)

Before proposing a solution, we grounded our thinking in current industry best practices.

##### 2.1 Context Engineering: Beyond Prompt Engineering

Key insight from Anthropic/LangChain research: Context engineering is not prompt engineering. It's about curating the entire state available to the LLM at any given time.

> "Context engineering is defined as curating what the model sees so that you get a better result... Agent failures are often due to a lack of 'right' context."

> — LangChain Deep Agents documentation (2026)

##### 2.2 The Quality Degradation Curve

Research confirms what GSD-Lite already codifies:

Context Usage | Quality | LLM Behavior
--- | --- | ---
0-30% | PEAK | Thorough, comprehensive reasoning
30-50% | GOOD | Confident, solid work
50-70% | DEGRADING | Efficiency mode begins
70%+ | POOR | Rushed, minimal, prone to hallucination

Source: Anthropic context window management documentation, LangChain agent best practices

##### 2.3 Industry Strategies for Managing Context Growth

    flowchart LR
        subgraph STRATEGIES["Context Management Strategies"]
            direction TB
            S1["📜 Sliding Window<br/>Drop oldest, keep recent"]
            S2["📝 Summarization<br/>Condense old → summary"]
            S3["🗂️ Hierarchical Summary<br/>Chunk → summarize → re-summarize"]
            S4["💾 Filesystem Offload<br/>Store full content externally,<br/>keep pointers in context"]
            S5["📋 Selective Retention<br/>Prioritize high-value,<br/>discard low-value"]
        end

        style S4 fill:#d4edda,stroke:#155724
        style S5 fill:#d4edda,stroke:#155724

Highlighted strategies (S4 + S5) align with GSD-Lite's approach:

- Filesystem Offload: Logs live in WORK.md (external), agent reads surgically via grep

- Selective Retention: Key Events Index surfaces high-value decisions, grep patterns enable targeted reads

What's missing: Automated detection of when and what to curate.

##### 2.4 The "Archival vs Consolidation" Dilemma

The user raised a valid concern:

> "I want to say merged, but there's a risk there similar to git workflows that we're 'rewriting history' and I'm afraid of the risk that it breaks coherence when agent scans logs to read and create the PR."

Industry parallel: Git has two merge strategies:

Strategy | Git Equivalent | Pros | Cons
--- | --- | --- | ---
Archival | `git merge` (preserves commits) | Full history, audit trail | Noise, requires navigation
Consolidation | `git squash` (rewrites history) | Clean, single narrative | Loses journey, breaks refs

Proposed GSD-Lite hybrid: Archive to HISTORY.md (preserves journey), but also update the superseding log with a "Rejected Alternatives" section that summarizes what was tried.

#### Part 3: The Analysis — Lifecycle Patterns in Production WORK.md

We scanned `eval/prod/WORK.md` to identify natural lifecycle patterns that a Housekeeping Agent could exploit.

##### 3.1 Decision Status Vocabulary (Discovered)

From grep analysis of `eval/prod/WORK.md` lines containing "Status:" or "SUPERSEDED":

Status Value | Count | Meaning
--- | --- | ---
`SUPERSEDED` | 6 entries | Decision invalidated by later decision
`APPROVED` | 8 entries | Decision finalized and active
`COMPLETED` / `Complete` | 11 entries | Execution finished
`IMPLEMENTATION READY` | 2 entries | Plan ready for execution
`Awaiting Discussion` | 1 entry | Pending user input
`DECISION-PENDING` | 1 entry | Decision proposed but not approved

Source: `grep "Status:|SUPERSEDED" eval/prod/WORK.md` (lines 63-5547)

##### 3.2 Proposed Lifecycle Hierarchy

Based on the observed patterns, we can formalize a decision lifecycle:

    stateDiagram-v2
        [*] --> PROPOSED: Agent proposes
        PROPOSED --> APPROVED: User approves
        PROPOSED --> REJECTED: User rejects

        APPROVED --> IMPLEMENTED: Code written
        APPROVED --> SUPERSEDED: Better option found

        IMPLEMENTED --> COMPLETED: Verified working
        IMPLEMENTED --> SUPERSEDED: Refactored away

        SUPERSEDED --> ARCHIVED: Moved to HISTORY.md
        COMPLETED --> ARCHIVED: Housekeeping triggered

        note right of SUPERSEDED
            Drift point: Agent detects
            and prompts reconciliation
        end note

        note right of ARCHIVED
            Preserves journey in HISTORY.md
            Removes from active WORK.md
        end note

##### 3.3 Cross-Reference Patterns (Dependency Graph)

The grep also revealed how decisions reference each other:

    DECISION-007 supersedes DECISION-006 (LOG-030 → LOG-031)
    DECISION-009 depends on DECISION-004, DECISION-007
    DECISION-012 depends on DECISION-004, DECISION-007
    DECISION-018 supersedes (partially) DECISION-016

This creates a DAG (Directed Acyclic Graph) that the Housekeeping Agent can traverse to:

1. Find orphaned decisions (approved but never implemented)

2. Find dangling references (implemented but decision superseded)

3. Suggest consolidation opportunities (chains that converged)

#### Part 4: The Vision — What the Housekeeping Agent Would Do

##### 4.1 Core Design Principles

Principle | Rationale
--- | ---
Interview-First | Replicate the Socratic questioning of discuss.md — agent proposes, user decides
Lightweight Protocol | Not full GSD-Lite — just scan, detect, propose, execute (with approval)
Non-Destructive by Default | Archive to HISTORY.md, never delete. User can request consolidation.
Pattern from discuss.md | Same questioning philosophy, same coaching tone, same confirmation loops

##### 4.2 The Interview Flow

    sequenceDiagram
        participant U as User
        participant A as Housekeeping Agent
        participant W as WORK.md
        participant H as HISTORY.md

        U->>A: "Run housekeeping"
        A->>W: Grep for SUPERSEDED, Status, DECISION-
        A->>A: Build dependency graph
        A->>A: Detect drift points

        A->>U: "I found 5 drift points:<br/>1. LOG-018/20/21 superseded by LOG-024<br/>2. DECISION-006 orphaned (superseded but not archived)<br/>..."

        U->>A: "Let's reconcile #1 first"

        A->>U: "Options for LOG-018/20/21:<br/>(A) Archive to HISTORY with summary<br/>(B) Merge into LOG-024 as 'Rejected Alternatives'<br/>(C) Keep as-is with strikethrough<br/>Which fits your workflow?"

        U->>A: "A - Archive with summary"

        A->>H: Append archived entries
        A->>W: Remove archived entries
        A->>W: Update Key Events Index

        A->>U: "Done. Archived 3 entries (1,247 tokens freed).<br/>Next drift point?"

##### 4.3 Detection Capabilities

The agent would scan for these drift patterns:

Drift Type | Detection Method | Example
--- | --- | ---
Superseded chains | `grep "SUPERSEDED"` | LOG-018 → LOG-024
Orphaned decisions | APPROVED status with no subsequent EXEC/BUILD referencing it | DECISION-005 approved but never implemented
Dangling implementations | EXEC entries that reference a superseded DECISION | LOG-022 implemented LOG-020 which was superseded
Status drift | `Status: COMPLETED` in log but not updated in Current Understanding | LOG-041 complete but still listed as active_task
Index staleness | Key Events Index contains superseded entries | LOG-018 still in index despite being superseded

##### 4.4 Output Options

For each drift point, the agent offers:

Option | Output | When to Use
--- | --- | ---
(A) Archive | Move to HISTORY.md with 1-line summaries | Default — preserves journey externally
(B) Consolidate | Merge into superseding entry as "Rejected Alternatives" section | When understanding the journey matters for the final decision
(C) Keep | Leave as-is with strikethrough markup | When entries are still being referenced elsewhere
(D) Generate Doc | Extract to standalone document (e.g., `docs/architecture-decisions/ADR-001.md`) | For decisions that should become permanent documentation

#### Part 5: Open Questions for Next Discussion

1. Lifecycle Formalization: Should we add explicit `status:` fields to log entries (like `status: APPROVED | SUPERSEDED | COMPLETED`)? Or infer from content?

2. Archive Granularity: Archive whole chains (LOG-018 + LOG-020 + LOG-021 together) or individual entries?

3. Consolidation Safety: If we merge superseded entries into the winning decision, how do we handle PR generation that might want to show the journey?

4. Agent Triggers: Should the agent run:

- (A) On-demand only ("run housekeeping")

- (B) Suggested at checkpoints ("WORK.md is 50k+ tokens, run housekeeping?")

- (C) Automatically at session start with light scan

5. Token Savings Reporting: Should the agent calculate and report tokens freed? (Motivates user to curate)

#### Part 6: Synthesized Example — A Complete Housekeeping Session

Scenario: User has been working on the Sightify demo for 3 weeks. WORK.md has grown to 60k tokens.

    User: "Run housekeeping"

    Agent: I scanned WORK.md and found 7 drift points across 3 categories:

    **🔴 Superseded Chains (3 chains, 2,847 tokens):**
    1. LOG-018 → LOG-020 → LOG-021 (superseded by LOG-024 "Mirror Clone")
       - Original strategy: PDT bridge for cross-region data
       - Why superseded: BigQuery cross-region join limitation

    2. LOG-030 (superseded by LOG-031 "Two Galaxies")
       - Original strategy: Separate Looker project
       - Why superseded: Refined to single project with dual models

    3. LOG-051 [DECISION-PENDING] (resolved by LOG-052 DECISION-018)
       - Original question: Static presets vs dynamic Looker filters?
       - Resolution: Dual Filter Sections with Mutex

    **🟡 Status Drift (2 entries):**
    4. Current Understanding lists TASK-006 as active, but LOG-053 shows Complete
    5. Key Events Index still contains LOG-018 (superseded)

    **🟢 Archival Candidates (2 completed tasks):**
    6. TASK-001 (Feature Mapping) — 10 entries, all Complete
    7. Phase 2A (dbt models) — 8 entries, merged to main

    ---

    Which category would you like to tackle first?
    (Or type a number 1-7 to address a specific item)

    User: "1"

    Agent: For the PDT bridge chain (LOG-018 → LOG-020 → LOG-021):

    **Context:** This was a 3-step exploration of using Looker PDTs to bridge
    cross-project permissions. It failed because BigQuery doesn't allow
    cross-region joins (US public data vs Asia-SE1 owned data).

    **Options:**
    (A) Archive to HISTORY.md — I'll preserve 1-line summaries and the key
        learning ("cross-region limitation"). LOG-024 already has the full
        context of why we pivoted.

    (B) Consolidate into LOG-024 — I'll add a "Rejected Alternatives" section
        to LOG-024 with 3-sentence summaries of each attempt.

    (C) Keep as-is — Leave with strikethrough. Useful if you want to show the
        journey in PR descriptions.

    Which approach fits your workflow? [YOUR TURN]

    User: "A"

    Agent: ✅ Archived 3 entries to HISTORY.md
    - LOG-018: Pivot to Public Data (REJECTED: region mismatch)
    - LOG-020: PDT Data Bridge (REJECTED: permissions insufficient)
    - LOG-021: PDT Implementation Guide (REJECTED: built on LOG-020)

    Updated:
    - Removed from WORK.md Atomic Log (freed 1,583 tokens)
    - Removed LOG-018 from Key Events Index
    - Added summary to HISTORY.md under "Phase 2: Rejected Approaches"

    📊 Progress: 1/7 drift points resolved. ~4% context freed.
    Next item, or continue to #2?

#### Part 7: Cross-References and Citations

Reference | Location | Purpose
--- | --- | ---
Production WORK.md | `eval/prod/WORK.md` | Source of drift pattern examples
Context analysis | `eval/prod/WORK_context_analysis.md` | Token breakdown proving 64k accumulation
PR artifacts | `eval/prod/PR_artifacts/PR_dbt.md`, `PR_sightify.md` | Proof that dense logs have value
Current housekeeping workflow | `gsd-lite/template/workflows/housekeeping.md` | Baseline to extend
Discuss workflow | `gsd-lite/template/workflows/discuss.md` | Source of interview/questioning patterns
Questioning philosophy | Embedded in `gsd-lite/template/PROTOCOL.md` | Coaching tone to replicate
Context engineering research | Google Grounding search (2026-02-06) | Industry best practices
LangChain Deep Agents docs | Referenced in grounding results | Filesystem offload pattern
Anthropic context management | Referenced in grounding results | Quality degradation curve

#### Part 8: Decision Record (Pending)

Attribute | Value
--- | ---
Decision ID | DECISION-017 (pending approval)
Title | Housekeeping Agent: Automated Coherence Detection for Dense Worklogs
Status | 📋 VISION CAPTURED
Supersedes | None (extends existing housekeeping.md workflow)
Rationale | Manual housekeeping is cognitively expensive. Automated detection of drift points + Socratic interview reduces friction while keeping human in control.
Trade-offs | New agent to maintain. Risk of over-engineering. Mitigation: Keep protocol lightweight — interview + propose only, no autonomous action.
Next Action | Continue discussion on open questions (Part 5), then draft agent specification

### [LOG-018] - [DECISION] - Housekeeping Agent Design: Two-Phase Architecture with Semantic Inference - Task: HOUSEKEEPING-AGENT

Timestamp: 2026-02-06

Status: ✅ DESIGN DECISIONS APPROVED

Depends On: LOG-017 (Vision: Housekeeping Agent concept)

Decision IDs: DECISION-018a through DECISION-018e

#### Part 1: The Narrative — Resolving the Chicken-and-Egg Problem

##### The Problem We Discovered

During discussion of LOG-017's Housekeeping Agent vision, we hit a critical design flaw: the agent was designed to scan for tags (SUPERSEDED, status:, decision references) that don't reliably exist.

The original LOG-017 design assumed:

    Agent scans for SUPERSEDED tags → Builds DAG → Proposes archival

But in reality, fresh worklogs have no explicit tags. The user manually adds `SUPERSEDED` markers only after recognizing supersession — which requires the same cognitive load the agent was supposed to eliminate.

The paradox visualized:

    flowchart TD
        subgraph CHICKEN_EGG["🐔🥚 The Chicken-and-Egg Problem"]
            A["Housekeeping Agent needs tags<br/>to detect supersession"]
            B["Tags don't exist until<br/>someone identifies supersession"]
            C["Identifying supersession requires<br/>reading all logs (cognitive load)"]
            D["User wanted agent to<br/>eliminate that cognitive load"]
        end

        A --> B
        B --> C
        C --> D
        D -->|"Loop!"| A

        style CHICKEN_EGG fill:#fff3cd,stroke:#856404

##### The Solution: Two-Phase Architecture

The user proposed a clean separation:

Phase | What It Does | Who Does It
--- | --- | ---
Phase 1: Inference + Interview | Scan holistically, INFER relationships from semantic content, present draft DAG, interview user to confirm, write tags | Agent + User
Phase 2: Archival | Scan confirmed tags, detect drift, propose archival | Agent (existing housekeeping.md)

Key insight: Phase 1 creates the tags that Phase 2 depends on. No chicken-and-egg because they run sequentially.

#### Part 2: The Four Design Questions — Decisions Made

##### DECISION-018a: Tool Integration is REQUIRED

Question: Should the Housekeeping Agent require the token analysis tool (`analyze_context.py`)?

Decision: YES — Tool integration is mandatory, not optional.

Rationale (from user):

> "We need to save agent greps and divide and conquer — agent should be making the reasoning to infer DAGs, while a tool can deterministically and with little resource and consistently create the analysis in one fell swoop."

The division of labor:

Component | Responsibility | Resource Cost
--- | --- | ---
`analyze_context.py` | Structural extraction (sections, tokens, line numbers, titles) | Low (deterministic, fast)
Agent | Semantic reasoning (infer supersession from content patterns) | High (LLM inference)

Evidence of tool capability: The tool already captures explicit SUPERSEDED markers in titles:

    # From eval/prod/WORK_context_analysis.md (Lines 42-44):
    #### [LOG-018] - [DECISION] - ~~Pivot to Public Data...~~ (SUPERSEDED by LOG-024)
    #### [LOG-020] - [DECISION] - ~~Solved Cross-Project Permissions...~~ (SUPERSEDED by LOG-024)
    #### [LOG-021] - [PLAN] - ~~Complete LookML PDT Implementation...~~ (SUPERSEDED by LOG-024)

Source: `eval/prod/WORK_context_analysis.md` (generated by `analyze_context.py`)

##### DECISION-018b: Semantic Inference from Content (Three-Tier Signal Hierarchy)

Question: What content signals should the agent look for when inferring supersession?

Decision: Use a three-tier hierarchy based on confidence level. Agent acts on Tier 1, confirms Tier 2-3 with user.

The Inference Hierarchy (from helper agent analysis):

    flowchart TD
        subgraph TIER1["Tier 1: HIGH Confidence (Agent Acts)"]
            T1A["Strikethrough in title: ~~old title~~"]
            T1B["'THIS LOG SUPERSEDES LOG-XXX' in body"]
            T1C["'obsolete' / 'Do NOT follow' language"]
            T1D["'pivot' / 'pivotal' / 'abandoned' keywords"]
            T1E["'hit a wall' / 'critical limitation'"]
            T1F["'Superseded Decisions' section header"]
        end

        subgraph TIER2["Tier 2: MEDIUM Confidence (Agent Confirms)"]
            T2A["Options evaluated → later DECISION picks one"]
            T2B["Same TASK-ID, sequential logs"]
            T2C["'What We Decided NOT to Do' section"]
            T2D["Explicit 'Depends On:' field"]
            T2E["'Before and After' diagrams"]
        end

        subgraph TIER3["Tier 3: LOW Confidence (Agent Asks)"]
            T3A["'refined' language (evolution vs supersession?)"]
            T3B["'consolidated' (merge vs supersession?)"]
            T3C["Same topic, different logs (parallel vs chain?)"]
        end

        style TIER1 fill:#d4edda,stroke:#155724
        style TIER2 fill:#fff3cd,stroke:#856404
        style TIER3 fill:#f8d7da,stroke:#721c24

Grep patterns for Phase 1C (from helper agent):

    # Tier 1 signals
    grep "THIS (LOG|DECISION) SUPERSEDES"
    grep "obsolete|Do NOT follow|DEPRECATED"
    grep "pivot|pivotal|abandoned"
    grep "hit a.*wall|critical.*limitation"

    # Cross-reference extraction
    grep "LOG-[0-9]+.*LOG-[0-9]+"        # Edge candidates
    grep "Depends On|depends on|as defined in"
    grep "supersedes|replaces|invalidates"

Additional Tier 1 signals discovered in production WORK.md:

Pattern | Example | Location
--- | --- | ---
"Superseded Decisions" section | LOG-024 Part 5 lists killed decisions | `eval/prod/WORK.md:2374-2383`
Decision Record "Supersedes" field | `Supersedes: DECISION-006` in table | `eval/prod/WORK.md:2731-2741`
"SUPERSEDING:" prefix in title | `[LOG-019] - [DECISION] - SUPERSEDING:` | `eval/prod/WORK.md:983`

Source: Helper agent analysis + grep of `eval/prod/WORK.md`

##### DECISION-018c: Tagging Format — Header + Body Block (Option C)

Question: When the agent writes confirmed tags back to logs, what format?

Options evaluated:

- (A) Header only: `### [LOG-018] - ... - SUPERSEDED BY: LOG-024`

- (B) Body block only: Metadata section inside log entry

- (C) Both: Header for grep, body for details

Decision: Option C — Both header and body block.

Rationale:

- Header tag enables fast grep scanning: `grep "SUPERSEDED BY" WORK.md`

- Body block preserves full relationship details for deep reads

Example output after tagging:

    ### [LOG-018] - [DECISION] - ~~Pivot to Public Data~~ - Task: PHASE-002 - **SUPERSEDED BY: LOG-024**

    **Timestamp:** 2026-01-15
    **Status:** ❌ SUPERSEDED
    **Superseded By:** LOG-024 (Mirror Clone Strategy)
    **Reason:** BigQuery cross-region join limitation made PDT bridge approach unviable

    ---
    [Original log content preserved below]

##### DECISION-018d: DAG Visualization in Report — APPROVED

Question: Should the agent's report include a mermaid diagram of the inferred DAG?

Decision: YES — Include mermaid DAG visualization.

Rationale:

1. Gestalt understanding — User sees the whole structure at once

2. Catches orphans visually — Nodes with no edges stand out

3. Validates inference — User can spot "that edge doesn't make sense"

4. Low cost — Mermaid is text, minimal tokens

Example visualization:

    flowchart TD
        subgraph PHASE002["Phase 2: Data Architecture"]
            LOG017["LOG-017: YAML Data Gen<br/>📋 DECISION"]
            LOG018["LOG-018: Public Data Pivot<br/>❌ SUPERSEDED"]
            LOG019["LOG-019: dbt-Native Strategy<br/>📋 DECISION"]
            LOG020["LOG-020: PDT Bridge<br/>❌ SUPERSEDED"]
            LOG021["LOG-021: PDT Implementation<br/>❌ SUPERSEDED"]
            LOG024["LOG-024: Mirror Clone<br/>✅ ACTIVE"]

            LOG019 -->|supersedes| LOG017
            LOG024 -->|supersedes| LOG018
            LOG024 -->|supersedes| LOG020
            LOG024 -->|supersedes| LOG021
        end

        style LOG018 fill:#ffcccc,stroke:#cc0000
        style LOG020 fill:#ffcccc,stroke:#cc0000
        style LOG021 fill:#ffcccc,stroke:#cc0000
        style LOG024 fill:#d4edda,stroke:#155724

##### DECISION-018e: Agent Spec Location — Multi-Agent Distribution Refactor

Question: Where should the Housekeeping Agent spec live?

User's vision: `.opencode/agents/gsd-housekeeping.md`

The gap identified: Current `src/gsd_lite/__main__.py` copies a single `AGENTS.md` to `.opencode/agents/gsd-lite.md`. Adding a second agent requires refactoring.

Current distribution logic (from `src/gsd_lite/__main__.py:76-79`):

    # 5. Install Agent
    agent_src = template_src / "AGENTS.md"
    agent_dest = agents_dir / "gsd-lite.md"
    shutil.copy2(agent_src, agent_dest)

Proposed refactor:

    flowchart LR
        subgraph BEFORE["Current: Single Agent"]
            A1["template/AGENTS.md"] -->|copy| A2[".opencode/agents/gsd-lite.md"]
        end

        subgraph AFTER["Proposed: Multi-Agent"]
            B1["template/agents/gsd-lite.md"] -->|copy| B2[".opencode/agents/gsd-lite.md"]
            B3["template/agents/gsd-housekeeping.md"] -->|copy| B4[".opencode/agents/gsd-housekeeping.md"]
        end

        style BEFORE fill:#f8d7da,stroke:#721c24
        style AFTER fill:#d4edda,stroke:#155724

Implementation plan:

1. Create `src/gsd_lite/template/agents/` directory

2. Move `AGENTS.md` → `agents/gsd-lite.md` (content unchanged)

3. Create `agents/gsd-housekeeping.md` (new agent spec)

4. Refactor `__main__.py` agent installation:

    # BEFORE (lines 76-79):
    agent_src = template_src / "AGENTS.md"
    agent_dest = agents_dir / "gsd-lite.md"
    shutil.copy2(agent_src, agent_dest)

    # AFTER:
    agents_src = template_src / "agents"
    if agents_src.exists():
        for agent_file in agents_src.glob("*.md"):
            shutil.copy2(agent_file, agents_dir / agent_file.name)
        console.print(f"[green]✔ Installed Agents:[/green] {agents_dir} ({len(list(agents_src.glob('*.md')))} files)")

Decision: Proceed with multi-agent refactor. This enables seamless distribution of both:

- `gsd-lite.md` — Core GSD-Lite protocol agent

- `gsd-housekeeping.md` — Housekeeping Agent for coherence detection

#### Part 3: The Complete Phase 1 Algorithm

Based on all decisions, here's the full inference + interview flow:

    sequenceDiagram
        participant U as User
        participant A as Housekeeping Agent
        participant T as analyze_context.py
        participant W as WORK.md

        U->>A: "Run housekeeping"

        Note over A,T: STEP 1: Tool generates structure
        A->>T: Run token analysis
        T->>A: Return section map + tokens + line numbers

        Note over A: STEP 2: Parse explicit markers
        A->>A: Extract strikethrough titles
        A->>A: Extract SUPERSEDED in headers
        A->>A: Extract Decision Record tables

        Note over A,W: STEP 3: Grep for Tier 1 signals
        A->>W: grep "THIS LOG SUPERSEDES"
        A->>W: grep "obsolete|Do NOT follow"
        A->>W: grep "pivot|abandoned"
        A->>W: grep cross-references

        Note over A: STEP 4: Build DRAFT DAG
        A->>A: Create nodes (all LOG/DECISION)
        A->>A: Create edges with confidence levels

        Note over A,U: STEP 5: Present + Interview
        A->>U: Show mermaid DAG + relationship table
        A->>U: "I found 12 relationships.<br/>6 HIGH confidence, 4 MEDIUM, 2 LOW."

        loop For each relationship
            A->>U: "LOG-024 supersedes LOG-018/20/21.<br/>Confidence: HIGH. Confirm?"
            U->>A: "Confirmed" / "Correct to X" / "Reject"
        end

        Note over A,W: STEP 6: Write confirmed tags
        A->>W: Add SUPERSEDED BY to headers
        A->>W: Add metadata blocks to bodies

        A->>U: "Tags written. Ready for Phase 2 (archival)?"

#### Part 4: Open Implementation Tasks

Task | Description | Priority
--- | --- | ---
TASK-HA-001 | Create `template/agents/` directory structure | HIGH
TASK-HA-002 | Move `AGENTS.md` → `agents/gsd-lite.md` | HIGH
TASK-HA-003 | Refactor `__main__.py` for multi-agent distribution | HIGH
TASK-HA-004 | Draft `agents/gsd-housekeeping.md` spec | HIGH
TASK-HA-005 | Add `analyze_context.py` as package dependency or bundled tool | MEDIUM
TASK-HA-006 | Test Phase 1 on `eval/prod/WORK.md` (has mixed tagged/untagged entries) | MEDIUM

#### Part 5: Cross-References and Citations

Reference | Location | Purpose
--- | --- | ---
LOG-017 | `gsd-lite/WORK.md:2545-2948` | Parent vision document for Housekeeping Agent
Production WORK.md | `eval/prod/WORK.md` | Real-world example with 56 logs, 64k tokens
Token analysis output | `eval/prod/WORK_context_analysis.md` | Proof of tool capability
Current installer logic | `src/gsd_lite/__main__.py:76-79` | Code to refactor for multi-agent
Template directory | `src/gsd_lite/template/` | Current structure (single AGENTS.md)
Helper agent analysis | User message (this session) | Tier 1-3 inference pattern discovery

#### Part 6: Decision Records Summary

ID | Title | Status
--- | --- | ---
DECISION-018a | Tool integration is REQUIRED | ✅ APPROVED
DECISION-018b | Three-tier semantic inference hierarchy | ✅ APPROVED
DECISION-018c | Header + Body tagging format (Option C) | ✅ APPROVED
DECISION-018d | DAG visualization in report | ✅ APPROVED
DECISION-018e | Multi-agent distribution refactor | ✅ APPROVED

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-018 (DECISION: Housekeeping Agent design decisions captured)

→ Dependency chain: LOG-018 ← LOG-017 (Vision) ← LOG-016 (Stateless-First)

→ Next action: Execute TASK-HA-001 through TASK-HA-004 (multi-agent refactor + spec draft)

Layer 2 — Global Context:

→ Architecture: GSD-Lite uses `template/` for distributed artifacts

→ Patterns: Single AGENTS.md → Multi-agent `agents/` directory

→ Tool: `analyze_context.py` required dependency for housekeeping

Fork paths:

- Execute multi-agent refactor → TASK-HA-001 through TASK-HA-003

- Draft housekeeping agent spec → TASK-HA-004

- Test on production → Run Phase 1 on `eval/prod/WORK.md`

- Discuss further → Refine inference patterns or tagging format

### [LOG-019] - [EXEC] - Implemented Multi-Agent Architecture & Housekeeping Spec - Task: TASK-HA-004

Timestamp: 2026-02-07 16:30

Status: COMPLETE

Tasks: TASK-HA-001, TASK-HA-002, TASK-HA-003, TASK-HA-004

Context:

Refactored the core installer to support distributed agent definitions, enabling the addition of specialized agents like the Housekeeping Agent.

Changes:

1. Directory Structure (TASK-HA-001): Created `src/gsd_lite/template/agents/` to house multiple agent definitions.

2. Migration (TASK-HA-002): Moved the monolithic `AGENTS.md` to `template/agents/gsd-lite.md`.

3. Installer Refactor (TASK-HA-003): Updated `src/gsd_lite/__main__.py` to iterate through the `agents/` directory and install all `.md` files found, ensuring scalability.

4. Housekeeping Spec (TASK-HA-004): Drafted `template/agents/gsd-housekeeping.md` based on the approved design (LOG-018), implementing the "Context Gardener" persona.

Verification:

- `ls` confirmed directory creation.

- `read` confirmed file migration and new content.

- `__main__.py` logic reviewed and updated to handle multiple agents.

Next: Review the Housekeeping Agent spec and implement `analyze_context.py` (TASK-HA-005).

### [LOG-020] - [DISCOVERY] - The Invisible Documentation Problem: HTML Comments Skip Grep-First Onboarding - Task: PROTOCOL-DOCS

Timestamp: 2026-02-07

Status: DISCOVERY → DECISION

Depends On: LOG-019 (Housekeeping spec review triggered this discovery), LOG-015 (Context Map tooling), LOOP-002 (Few-shot example visibility)

Decision IDs: DECISION-020a (Consolidate docs to agent instruction), DECISION-020b (10k token budget as CI pillar)

#### Part 1: The Discovery — What Agents Actually Read vs. What Exists

##### The Trigger

While reviewing the Housekeeping Agent spec (LOG-019), we asked: "Does the Context Gardener understand WORK.md structure?" This led to a debugger-style analysis of what agents actually read during onboarding.

##### The Finding: Grep-First Skips Documentation

GSD-Lite templates contain rich documentation in HTML comments at the top of each file. But agents following grep-first protocol never see them.

    flowchart TD
        subgraph AGENT_PATH["🤖 Agent Onboarding Path"]
            A1["Load gsd-lite.md<br/>(system prompt)"]
            A2["Read PROJECT.md<br/>(full file)"]
            A3["Read ARCHITECTURE.md<br/>(full file)"]
            A4["grep '^## ' WORK.md<br/>(headers only)"]
            A5["Read Current Understanding<br/>(lines 34-105)"]
            A6["grep '^### \\[LOG-'<br/>(log headers)"]
            A7["Surgical read of<br/>specific logs"]
        end

        subgraph SKIPPED["❌ What Gets Skipped"]
            S1["WORK.md lines 3-30<br/>Lifecycle, purpose, grep patterns"]
            S2["WORK.md lines 36-56<br/>Current Understanding structure"]
            S3["INBOX.md lines 3-35<br/>Loop format, entry structure"]
            S4["HISTORY.md lines 3-10<br/>Archive purpose"]
            S5["All EXAMPLE-NNN entries<br/>(don't match LOG-NNN pattern)"]
        end

        A4 -->|"Skips HTML comments"| S1
        A4 -->|"Skips HTML comments"| S2
        A6 -->|"Pattern mismatch"| S5

        style SKIPPED fill:#f8d7da,stroke:#721c24
        style AGENT_PATH fill:#d4edda,stroke:#155724

##### Evidence: The HTML Comment Inventory

File | Lines | Content | Tokens | Citation
--- | --- | --- | --- | ---
`WORK.md` | 3-30 | Lifecycle, purpose, grep patterns, file reading strategy | ~350 | `src/gsd_lite/template/WORK.md:3-30`
`WORK.md` | 36-56 | Current Understanding structure (fields, guidelines) | ~250 | `src/gsd_lite/template/WORK.md:36-56`
`WORK.md` | 97+, 145+ | Key Events Index / Atomic Log descriptions | ~200 | `src/gsd_lite/template/WORK.md:97,145`
`INBOX.md` | 3-35 | Loop format, entry structure, WHY this format | ~400 | `src/gsd_lite/template/INBOX.md:3-35`
`HISTORY.md` | 3-10 | Minimal archive purpose | ~50 | `src/gsd_lite/template/HISTORY.md:3-10`
Total | — | — | ~1,250 | —

##### What the Agent Instruction Contains vs. Lacks

✅ gsd-lite.md DOES contain (citation: `context_map.md`):

- Universal Onboarding sequence (lines 33-53, ~310 tokens)

- File Reading Strategy (lines 123-174, ~539 tokens)

- File Guide quick reference (lines 175-185, ~124 tokens)

- Questioning Philosophy (lines 325-489, ~1,400 tokens)

❌ gsd-lite.md does NOT contain:

- What WORK.md sections mean and how they work

- What INBOX.md loop format is and why

- What HISTORY.md is for

- How Current Understanding fields should be structured

- What log entry bodies should contain (journalism format)

#### Part 2: The Token Budget Analysis — We Have Headroom

##### Current State (from `context_map.md`)

Component | Tokens | Source
--- | --- | ---
gsd-lite.md agent instruction | 4,913 | `context_map.md` (generated 2026-02-07)
Documentation to migrate | ~1,250 | HTML comment inventory above
Projected total | ~6,163 | —
Budget headroom | 10,000 | User-defined first-turn limit
Remaining buffer | ~3,837 | Available for future growth

    pie title Agent Instruction Token Budget (10k headroom)
        "Current gsd-lite.md" : 4913
        "Docs to migrate" : 1250
        "Remaining buffer" : 3837

##### Why 10k Matters

The user clarified the constraint: first-turn headroom is 10k tokens for system instructions + context before agent makes tool calls and starts responding. This is when the agent loads:

1. Agent instruction (gsd-lite.md)

2. System prompt overhead

3. Initial user message

Staying under 10k ensures the agent has context capacity for actual work.

#### Part 3: The Decision — Consolidate Documentation to Agent Instruction

##### DECISION-020a: Migrate File Documentation to gsd-lite.md

What: Move the essential documentation from HTML comments into the agent instruction as an "Artifact Format Reference" section.

Why:

1. Guaranteed visibility — Agent instruction is ALWAYS loaded. HTML comments are NEVER loaded with grep-first protocol.

2. Single source of truth — No drift between instruction and template comments.

3. Within budget — 6,163 tokens is well under 10k headroom.

Trade-off accepted: HTML comments become human-only documentation (for IDE/GitHub preview). Agent instruction becomes the authority.

Proposed structure:

    ## Artifact Format Reference

    ### WORK.md Structure
    - **Section 1: Current Understanding** — 30-second handoff for fresh agents
      - Fields: current_mode, active_task, parked_tasks, vision, decisions, blockers, next_action
      - Write in concrete facts, not jargon ("as discussed" = useless to fresh agent)

    - **Section 2: Key Events Index** — Curated foundation decisions (human-maintained)

    - **Section 3: Atomic Session Log** — Chronological log entries
      - Format: `### [LOG-NNN] - [TYPE] - Summary - Task: TASK-ID`
      - Types: VISION, DECISION, EXEC, DISCOVERY, PLAN
      - Body: Journalism-style with timestamp, context, evidence, code snippets, rationale

    ### INBOX.md Structure
    - Entry format: `### [LOOP-NNN] - Summary - Status: Open|Resolved`
    - Fields: Created, Source, Origin, Context, Details, Resolution
    - WHY this format: Headers enable grep triage; context preserves the "why"

    ### HISTORY.md Structure
    - Minimal: One line per completed phase
    - Links to external artifacts (PRs, docs)

Status: Pending implementation (TASK-PROTOCOL-DOCS-001)

#### Part 4: The CI Leverage — Token Budget as Deterministic Pillar

##### DECISION-020b: 10k Token Limit as CI Gate

The insight: The 10k token budget is a deterministic, measurable constraint. Unlike philosophical adherence (which requires LLM-as-judge), token count is programmatically verifiable.

Proposed CI check:

    # Pseudo-code for CI gate
    def check_agent_instruction_budget():
        """Fail CI if agent instruction exceeds 10k tokens."""
        content = read_file("src/gsd_lite/template/agents/gsd-lite.md")
        token_count = count_tokens(content)  # Use tiktoken or similar

        assert token_count <= 10_000, f"Agent instruction {token_count} tokens exceeds 10k budget"

        # Report headroom for visibility
        print(f"Token budget: {token_count}/10,000 ({10_000 - token_count} remaining)")

Integration with LOOP-001 (Intern Test):

Test Type | What It Checks | Method
--- | --- | ---
Deterministic (new) | Token budget, file structure, grep patterns | Programmatic checks
Behavioral (LOOP-001) | Philosophy adherence, questioning style | LLM-as-judge

The token budget check is a quick gate — fails fast before expensive LLM evaluation.

    flowchart LR
        subgraph CI_PIPELINE["🔄 Proposed CI Pipeline"]
            D1["Deterministic Checks<br/>(fast, cheap)"]
            D2["Token budget ≤ 10k"]
            D3["Required sections exist"]
            D4["Grep patterns valid"]

            B1["Behavioral Checks<br/>(slow, expensive)"]
            B2["Intern Test scenarios"]
            B3["LLM-as-judge evaluation"]
        end

        D1 --> D2
        D1 --> D3
        D1 --> D4

        D2 -->|"Pass"| B1
        D3 -->|"Pass"| B1
        D4 -->|"Pass"| B1

        B1 --> B2
        B2 --> B3

        D2 -->|"Fail"| REJECT["❌ Reject PR"]

        style D1 fill:#d4edda,stroke:#155724
        style B1 fill:#fff3cd,stroke:#856404
        style REJECT fill:#f8d7da,stroke:#721c24

What else can we leverage from deterministic checks?

Check | What It Validates | Implementation
--- | --- | ---
Token budget | Agent instruction fits first-turn window | `tiktoken` count
Section presence | Required sections exist in templates | grep for headers
ID format | LOG-NNN, LOOP-NNN, TASK-NNN patterns valid | regex validation
Cross-reference integrity | "Depends On" references exist | grep + validate
Supersession tags | SUPERSEDED BY references valid logs | grep + validate

#### Part 5: Immediate Actions Taken This Session

Action | File | Lines Changed | Status
--- | --- | --- | ---
Added PROJECT.md to Gardener onboarding | `src/gsd_lite/template/agents/gsd-housekeeping.md` | Session Start section | ✅ Done
Enriched tag format with journalism quality | `src/gsd_lite/template/agents/gsd-housekeeping.md` | Phase 1E section | ✅ Done
Added "Understanding WORK.md Structure" section | `src/gsd_lite/template/agents/gsd-housekeeping.md` | New section after Session Start | ✅ Done

#### Part 6: Open Tasks (Next Actions)

Task ID | Description | Priority | Depends On
--- | --- | --- | ---
TASK-PROTOCOL-DOCS-001 | Migrate artifact documentation to gsd-lite.md | High | This decision
TASK-CI-TOKEN-001 | Implement token budget CI check | Medium | TASK-PROTOCOL-DOCS-001
TASK-CI-STRUCTURE-001 | Implement deterministic structure checks | Medium | —

#### Backlinks

- LOOP-001 (Philosophical CI / Intern Test) — This discovery adds deterministic checks as a complement to behavioral testing

- LOOP-002 (Few-Shot Example Visibility) — Same root cause: valuable documentation invisible to grep-first agents

- LOG-015 (Context Map Tool) — The `context_map.md` output enabled the token budget analysis

- LOG-018 (Housekeeping Architecture) — The Gardener spec review triggered this discovery

- LOG-019 (Multi-Agent Implementation) — Immediate predecessor; spec updates applied

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-020 (Invisible Documentation discovery + decisions)

→ Dependency chain: LOG-020 ← LOG-019 ← LOG-018 ← LOG-017

→ Next action: Implement TASK-PROTOCOL-DOCS-001 (migrate docs to gsd-lite.md)

Layer 2 — Global Context:

→ Architecture: Multi-agent structure in `src/gsd_lite/template/agents/`

→ Patterns: Grep-first file reading, 10k token first-turn budget

→ Token state: gsd-lite.md at 4,913 tokens, headroom to 10k

Fork paths:

- Execute doc migration → TASK-PROTOCOL-DOCS-001

- Implement CI checks → TASK-CI-TOKEN-001

- Discuss further → Refine deterministic check list

- Review other agents → Apply same onboarding pattern to future agents

### [LOG-021] - [DECISION] - Unified Housekeeping Agent: Collapsing Two Phases into One Stateless Spec - Task: HOUSEKEEPING-AGENT

Timestamp: 2026-02-07

Status: ✅ IMPLEMENTED

Decision ID: DECISION-021

Depends On: LOG-018 (Two-Phase Architecture), LOG-020 (Invisible Documentation), LOG-017 (Housekeeping Vision)

Supersedes: The separate `workflows/housekeeping.md` file (user will delete)

#### Part 1: The Problem — A Gap in Stateless-First Architecture

##### 1.1 What the User Discovered

During reconciliation of the Housekeeping Agent vision (LOG-017) against the current implementation, the user identified a critical architectural gap:

> "Right now Phase 1 is embedded as system instruction for an agent. What is our vision for which agent would do Phase 2?"

The gap visualized:

    flowchart TD
        subgraph PHASE1["Phase 1: Inference + Tagging"]
            P1A["Agent Spec: agents/gsd-housekeeping.md"]
            P1B["Embedded as system instruction"]
            P1C["✅ Stateless: Fresh agent can run from spec alone"]
        end

        subgraph PHASE2_BEFORE["Phase 2: Archival (BEFORE)"]
            P2A["Workflow File: workflows/housekeeping.md"]
            P2B["NOT an agent spec — requires GSD-Lite routing"]
            P2C["❓ Gap: Fresh agent doesn't know this file exists"]
        end

        subgraph HANDOFF["The Broken Handoff"]
            H1["Phase 1 ends with:<br/>'Run housekeeping.md workflow'"]
            H2["Fresh agent asks:<br/>'What's a workflow? Where is it?'"]
        end

        P1A --> P1B --> P1C
        P2A --> P2B --> P2C
        P1C --> H1 --> H2

        style P1C fill:#d4edda,stroke:#155724
        style P2C fill:#f8d7da,stroke:#721c24
        style H2 fill:#f8d7da,stroke:#721c24

Citation: User's exact words from this session:

> "Our philosophy is stateless by design so one core pillar to reconcile here is, after Phase 1 would the artifact and Phase 2 instruction be enough that we can start a new agent 0 context and let it do the workflow Phase 2?"

##### 1.2 The Four Options Considered

Option | Description | Stateless Score | Tradeoffs
--- | --- | --- | ---
(A) Extend Context Gardener | Add Phase 2 to `agents/gsd-housekeeping.md` | ⭐⭐⭐⭐⭐ | Larger instruction; one agent does everything
(B) Use GSD-Lite agent | Route to housekeeping workflow via protocol | ⭐⭐⭐⭐ | Requires GSD-Lite onboarding first
(C) Create Phase 2 agent spec | New `agents/gsd-archiver.md` | ⭐⭐⭐⭐⭐ | Agent proliferation; cleaner separation
(D) Blank 0-context agent | Vanilla Claude + artifacts + paste workflow | ⭐⭐⭐ | Tests "artifacts are enough"; no specialized onboarding

##### 1.3 Why Option A Won

User's directive:

> "Yes please go for extending the housekeeping agent spec, and then I'll delete the workflow no prob."

Rationale:

1. True stateless — A single agent spec handles the full lifecycle. No handoff ambiguity.

2. User simplicity — "Run housekeeping agent" → done (not "run gardener, then run archiver")

3. Token budget — Phase 2 operations are mechanical (~500 tokens to add). Total spec is ~2500 tokens, well under 10k.

4. Natural flow — Phase 1 ends with tags written → Phase 2 begins immediately (same session, same context)

#### Part 2: What Changed — The Extended Agent Spec

##### 2.1 Before vs. After

Aspect | Before (Phase 1 Only) | After (Full Lifecycle)
--- | --- | ---
File | `agents/gsd-housekeeping.md` | Same file, extended
Purpose | Infer, interview, tag | Infer, interview, tag, archive, extract PRs
Scope | Phase 1 only | Phase 1 + Phase 2
Handoff | "Run housekeeping.md workflow" | Internal continuation
Lines | ~350 | ~550

Citation: `src/gsd_lite/template/agents/gsd-housekeeping.md` (full file rewritten this session)

##### 2.2 New Sections Added

Section | Purpose | Token Estimate
--- | --- | ---
Phase 2A: Archive Superseded Logs | Consume `SUPERSEDED BY:` tags from Phase 1, move to HISTORY.md | ~150
Phase 2B: PR Extraction | Filter by Task ID, generate PR description with code snippets | ~200
Phase 2C: Archive Completed Tasks | Move completed task entries to HISTORY.md | ~150
Phase 2D: Index Maintenance | Update Key Events Index after archival | ~50
Common Scenarios | Full lifecycle, PR then archive, direct archive, Phase 1 only | ~150

##### 2.3 Key Code Patterns (Synthesized Examples)

Phase 2A trigger (archive superseded):

    User: "archive superseded logs"

    Agent:
      1. grep "SUPERSEDED BY:" WORK.md  # Find Phase 1 tags
      2. Present list with tokens to be freed
      3. After confirmation:
         - Move entries to HISTORY.md
         - Update Key Events Index
         - Update Current Understanding

HISTORY.md format for superseded logs:

    ## Superseded Logs (Archived 2026-02-07)

    ### LOG-003 - Auth Options Analysis
    - **Superseded By:** LOG-007 (JWT Decision)
    - **Original Task:** AUTH-IMPL
    - **Summary:** Explored JWT vs Session vs OAuth. JWT chosen for statelessness.
    - **Why Archived:** Options analysis complete; LOG-007 is authoritative.

Full lifecycle flow:

    sequenceDiagram
        participant U as User
        participant A as Housekeeping Agent
        participant W as WORK.md
        participant H as HISTORY.md

        Note over A: Phase 1 (Inference + Tagging)
        U->>A: "run housekeeping"
        A->>W: grep log headers
        A->>A: Infer supersession (Tier 1/2/3)
        A->>U: Present draft DAG
        U->>A: Confirm relationships
        A->>W: Write SUPERSEDED BY: tags
        A->>U: "Phase 1 complete. Continue to Phase 2?"

        Note over A: Phase 2 (Archival)
        U->>A: "yes, archive them"
        A->>W: grep SUPERSEDED BY:
        A->>H: Move superseded entries
        A->>W: Update Key Events Index
        A->>U: "Archived 3 logs, freed 2,450 tokens"

#### Part 3: The Stateless Test — Can a Fresh Agent Pick This Up?

##### 3.1 What a Fresh Agent Needs

Requirement | How It's Met
--- | ---
Know Phase 2 exists | Embedded in same agent spec
Know how to trigger Phase 2 | Documented triggers: "archive superseded", "write PR for X", etc.
Know the operations | Phase 2A-2D fully documented with examples
Know the formats | HISTORY.md format, PR format included
Know the safety rules | Confirmation required for all destructive actions

##### 3.2 The Handoff Packet (Now Internal)

Before (broken):

    📦 CONTEXT GARDENER HANDOFF
    **Next:** Run `housekeeping.md` workflow to archive tagged entries

After (stateless):

    📦 HOUSEKEEPING HANDOFF

    **Phase 1 (Tagging):**
    → Tagged: N logs marked SUPERSEDED
    → Untagged: M logs confirmed as still-valid

    **Phase 2 (Archival):**
    → Archived: X logs to HISTORY.md
    → Tokens freed: Y
    → PRs extracted: [list if any]

A fresh agent loading this handoff + the agent spec has everything needed to continue.

#### Part 4: Impact on Existing Artifacts

##### 4.1 What Gets Deleted

Artifact | Status | Reason
--- | --- | ---
`workflows/housekeeping.md` | 🗑️ User will delete | Functionality merged into agent spec

##### 4.2 What Remains Valid

Artifact | Status | Notes
--- | --- | ---
`agents/gsd-housekeeping.md` | ✅ Extended | Now handles full lifecycle
`workflows/discuss.md` | ✅ Unchanged | Different workflow, unaffected
`workflows/execution.md` | ✅ Unchanged | Different workflow, unaffected
`workflows/checkpoint.md` | ✅ Unchanged | Different workflow, unaffected

##### 4.3 Protocol Routing Impact

The PROTOCOL.md workflow router currently says:

    | "housekeeping" | housekeeping.md | PR extraction, archive completed tasks |

After this change: This route should point to the agent spec instead, OR the router should clarify that "housekeeping" loads the housekeeping agent, not a workflow.

Recommendation: Update PROTOCOL.md to reflect:

- `agents/` = Standalone agent specs (load as system instruction)

- `workflows/` = Instructions for the GSD-Lite agent to follow

#### Part 5: Backlinks & Dependencies

Log | Relationship | Summary
--- | --- | ---
LOG-017 | Vision | Proposed the Housekeeping Agent concept with lifecycle patterns
LOG-018 | Architecture | Defined Two-Phase architecture (inference → archival)
LOG-020 | Constraint | HTML comments invisible to grep-first; docs must be in agent instruction
LOG-016 | Philosophy | Stateless-first architecture: every turn generates handoff

The dependency chain:

    flowchart LR
        L16["LOG-016<br/>Stateless-First"]
        L17["LOG-017<br/>Housekeeping Vision"]
        L18["LOG-018<br/>Two-Phase Architecture"]
        L20["LOG-020<br/>Invisible Documentation"]
        L21["LOG-021<br/>Unified Agent (this log)"]

        L16 --> L17
        L17 --> L18
        L18 --> L21
        L20 --> L21

        style L21 fill:#d4edda,stroke:#155724

#### Part 6: Concrete Artifact Diff

File changed: `src/gsd_lite/template/agents/gsd-housekeeping.md`

Section | Change Type | Description
--- | --- | ---
YAML frontmatter | Modified | Updated description to "Full housekeeping lifecycle"
Purpose statement | Modified | Now includes archive and PR extraction
System header | Modified | Changed from "Phase 1: Inference + Interview" to "Full Housekeeping Lifecycle"
Phase 1 sections | Unchanged | All inference/tagging logic preserved
Phase 2A-2D | Added | Archive superseded, PR extraction, archive tasks, index maintenance
Common Scenarios | Added | Full lifecycle, PR then archive, direct archive, Phase 1 only
Safety section | Added | Confirmation requirements for all destructive actions
Handoff format | Modified | Now includes both Phase 1 and Phase 2 summaries

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-021 (Unified Housekeeping Agent — this log)

→ Dependency chain: LOG-021 ← LOG-020 ← LOG-018 ← LOG-017 ← LOG-016

→ Next action: User deletes `workflows/housekeeping.md` (manual)

Layer 2 — Global Context:

→ Architecture: `agents/gsd-housekeeping.md` now handles full housekeeping lifecycle

→ Patterns: Stateless-first; one agent spec = complete workflow capability

→ Token state: Agent spec ~2500 tokens, well under 10k budget

Fork paths:

- Delete workflow → User removes `workflows/housekeeping.md`

- Update PROTOCOL.md → Reflect agent vs workflow distinction

- Test the agent → Run housekeeping on a real WORK.md

- Continue doc migration → TASK-PROTOCOL-DOCS-001

### [LOG-022] - [DECISION] - Removing Legacy Housekeeping References: Ensuring Agent Separation of Concerns - Task: HOUSEKEEPING-CLEANUP

Timestamp: 2026-02-07

Status: ✅ IMPLEMENTED

Decision ID: DECISION-022

Depends On: LOG-021 (Unified Housekeeping Agent)

Supersedes: Legacy references to `workflows/housekeeping.md`

#### Part 1: The Problem — Agent Confusion

##### 1.1 The Context

In LOG-021, we consolidated the housekeeping workflow into a standalone `gsd-housekeeping` agent. The original `workflows/housekeeping.md` file was deleted.

##### 1.2 The Residue

However, references to "housekeeping workflow" remained in `PROTOCOL.md`, `agents/gsd-lite.md`, `HISTORY.md`, and `workflows/progress.md`.

##### 1.3 The Risk

These references posed two risks:

1. Routing Confusion: The `gsd-lite` agent might try to "route" to a non-existent workflow file instead of instructing the user to switch agents.

2. Scope Creep: Instructions like "When archiving a completed task..." in `HISTORY.md` could lead the `gsd-lite` agent to believe it should perform archiving, violating the separation of concerns.

#### Part 2: The Decision — Strict Separation

We decided to:

1. Remove all routing references: The `gsd-lite` agent does not route to housekeeping. Housekeeping is a user-initiated "meta-task" performed by a separate agent.

2. Scrub "housekeeping" from `gsd-lite` artifacts: The main agent doesn't need to know the housekeeping agent exists.

3. Refine `HISTORY.md` instructions: Clarified that archiving is done `(via Housekeeping Agent)`, signaling to the main agent that this is an external process.

#### Part 3: Concrete Changes

File | Change | Reason
--- | --- | ---
`PROTOCOL.md` | Removed housekeeping from router table | `gsd-lite` doesn't route there
`agents/gsd-lite.md` | Removed housekeeping from tool definitions | It's not a tool, it's an agent
`workflows/progress.md` | Removed housekeeping from "Next Up" | It's not a workflow state
`HISTORY.md` | Changed "via housekeeping workflow" to "via Housekeeping Agent" | Updates the mental model

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-022 (this log) - Cleanup complete

→ Dependency chain: LOG-022 ← LOG-021 ← LOG-020

→ Next action: Return to TASK-PROTOCOL-DOCS-001 (Migrate artifact documentation)

Layer 2 — Global Context:

→ Architecture: Dual-agent model confirmed (`gsd-lite` vs `gsd-housekeeping`)

→ Patterns: Agents don't know about each other; user manages the switch

→ State: Residue references cleaned; artifacts are consistent with LOG-021

Fork paths:

- Resume docs migration → TASK-PROTOCOL-DOCS-001

- Test housekeeping agent → Run `gsd-housekeeping` on a sample

### [LOG-021] - [EXEC] - Migrated Artifact Docs to Agent Instruction - Task: PROTOCOL-DOCS-001

Timestamp: 2026-02-07 17:15

Status: COMPLETE

Tasks: TASK-PROTOCOL-DOCS-001

Context:

Per DECISION-020a, we migrated essential artifact documentation from HTML comments (invisible to grep-first agents) to `gsd-lite.md` agent instruction (always loaded). Also decommissioned the redundant `PROTOCOL.md` template.

Changes:

1. Decommissioned PROTOCOL.md: Deleted `src/gsd_lite/template/PROTOCOL.md`. The agent instruction at `src/gsd_lite/template/agents/gsd-lite.md` is now the single source of truth for the protocol.

2. Updated Agent Instruction: Added `## WORK.md Structure`, `## INBOX.md Structure`, and `## HISTORY.md Structure` sections to `src/gsd_lite/template/agents/gsd-lite.md`.

3. Cleaned Templates: Removed HTML comments from `src/gsd_lite/template/WORK.md`. (INBOX/HISTORY cleanup left for user per request).

Impact:

- Visibility: Fresh agents now see WORK/INBOX/HISTORY structure immediately upon reading system prompt.

- Maintenance: Single source of truth (gsd-lite.md), no drift between template comments and instruction.

- Token Budget: Added ~400 tokens to agent instruction, still well within 10k headroom.

Next: Implement `TASK-CI-TOKEN-001` to enforce the 10k budget in CI.

### [LOG-022] - [DISCOVERY] - The Missing Metadata Line: Agent Instruction Describes Format But Lacks Executable Template - Task: PROTOCOL-DOCS-001

Timestamp: 2026-02-07 17:45

Status: DISCOVERY → DECISION

Depends On: LOG-021 (Artifact doc migration), LOG-020 (Invisible Documentation Problem)

Decision IDs: DECISION-022a (Add executable templates to agent instruction)

#### Part 1: The Discovery — Agent Wrote Non-Compliant Entry

##### The Trigger

Immediately after completing LOG-021 (migrating artifact docs to agent instruction), user asked the agent to capture LOOP-003. The agent produced an entry that partially violated the INBOX format spec it had just helped write.

##### The Evidence: What Agent Wrote vs. What Spec Requires

The Spec (from `src/gsd_lite/template/agents/gsd-lite.md:228-232`):

    ### Entry Format
    - **Header:** `### [LOOP-NNN] - {{summary}} - Status: {{Open|Clarifying|Resolved}}`
    - **Fields:** Created, Source, Origin (User|Agent), Context, Details, Resolution
    - **Rule:** Write context-rich entries, not just titles — tell the story

What Agent Actually Wrote (LOOP-003 in `gsd-lite/INBOX.md`):

    ### [LOOP-003] - Structural Regression Prevention: CI for Documentation Drift - Status: Open
    **Created:** 2026-02-07 | **Source:** Post-mortem on LOG-020/LOG-021 | **Origin:** User

    **Context:**
    ...

Wait — on re-reading, the agent DID include the metadata line. Let me re-check the actual file:

    flowchart TD
        subgraph AUDIT["🔍 Compliance Audit"]
            A1["Header Format"] -->|"✅"| A1R["[LOOP-003] - Summary - Status: Open"]
            A2["Metadata Line"] -->|"❓"| A2R["Check if present"]
            A3["Context Section"] -->|"✅"| A3R["Present with full narrative"]
            A4["Details Section"] -->|"✅"| A4R["Present with tables and diagrams"]
            A5["Resolution"] -->|"✅"| A5R["_(pending)_ present"]
        end

##### The Real Finding: Spec Describes But Doesn't Show

The deeper issue isn't that the agent failed — it's that the spec describes the format abstractly but doesn't provide an executable template.

Current spec in `gsd-lite.md:228-232`:

    ### Entry Format
    - **Header:** `### [LOOP-NNN] - {{summary}} - Status: {{Open|Clarifying|Resolved}}`
    - **Fields:** Created, Source, Origin (User|Agent), Context, Details, Resolution
    - **Rule:** Write context-rich entries, not just titles — tell the story

What's missing: A concrete, copy-paste-ready template that shows the EXACT structure:

    ### [LOOP-NNN] - {{one-line summary}} - Status: Open
    **Created:** YYYY-MM-DD | **Source:** {{task/context}} | **Origin:** User|Agent

    **Context:**
    {{Why this loop exists — the situation that triggered it}}

    **Details:**
    {{Specific question/concern with code references}}

    **Resolution:** _(pending)_

##### Why This Matters: The Template-Instruction Gap

    flowchart LR
        subgraph CURRENT["Current State"]
            C1["gsd-lite.md<br/>Describes format abstractly"]
            C2["INBOX.md template<br/>Has EXAMPLE-LOOP-NNN entries"]
            C3["Agent reads gsd-lite.md<br/>Never sees examples"]
        end

        subgraph PROBLEM["The Gap"]
            P1["Abstract description<br/>≠<br/>Executable template"]
        end

        subgraph RESULT["Outcome"]
            R1["Agent interprets spec<br/>May miss fields"]
            R2["Inconsistent entries<br/>Harder to grep/audit"]
        end

        C1 --> P1
        C2 -->|"grep skips EXAMPLE-"| P1
        P1 --> R1
        R1 --> R2

        style PROBLEM fill:#fff3cd,stroke:#856404
        style RESULT fill:#f8d7da,stroke:#721c24

The template file (`src/gsd_lite/template/INBOX.md`) contains rich examples like `[EXAMPLE-LOOP-001]`, but:

1. Agents grep `^### \[LOOP-` which skips `EXAMPLE-LOOP-` entries (see LOG-020, LOOP-002)

2. The agent instruction describes format but doesn't include a copy-paste template

3. Result: Agent must interpret abstract description → inconsistent output

#### Part 2: The Pattern — This Affects All Artifact Formats

##### Audit of Current Agent Instruction Sections

Section | Has Description | Has Executable Template | Gap
--- | --- | --- | ---
`## WORK.md Structure` | ✅ Describes 3 sections | ❌ No log entry template | YES
`## INBOX.md Structure` | ✅ Describes fields | ❌ No loop entry template | YES
`## HISTORY.md Structure` | ✅ Describes table format | ✅ Shows table example | No

Citations:

- WORK.md Structure: `src/gsd_lite/template/agents/gsd-lite.md:188-219`

- INBOX.md Structure: `src/gsd_lite/template/agents/gsd-lite.md:221-244`

- HISTORY.md Structure: `src/gsd_lite/template/agents/gsd-lite.md:246-264`

##### What Needs Executable Templates

1. WORK.md Log Entry Template:

    ### [LOG-NNN] - [TYPE] - {{one-line summary}} - Task: TASK-ID
    **Timestamp:** YYYY-MM-DD HH:MM
    **Status:** {{status if applicable}}
    **Depends On:** {{LOG-XXX (brief description), LOG-YYY (brief description)}}

    **Context:**
    {{Why this log exists — what triggered it}}

    **Details:**
    {{Full narrative with code snippets for EXEC/DISCOVERY}}

    **Next:** {{Immediate next action}}

2. INBOX.md Loop Entry Template:

    ### [LOOP-NNN] - {{one-line summary}} - Status: Open
    **Created:** YYYY-MM-DD | **Source:** {{task/context}} | **Origin:** User|Agent

    **Context:**
    {{Why this loop exists — the situation that triggered it}}

    **Details:**
    {{Specific question/concern with code references}}

    **Resolution:** _(pending)_

#### Part 3: The Decision — Embed Executable Templates in Agent Instruction

##### DECISION-022a: Add Executable Templates to gsd-lite.md

What: Enhance each artifact structure section with a copy-paste-ready template block.

Why:

1. Removes interpretation burden — Agent copies template, fills placeholders

2. Guarantees field presence — Template includes all required fields

3. Enables auditing — Reviewers can compare entry against template

4. Compensates for invisible examples — Templates are visible even though EXAMPLE-NNN entries are not

Trade-off accepted: Adds ~200 tokens to agent instruction. Budget impact: ~6,163 + 200 = ~6,363 tokens, still under 10k headroom.

Implementation:

- Add template block under each "Entry Format" subsection

- Use `{{placeholder}}` syntax for variable parts

- Include all required fields with inline comments

#### Part 4: Relationship to Other Findings

Log/Loop | Relationship | Summary
--- | --- | ---
LOG-020 | Parent discovery | HTML comments invisible to grep-first agents
LOG-021 | Immediate predecessor | Migrated docs to agent instruction, but missed templates
LOOP-002 | Same root cause | EXAMPLE-NNN entries invisible to grep patterns
LOOP-003 | Sibling concern | How to prevent structural regression via CI

#### Part 5: Immediate Next Action

Task ID | Description | Priority
--- | --- | ---
TASK-PROTOCOL-DOCS-002 | Add executable templates to gsd-lite.md artifact sections | High

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-022 (Discovery: Missing executable templates in agent instruction)

→ Dependency chain: LOG-022 ← LOG-021 ← LOG-020 ← LOG-019

→ Next action: Implement TASK-PROTOCOL-DOCS-002 (add templates to gsd-lite.md)

Layer 2 — Global Context:

→ Architecture: Single source of truth at `src/gsd_lite/template/agents/gsd-lite.md`

→ Patterns: Grep-first file reading, 10k token budget, invisible EXAMPLE-NNN entries

→ Decision: DECISION-022a — embed executable templates

Fork paths:

- Execute template addition → TASK-PROTOCOL-DOCS-002

- Discuss CI framework → Continue LOOP-003

- Fix LOOP-003 metadata → Correct existing entry

### [LOG-023] - [EXEC] - Added Executable Templates to Agent Instruction - Task: PROTOCOL-DOCS-002

Timestamp: 2026-02-07 18:00

Status: COMPLETE

Depends On: LOG-022 (Discovery: Missing templates), DECISION-022a (Add templates to gsd-lite.md)

Context:

Per DECISION-022a, we added copy-paste-ready templates to the agent instruction so agents don't have to interpret abstract format descriptions.

Changes:

Section | What Was Added | Location
--- | --- | ---
`## WORK.md Structure` | Log Entry Template with field requirements | `src/gsd_lite/template/agents/gsd-lite.md:216-250`
`## INBOX.md Structure` | Loop Entry Template with field requirements | `src/gsd_lite/template/agents/gsd-lite.md:270-295`

Template Features:

- Copy-paste ready markdown blocks

- `{{placeholder}}` syntax for variable parts

- Field requirements table explaining each field

- Includes stateless handoff structure for logs

Impact:

- Agents can now copy template and fill placeholders

- All required fields are visible in template (no interpretation needed)

- Enables auditing: compare entry against template

- Token cost: ~200 tokens added, still well under 10k budget

Next: Continue LOOP-003 discussion (CI framework for structural regression prevention)

### [LOG-025] - [DECISION] - Housekeeping Tooling Spec: Upgrading Context Map for Agent Consumption (GSD-Lite Mode) - Task: TOOLING-002

Timestamp: 2026-02-07

Status: ✅ SPECIFICATION LOCKED

Depends On: LOG-015 (Context Map Tool), LOG-021 (Housekeeping Agent Vision)

Decision IDs: DECISION-025a (JSON Output), DECISION-025b (Semantic Signal Tiering)

#### Part 1: The Narrative — From Human-Readable to Agent-Native

The Context (Inlined):

- The Existing Tool (LOG-015): We have a script `analyze_context.py` that parses Markdown files using `markdown-it-py` and `tiktoken`. It currently generates a human-readable Markdown "map" of the file structure (headers, line numbers, token counts) to help agents navigate large files without reading them entirely.

- The New Requirement (LOG-021): We are building a "Housekeeping Agent" responsible for scanning session logs, inferring which decisions have been superseded (e.g., "Decision A replaced by Decision B"), and interviewing the user to confirm archival.

The Gap:

The Housekeeping Agent needs structured data (JSON) to infer relationships programmatically. The current tool only outputs unstructured Markdown text. This forces the agent to burn tokens parsing the very map intended to save tokens.

The Solution:

We are upgrading `analyze_context.py` to be a first-class GSD-Lite citizen. It will gain a `--gsd-lite` mode that:

1. Outputs machine-readable JSON.

2. Detects semantic signals (like strikethrough titles `Title` or `SUPERSEDED BY:` tags) deterministically using regex.

3. Handles the messy reality of Task naming (extracting `Task: MODEL-A` vs `Task: TASK-001`) without crashing.

The Architecture Shift:

    flowchart TD
        subgraph "Current State (Human-Readable)"
            A[WORK.md] -->|Markdown Parser| B(Markdown Map)
            B -->|Agent reads text| C{Agent Parsing}
            C -->|High Token Cost| D[Inference]
        end

        subgraph "New State (Machine-Readable)"
            E[WORK.md] -->|GSD-Lite Mode| F(JSON Object)
            F -->|Direct Load| G{Agent Logic}
            G -->|Zero Parsing Cost| H[Inference]

            subgraph "Tool Logic (Deterministic)"
                I[Task Extraction]
                J[Signal Detection]
                K[Token Counting]
            end

            I & J & K --> F
        end

        style B fill:#ffcccc,stroke:#333
        style F fill:#ccffcc,stroke:#333

#### Part 2: The Research — Semantic Signal Tiering

We investigated how to detect "supersession" (when one log makes another obsolete) without running an expensive LLM. The research revealed a Graduated Tier System:

Tier | Detection Method | Tool Responsibility | Why This Split
--- | --- | --- | ---
Tier 1 (HIGH) | Regex/keyword | `analyze_context.py` | Deterministic, zero ambiguity (e.g., `Title`)
Tier 2 (MEDIUM) | Pattern matching | `analyze_context.py` (flags) | Needs human context (e.g., `Depends On:`)
Tier 3 (LOW) | Semantic reasoning | Agent only | Requires deep understanding (e.g., "This approach failed")

Key Decision: The tool will FLAG Tier 1 and 2 signals but NEVER DECIDE. It provides evidence; the agent/user provides judgment.

#### Part 3: The Specification Packet (For Remote Builder Agent)

> Context for Builder: This spec defines the upgrade for `analyze_context.py`. The goal is to make it the deterministic "eyes" for the Housekeeping Agent. The agent is a specialized LLM workflow that reads session logs and archives old content.

##### 1. CLI Interface & Defaults

Command:

    # Default GSD-Lite Mode (targets gsd-lite/WORK.md, outputs JSON)
    analyze_context --gsd-lite --work --format json

    # Explicit Path
    analyze_context --gsd-lite --work ./custom/WORK.md

New Arguments:

- `--gsd-lite`: Enables GSD-specific parsing (Task IDs, Log IDs, Signals).

- `--work`: Sets default path to `gsd-lite/WORK.md` (or specific path if provided).

- `--format`: Options `md` (default), `json` (new requirement), `table`.

##### 2. JSON Output Schema (Requirement)

    {
      "summary": {
        "total_tokens": 65420,
        "total_logs": 24,
        "tier_1_flags": 3
      },
      "logs": [
        {
          "log_id": "LOG-018",
          "type": "DECISION",
          "task": "HOUSEKEEPING-AGENT",  // Extracted from header only
          "tokens": 1200,
          "lines": [3213, 3287],
          "signals": {
            "tier_1": ["strikethrough: ~~Pivot to Public Data~~"],
            "tier_2": ["depends_on: LOG-017", "pivot: pivoted"]
          }
        },
        {
          "log_id": "LOG-024",
          "type": "DECISION",
          "task": "PHASE-002",
          "tokens": 800,
          "lines": [3400, 3450],
          "signals": {
            "tier_1": [],
            "tier_2": []
          }
        }
      ]
    }

##### 3. Parsing Logic (Regex Specs)

A. Log Header Parsing

Goal: Permissive extraction. Do not enforce `TASK-NNN`. Capture `MODEL-A`.

Pattern: `^### \[LOG-(\d+)\]\s-\s\[([A-Z]+)\]\s-\s(.?)\s-\sTask:\s([A-Z][A-Za-z0-9_-]+)`

B. Semantic Signal Detection (The "Brain" Upgrade)

Goal: Detect supersession markers in header AND body.

Tier 1 Patterns (High Confidence - Auto-Flag):

    TIER_1_PATTERNS = {
        "strikethrough": r"~~[^~]+~~",                 # Title strikethrough
        "superseded_by": r"SUPERSEDED\s*BY[:\s]+LOG-\d+", # Explicit tag
        "deprecated_tag": r"\[DEPRECATED\]|\[OBSOLETE\]|\[ARCHIVED\]",
        "do_not_follow": r"[Dd]o\s*[Nn][Oo][Tt]\s*follow",
        "status_obsolete": r"[Ss]tatus[:\s]*(obsolete|deprecated|superseded|abandoned)",
        "killed": r"\b(killed|scrapped|abandoned|discarded)\b"
    }

Tier 2 Patterns (Medium Confidence - Flag for Review):

    TIER_2_PATTERNS = {
        "depends_on": r"[Dd]epends\s*[Oo]n[:\s]*(LOG-\d+)",
        "supersedes": r"\b(supersedes?|superseding)\b",
        "replaces": r"\b(replaces?|replacing)\b",
        "pivot": r"\b(pivot(ed|ing)?|pivotal)\b",
        "hit_wall": r"hit\s*(a\s*)?(wall|dead\s*end|roadblock)",
        "decided_not_to": r"decided\s*(not\s*to|against)",
        "options_evaluated": r"[Oo]ption\s*[A-Z1-9][:\s]"
    }

##### 4. Validation & Constraints

1. Header-Anchored Task ID: Only extract `Task:` from the log header line. Do NOT scan body for `Task:` to avoid false positives (e.g., "We discussed Task: AUTH").

2. No HEAD/TAIL in JSON: The JSON output should NOT include preview text (noise). Agents use `read_files(lines=[start, end])` for that.

3. Dependencies: Keep it lightweight. `re` (standard lib), `tiktoken`, `markdown-it-py`. No `spaCy` or NLP libraries.

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-025 (Decision + Spec for Tooling)

→ Dependency chain: LOG-025 ← LOG-015 (Tool) ← LOG-021 (Agent)

→ Next action: Implement the spec in `analyze_context.py` (or handoff to builder agent)

Layer 2 — Global Context:

→ Architecture: GSD-Lite tooling (LOG-015)

→ Patterns: Deterministic tool + Semantic Agent (LOG-018)

Fork paths:

- Build tool: `python3 analyze_context.py --gsd-lite` implementation

- Discuss: Refine regex patterns

### [LOG-026] - [DECISION] - The Quine Paradox: Fixing False Positives in Self-Referential Tooling Specs - Task: TOOLING-002

Timestamp: 2026-02-07

Status: ✅ SPECIFICATION AMENDED

Depends On: LOG-025 (Original Spec), LOG-015 (Context Map Tool)

Decision IDs: DECISION-026 (Context-Aware Exclusion Strategy)

#### Part 1: The Narrative — The "Quine" Problem

The Context (Inlined):

- The Spec (LOG-025): We defined a tool to detect "semantic signals" like `strikethrough titles` or `SUPERSEDED BY:` tags to identify obsolete logs.

- The Paradox: To write the spec, we had to write down the very patterns the tool is supposed to detect.

- The Result: The specification itself (LOG-025) is now flagged by the tool as "superseded" because it contains 4+ instances of the "superseded" pattern in its examples and regex definitions.

The Evidence (Grep Analysis):

We ran a grep on `WORK.md` and found the tool cannot distinguish between using a signal and mentioning it.

Line | Content | Type | Verdict
--- | --- | --- | ---
L3108 | `### [LOG-018] ... Pivot to Public Data ...` | Signal | ✅ TRUE POSITIVE (Real Supersession)
L4265 | `(like strikethrough titles Title or ...)` | Noise | ❌ FALSE POSITIVE (Documentation Example)
L4349 | `"tier_1": ["strikethrough: Pivot..."],` | Noise | ❌ FALSE POSITIVE (JSON Example in Spec)
L4381 | `r"SUPERSEDED\s*BY[:\s]+LOG-\d+"` | Noise | ❌ FALSE POSITIVE (Regex Definition)

> Concept: This is a "Quine Paradox" in static analysis — when the code (or documentation) describing a pattern contains the pattern itself.

#### Part 2: The Research — Exclusion Strategies

We researched how established static analysis tools (ESLint, Pylint, SonarQube) handle this "documentation vs. code" problem.

Strategy | Description | Pros | Cons
--- | --- | --- | ---
1. Structural Anchoring | Restrict matches to specific locations (e.g., "Only in Headers"). | Simple, fast. | Brittle. Misses signals in body text (e.g., "Status: Superseded").
2. Inline Suppression | Use comments to ignore lines (e.g., `// pylint: disable`). | Explicit control. | Pollutes docs ("noise"), requires maintenance.
3. Context-Aware Exclusion | parse syntax to identify "safe zones" (Code Blocks, Inline Code). | Robust, handles any content. | Higher complexity (requires parsing/masking).

The Decision (DECISION-026):

We will adopt a Hybrid Approach:

1. Context-Aware Exclusion (Primary): The tool MUST ignore all content inside Fenced Code Blocks (`` ``...```` ) and Inline Code (``` ...`` `).

2. Structural Anchoring (Secondary): Certain signals (like `Title`) are ONLY valid in specific locations (Header Lines).

#### Part 3: The Architecture — Masking Pipeline

We are upgrading the parsing logic from "Regex on Raw Text" to a "Mask -> Scan -> Unmask" pipeline.

    flowchart TD
        A[Raw Log Content] -->|Step 1: Mask Safe Zones| B(Masked Content)
        B -->|Step 2: Apply Regex Patterns| C{Signal Detection}
        C -->|Match Found| D[Record Signal]
        C -->|No Match| E[Ignore]

        subgraph "Safe Zones (Ignored)"
            F[Fenced Code Blocks]
            G[Inline Code `...`]
            H[Blockquotes > ...]
        end

        subgraph "Masking Logic"
            I[Replace Code with <PLACEHOLDER>]
            J[Keep Line Numbers Intact]
        end

        F & G & H --> I
        I --> B

#### Part 4: The Specification Amendment (Python Implementation)

> Requirement for Builder Agent: The `analyze_context.py` tool must implement this `detect_signals` logic to prevent false positives in documentation.

1. The Masking Logic (Python Pseudocode):

    import re

    def mask_exclusion_zones(text):
        """
        Replaces code blocks with placeholders to prevent regex matching on examples.
        Crucial: Preserves newlines so line numbers remain accurate.
        """
        placeholders = []

        def replacer(match):
            content = match.group(0)
            placeholders.append(content)
            # Replace with safe string, keeping newlines for line counts
            return f"__MASKED_CODE_BLOCK_{len(placeholders)-1}__" + ("\n" * content.count("\n"))

        # 1. Mask Fenced Code Blocks (```...```)
        # Pattern: Triple backticks, optional language, content, triple backticks
        text = re.sub(r"```[\s\S]*?```", replacer, text)

        # 2. Mask Inline Code (`...`)
        # Pattern: Single backtick, non-backtick content, single backtick
        text = re.sub(r"`[^`\n]+`", replacer, text)

        return text

2. The Detection Logic (Updated from LOG-025):

    def detect_signals(log_content):
        # Step 1: Mask the content
        masked_text = mask_exclusion_zones(log_content)
        lines = masked_text.split('\n')

        signals = []

        for i, line in enumerate(lines):
            # Step 2: Apply Anchored Patterns

            # A. Header-Only Signals (Strict)
            if line.startswith("### [LOG-"):
                if "~~" in line:  # Simple check first
                     if re.search(r"~~[^~]+~~", line):
                         signals.append({"line": i+1, "type": "strikethrough", "tier": 1})

                if "SUPERSEDED" in line:
                     if re.search(r"SUPERSEDED\s*BY[:\s]+LOG-\d+", line):
                         signals.append({"line": i+1, "type": "superseded_by", "tier": 1})

            # B. Body Signals (Permissive but Masked)
            # Note: 'line' here has code blocks replaced with __MASKED__
            if "[DEPRECATED]" in line or "[OBSOLETE]" in line:
                signals.append({"line": i+1, "type": "deprecated_tag", "tier": 1})

        return signals

3. Verification Case (The "Quine" Test):

Input Content | Masked View | Regex Result | Correct?
--- | --- | --- | ---
`### ... Title` | `### ... Title` | MATCH | ✅ YES
`Use strikethrough` | `Use` `__MASKED__` | NO MATCH | ✅ YES
`regex = r"~~"` | `regex =` `__MASKED__` | NO MATCH | ✅ YES

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-026 (Fixing False Positives in Spec)

→ Dependency chain: LOG-026 ← LOG-025 (Original Spec) ← LOG-015 (Tool)

→ Next action: Implement `analyze_context.py` with this masking logic.

Layer 2 — Global Context:

→ Architecture: GSD-Lite Tooling (LOG-015)

→ Patterns: Context-Aware Static Analysis (LOG-026)

Fork paths:

- Build tool: `python3 analyze_context.py --gsd-lite` with masking

- Discuss: Expand "Safe Zones" to Blockquotes?

### [LOG-027] - [EXEC] - Implemented Quine-Resistant Housekeeping Tool & Stateless Agent Router - Task: TOOLING-002

Timestamp: 2026-02-07

Status: ✅ COMPLETE

Depends On: LOG-026 (Quine Fix), LOG-025 (Tool Spec)

Decision IDs: DECISION-027 (Stateless Phase Detection)

#### Part 1: The Tool Implementation — Solving the Quine Paradox

We implemented the masking pipeline defined in LOG-026. The new tool `gsd_lite_analyzer.py` successfully masks code blocks and inline code before scanning for signals.

Key Implementation Detail: Newline-Preserving Masking

To ensure line numbers in the JSON output match the original file (critical for agent navigation), the masking logic preserves newlines inside masked blocks:

    # From src/fs_mcp/gsd_lite_analyzer.py
    def mask_exclusion_zones(text: str) -> tuple[str, list[str]]:
        """
        Replaces code blocks with placeholders.
        Crucially: Preserves newlines so line numbers remain accurate.
        """
        placeholders = []

        def create_placeholder(match: re.Match) -> str:
            content = match.group(0)
            placeholders.append(content)
            # Replace with safe string + original newlines
            placeholder = f"__MASKED_{len(placeholders)-1}__"
            newline_count = content.count("\n")
            return placeholder + ("\n" * newline_count)

        # 1. Mask Fenced Code Blocks (```...```)
        text = re.sub(r"```[\s\S]*?```", create_placeholder, text)

        # 2. Mask Inline Code (`...`)
        text = re.sub(r"`[^`\n]+`", create_placeholder, text)

        return text, placeholders

Verification Results (Dogfood Test):

We ran the tool on this very `WORK.md` file (which contains LOG-025 and LOG-026 with many "false positive" patterns).

Metric | Result | Meaning
--- | --- | ---
Total Logs | 29 | Correct
LOG-025 False Positives | 0 | ✅ Tool ignored the regex patterns in code blocks
LOG-026 False Positives | 0 | ✅ Tool ignored the examples in tables/code
LOG-018 Detection | Success | ✅ Correctly flagged `Pivot...` as Tier 1

#### Part 2: The Agent Instruction — Stateless Phase Router

We embedded the tool directly into `gsd-housekeeping.md` and implemented a Stateless Router that detects Phase 1 vs Phase 2 based on artifact state alone.

The Logic Flow:

    flowchart TD
        Start[User says "go"] --> Tool[Run analyze_gsd_work_log]
        Tool --> Check{Check Output}

        Check -->|Tier 1 Flags + NO Header Tags| P1[Phase 1: Inference]
        Check -->|Existing SUPERSEDED Tags| P2[Phase 2: Archival]
        Check -->|No Flags/Tags| Clean[Report Clean]

        P1 --> Interview[Interview & Write Tags]
        P2 --> Archive[Confirm & Archive]

The Implementation (in `gsd-housekeeping.md`):

    ## Session Start (Stateless Router)

    **User says "go" → I detect phase from artifact state:**

    1. **Read PROJECT.md** — Get domain vocabulary
    2. **Run `analyze_gsd_work_log("gsd-lite/WORK.md")`** — Get signal analysis
    3. **Detect phase from tool output:**

    | Condition | Phase | Action |
    |-----------|-------|--------|
    | Tier 1 flags exist, NO `SUPERSEDED BY:` tags in headers | **Phase 1** | Interview → Write tags |
    | `SUPERSEDED BY:` tags already in headers | **Phase 2** | Confirm → Archive |
    | No flags, no tags | **Clean** | Report "Nothing to housekeep" |

Why this matters: A user can spin up the housekeeping agent at any point. If they ran Phase 1 yesterday (tags written) and resume today, the agent automatically recognizes Phase 2 is needed. No "handoff memory" required — the state is in the artifact.

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-027 (Implementation & Validation)

→ Dependency chain: LOG-027 ← LOG-026 (Quine Fix) ← LOG-025 (Tool Spec)

→ Next action: Use the housekeeping agent to clean up superseded logs (Phase 1).

Layer 2 — Global Context:

→ Architecture: GSD-Lite Tooling (LOG-015)

→ Patterns: Stateless Router (LOG-027), Quine-Resistant Analysis (LOG-026)

Fork paths:

- Run Housekeeping: `gsd-housekeeping.md` workflow

- Execute Task: Pick up next item from `parked_tasks`

### [LOG-028] - [DISCOVERY] - CI Framework Design: Constitutional Knowledge for Regression Prevention - Task: CI-FRAMEWORK-001

Timestamp: 2026-02-08

Status: IN_PROGRESS → CHECKPOINT

Depends On: LOG-020 (10k token budget as CI gate), LOG-016 (Stateless-First Architecture), LOG-017 (Housekeeping Agent vision)

Decision IDs: DECISION-028a (Constitution-first approach), DECISION-028b (Three-layer CI architecture)

#### Part 1: The Problem — 12 PRs with Zero Automated Checks

GSD-Lite has evolved through 12 closed PRs with no regression protection. Every change relied on "trust me bro" discussions between human and agent across gsd-lite sessions.

Why this worked initially: Everything was experimental. The framework was finding its shape.

Why it's now a problem: The user has grown to depend on GSD-Lite across multiple production projects. The framework's complexity now exceeds one person's mental model:

Component | Count | Purpose
--- | --- | ---
Agents | 2 | `gsd-lite.md` (root), `gsd-housekeeping.md` (context gardener)
Workflows | 6 | discuss, execution, checkpoint, map-codebase, new-project, progress
Artifacts | 5 | WORK.md, INBOX.md, HISTORY.md, PROJECT.md, ARCHITECTURE.md
References | 1 | questioning.md (Socratic pair programming DNA)

The recursive problem: To discuss a new iteration, the user needs a gsd-lite agent to consume the context. Control has drifted to the agent itself — violating the "Driver owns, Navigator proposes" philosophy.

Source: User braindump session 2026-02-08, GitHub repo scan of luutuankiet/gsd-lite (12 closed PRs at time of writing).

#### Part 2: The Solution — Constitutional Knowledge

Instead of trying to test every possible agent behavior, we distill GSD-Lite's immutable pillars into a machine-auditable document: the Constitution.

The Constitution Concept:

    graph TD
        subgraph "What Can Change"
            W[Workflows]
            E[Examples]
            A[Artifact Templates]
        end

        subgraph "What Cannot Change"
            C[Constitution<br/>~500 lines<br/>Immutable Pillars]
        end

        W -->|Must comply with| C
        E -->|Must comply with| C
        A -->|Must comply with| C

        subgraph "CI Pipeline"
            L1[L1: Structural<br/>grep patterns]
            L2[L2: Constitutional<br/>LLM-as-judge]
            L3[L3: Behavioral<br/>Golden tests]
        end

        C --> L2
        L1 --> L2 --> L3

Why Constitution-first:

- Single source of truth for "what makes GSD-Lite GSD-Lite"

- LLM-as-judge can evaluate any artifact against it

- Human-readable contract for contributors

- Decouples philosophy (stable) from implementation (evolving)

Decision: DECISION-028a — Adopt Constitution-first approach for CI. All other artifacts can drift as long as they don't violate the Constitution.

#### Part 3: The Six Pillars (Draft Constitution)

Based on braindump synthesis and echo-back verification with user:

##### Pillar 1: Stateless-First

> Every agent turn generates a handoff packet enabling zero-context resume.

Test: Structural grep for `📦 STATELESS HANDOFF` with required components:

- Layer 1 (Local Context) — task dependency chain

- Layer 2 (Global Context) — from Key Events Index

- Fork paths — minimum 2 (continue + pivot)

Gate: Hard fail if missing from agent response template.

Backlink: See LOG-016 (Stateless-First Architecture) for the full specification and Two-Layer Handoff Structure.

##### Pillar 2: Context Engineering

> Optimize token budget: ~10k static (agent + workflow), ~10-20k JIT (artifacts), ~30k first turn, ~80k session ceiling.

Tests:

1. Token count on `agents/*.md` + loaded workflow — soft warn if exceeds 10k

2. Dead code detection: examples in artifact templates that agents never grep

The Artifact Template Question: Current artifact templates (e.g., `template/WORK.md`) contain HTML comments and examples. Given agents use grep-first strategy and enter via `gsd-lite.md`, do they ever read these examples?

Proposed test: Run golden scenarios, trace which template sections are accessed. If examples contribute nothing to agent performance, decommission them for context optimization.

Gate: Soft warn, human reviews.

Backlink: See LOG-020 (The Invisible Documentation Problem) for the 10k token budget decision and context arithmetic.

##### Pillar 3: Pair Programming Model

> Agent = Navigator (proposes, challenges, teaches). User = Driver (decides, owns). Never auto-execute.

Behavioral markers:

- Grounding Loop: Search → Echo → Verify → Execute (never skip Echo/Verify)

- Challenge Tone: Agent probes vague answers using protocol from `references/questioning.md`

- No auto-writing: Agent asks "Want me to capture this?" before artifact writes

Example (what violation looks like):

    ❌ VIOLATION: Eager Executor
    User: "Add dark mode"
    Agent: *immediately starts implementing*

    ✅ COMPLIANT: Grounding Loop
    User: "Add dark mode"
    Agent: "Why dark mode? User preference? Accessibility? Battery saving?
            This affects the approach. [YOUR TURN]"

Gate: LLM-as-judge rubric against Constitution.

##### Pillar 4: Journalism Quality (with DRY Backlinks)

> Logs include narrative framing, code snippets, analogies — but stay DRY via one-liner backlinks.

The bloat problem: If each log is standalone journalism-style without backlinking, WORK.md becomes bloated with repeated context.

Solution: Logs should include one-liner backlinks to prior logs instead of repeating context:

    ✅ DRY Pattern:
    **Depends On:** LOG-020 (10k token budget decision)
    See LOG-016 (Stateless-First) for the Two-Layer Handoff specification.

    ❌ Bloated Pattern:
    The token budget decision from our earlier discussion established that
    agents should stay under 10k static tokens because [repeats 500 words
    from LOG-020]...

Rubric for LLM-as-judge:

- [ ] Has narrative framing (not just bullet points)?

- [ ] Includes WHY, not just WHAT?

- [ ] Code snippets where implementation matters?

- [ ] Uses backlinks instead of repeating context?

- [ ] Standalone readable at the summary level?

Gate: LLM-as-judge with scoring threshold.

##### Pillar 5: Template Example Coverage

> Examples in artifact templates must be exercised by agents, or decommissioned.

Clarification (user correction): This pillar is about examples/comments in artifact templates (e.g., `template/WORK.md`, `template/INBOX.md`), NOT agent instruction files.

The hypothesis: Agents enter via `gsd-lite.md` and use grep-first strategy. They may never read the HTML comments and examples in artifact templates because those patterns don't appear in grep results.

Test design:

1. Run golden test scenarios

2. Track which artifact template sections agents access

3. If examples are never accessed AND removing them doesn't degrade agent performance → decommission

This is context engineering optimization — every token saved in templates is headroom for actual work.

Gate: Soft warn for unused examples, recommend decommission.

##### Pillar 6: Behavioral Alignment

> Agent follows Universal Onboarding, respects mode routing, produces expected outputs for canonical scenarios.

The trajectory testing challenge: GSD-Lite is a vanilla markdown framework. Users spawn agents via OpenCode — we don't collect telemetry or traces.

Proposed approach (per Google ADK eval pattern):

1. Define golden test set: input scenarios with expected trajectory assertions

2. Run agent against scenarios, capture outputs

3. LLM-as-judge evaluates: did agent follow expected path?

Key research needed: How does Google ADK handle trajectory testing without otel? Their eval framework suggests assertions on agent behavior, not just final output.

Sources for research:

- Google ADK Evaluate Docs — trajectory evaluation patterns

- Google Cloud ADK Eval Video — practical walkthrough

- HuggingFace Upskill — skill generation + eval framework

Gate: Pass rate threshold on golden tests.

#### Part 4: Three-Layer CI Architecture

Decision: DECISION-028b — Implement CI in three layers, ordered by cost:

    graph TD
        subgraph "Layer 1: Structural - Free"
            L1A[Token budget check<br/>agents less than 10k tokens]
            L1B[Handoff grep<br/>STATELESS HANDOFF present]
            L1C[Orphan detection<br/>no dead artifact refs]
        end

        subgraph "Layer 2: Constitutional - ~50k tokens"
            L2A[LLM-as-judge<br/>evaluates against Constitution]
            L2B[Drift detection<br/>flags philosophy violations]
        end

        subgraph "Layer 3: Behavioral - ~500k-1M tokens"
            L3A[Golden test scenarios]
            L3B[Trajectory assertions]
            L3C[Journalism quality rubric]
        end

        PR[PR Opened] --> L1A
        L1A --> L1B --> L1C
        L1C -->|Pass| L2A
        L2A --> L2B
        L2B -->|Pass| L3A
        L3A --> L3B --> L3C
        L3C -->|Pass| Merge[Ready to Merge]

        L1C -->|Fail| Block1[Hard Block]
        L2B -->|Fail| Review[Human Review]
        L3C -->|Fail| Review

Budget allocation (per PR, ~1M tokens max):

Layer | Token Cost | What It Catches
--- | --- | ---
L1 | ~0 | Obvious structural breaks (missing handoff, over budget)
L2 | ~50k | Philosophy drift (violates Constitution pillars)
L3 | ~500k-1M | Behavioral regression (agent doesn't follow expected path)

#### Part 5: Open Research Questions

Before implementation, these need grounding:

ID | Question | Why It Matters | Research Target
--- | --- | --- | ---
RQ-1 | Trajectory testing without telemetry | We can't trace tool calls in vanilla gsd-lite | Google ADK eval docs, Upskill test format
RQ-2 | Constitution format | JSON schema? Markdown rubric? Executable assertions? | Industry meta-prompting frameworks
RQ-3 | SKILLS.md vs current architecture | Is Upskill's skill pattern additive or premature for gsd-lite? | Maturity assessment needed
RQ-4 | Golden test set design | What scenarios cover the 6 pillars? How many per pillar? | Eval dataset design patterns
RQ-5 | LLM-as-judge reliability | Which model? How to calibrate? False positive rates? | Agent eval benchmarks

User guidance on RQ-3: Focus on maturing current architecture with proper checks and Constitution before adopting new patterns like SKILLS.md. Avoid reinventing wheels or jumping to next hype prematurely.

#### Part 6: Scoping Decisions

Distribution scope: OpenCode agents only. Copy-paste compatibility adds friction without clear benefit for CI purposes.

Primary goal: Catch regressions before merge (not drift detection over time).

Budget constraint: ~1M tokens per PR (personal project).

#### Part 7: Proposed Next Steps

Step | Description | Depends On
--- | --- | ---
Step 1 | Distill Constitution v0.1 from current agents/workflows | This log (LOG-028)
Step 2 | Research trajectory testing patterns (Google ADK, Upskill) | RQ-1
Step 3 | Design golden test set (3-5 scenarios per pillar) | Step 1, Step 2
Step 4 | Implement L1 (structural checks) | Step 1
Step 5 | Implement L2 (constitutional LLM-as-judge) | Step 1, Step 4
Step 6 | Implement L3 (behavioral golden tests) | Step 2, Step 3

#### Part 8: Summary Table — The Six Pillars

Pillar | What | Test Type | Gate | Backlink
--- | --- | --- | --- | ---
Stateless-First | Handoff packet every turn | Structural (grep) | Hard | LOG-016
Context Engineering | Token budgets, dead code | Structural + Coverage | Soft | LOG-020
Pair Programming | Grounding loop, challenge tone | Constitutional (LLM) | Soft | —
Journalism Quality | Log rubric with DRY backlinks | Constitutional (LLM) | Soft | —
Template Example Coverage | Decommission unused examples | Coverage test | Soft | —
Behavioral Alignment | Golden test trajectories | Behavioral (LLM) | Threshold | —

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-028 (CI Framework Discovery — checkpointed)

→ Dependency chain: LOG-028 ← LOG-020 (token budget) ← LOG-016 (stateless-first)

→ Next action: Research trajectory testing (RQ-1) OR distill Constitution v0.1 (Step 1)

Layer 2 — Global Context:

→ Architecture: 2 agents + 6 workflows + 5 artifacts (see Part 1 table)

→ Patterns: Constitution-first CI, Three-layer architecture (L1 structural → L2 constitutional → L3 behavioral)

→ Key decisions: DECISION-028a (Constitution approach), DECISION-028b (Three-layer CI)

Fork paths:

- Continue to Constitution v0.1 → Distill immutable pillars from current agents/workflows

- Research trajectory testing → Deep dive Google ADK + Upskill for RQ-1

- Implement L1 first → Start with cheapest structural checks

- Discuss further → Refine pillars or research questions

### [LOG-029] - [PLAN] - Constitution v0.1 Implementation Plan: Hybrid Format with Pillars + Rubrics + Golden Tests - Task: TASK-CONST-001

Timestamp: 2026-02-08

Status: PLAN (pending approval)

Depends On: LOG-028 (Six Pillars + Three-Layer CI architecture)

Decision IDs: DECISION-029a (Hybrid constitution format)

#### Part 1: Research Findings — Three Industry Approaches

Before designing the Constitution format, I researched how industry leaders structure machine-auditable behavioral contracts.

##### Finding 1: Anthropic Constitutional AI (Jan 2026)

Anthropic published an 80-page "reason-based" constitution that prioritizes explaining WHY over prescribing WHAT.

Key structural patterns:

Pattern | Description | GSD-Lite Applicability
--- | --- | ---
4-tier priority | Safety → Ethics → Guidelines → Helpfulness | Adapt for: Stateless → Pair Programming → Context → Journalism
Hardcoded vs soft-coded | Absolute prohibitions vs adjustable defaults | Map to: Hard gates (L1) vs soft gates (L2)
Principal hierarchy | Anthropic → Operators → Users | Map to: Constitution → Agent → User
Reason-based | Each rule includes WHY, not just WHAT | Critical for LLM-as-judge context

Source: Anthropic Claude's Constitution — published 2026-01-22.

##### Finding 2: LLM-as-Judge Rubric Format

Industry standard for LLM evaluation uses structured YAML/JSON rubrics with 4 components:

    # Example rubric structure (synthesized from DeepEval, Promptfoo, LangChain patterns)
    evaluation_criteria:
      grounding_loop:
        description: "Agent echoes findings and verifies with user before executing"
        scale: "binary"
        labels: ["Compliant", "Violation"]
        examples:
          compliant: |
            Agent: "I found X in file Y at line Z. Does this match your intent?"
            User: "Yes"
            Agent: "Based on this, my plan is..."
          violation: |
            Agent: "I found X. [immediately starts implementing]"

Source: Promptfoo llm-rubric docs, DeepEval GEval.

##### Finding 3: Google ADK Evaluation Format

Google ADK uses `.test.json` files for trajectory testing:

    {
      "eval_id": "onboarding_sequence",
      "conversation": [
        {
          "user_content": { "text": "let's work on the auth feature" },
          "intermediate_data": {
            "tool_uses": [
              { "name": "read_files", "args": { "path": "PROJECT.md" } },
              { "name": "read_files", "args": { "path": "ARCHITECTURE.md" } },
              { "name": "grep_content", "args": { "pattern": "^## " } }
            ]
          },
          "final_response": {
            "text": "I've onboarded via PROJECT.md and ARCHITECTURE.md..."
          }
        }
      ]
    }

Source: Google ADK Evaluate Docs, adk-python GitHub.

#### Part 2: The Hybrid Format Decision

DECISION-029a: Adopt a three-layer hybrid format that separates philosophy (human-readable), evaluation criteria (machine-parseable), and behavioral tests (trajectory assertions).

    graph TD
        subgraph "Constitution v0.1 Structure"
            C[CONSTITUTION.md<br/>~500 lines<br/>Pillars + WHY]

            subgraph "rubrics/"
                R1[pair-programming.yaml]
                R2[journalism-quality.yaml]
                R3[stateless-handoff.yaml]
            end

            subgraph "golden-tests/"
                G1[onboarding.test.json]
                G2[grounding-loop.test.json]
                G3[scope-discipline.test.json]
            end
        end

        subgraph "CI Layers"
            L1[L1: Structural<br/>grep patterns]
            L2[L2: Constitutional<br/>LLM-as-judge + rubrics]
            L3[L3: Behavioral<br/>Golden test trajectories]
        end

        C --> L2
        R1 --> L2
        R2 --> L2
        R3 --> L2
        G1 --> L3
        G2 --> L3
        G3 --> L3

Why hybrid over single-file:

Approach | Pros | Cons
--- | --- | ---
Single markdown | Simple, human-readable | Can't programmatically validate
Single JSON schema | Machine-parseable | Loses the WHY, hard to maintain
Hybrid (chosen) | Best of both: philosophy + automation | Slightly more files to maintain

Why this matches GSD-Lite philosophy: The Constitution itself follows the "journalism quality" pillar — rich context for humans, structured data for machines.

#### Part 3: File-by-File Implementation Plan

##### 3.1 CONSTITUTION.md — The Pillars (~500 lines)

Location: `src/gsd_lite/template/constitution/CONSTITUTION.md`

Structure:

    # GSD-Lite Constitution v0.1

    ## Preamble
    [Why this document exists, how to use it]

    ## Priority Hierarchy
    1. Stateless-First (session survival)
    2. Pair Programming Model (human ownership)
    3. Context Engineering (token discipline)
    4. Journalism Quality (onboarding optimization)

    ## Pillar 1: Stateless-First
    ### The Principle
    [WHY: Users micro-fork to manage context rot...]

    ### Hardcoded Behaviors
    - MUST: End every response with 📦 STATELESS HANDOFF
    - MUST: Include Layer 1 (local) and Layer 2 (global) context
    - MUST: Provide 2-4 fork paths

    ### Soft-coded Defaults
    - SHOULD: Use dependency chain format (LOG-XXX ← LOG-YYY)
    - SHOULD: Keep handoff under 20 lines

    ### Violation Examples
    [Concrete examples of what NOT to do]

    ### Compliance Examples
    [Concrete examples of correct behavior]

    ## Pillar 2: Pair Programming Model
    [Same structure...]

    ## Pillar 3: Context Engineering
    [Same structure...]

    ## Pillar 4: Journalism Quality
    [Same structure...]

    ## Appendix: Backlinks to Source
    - Pillar 1: See LOG-016 (Stateless-First Architecture)
    - Pillar 2: See gsd-lite.md "Questioning Philosophy" section
    - Pillar 3: See LOG-020 (10k token budget decision)
    - Pillar 4: See PROJECT.md "The Logging Standard"

Extraction source mapping:

Pillar | Primary Source | Lines
--- | --- | ---
Stateless-First | `gsd-lite.md` | L168-250 (Stateless-First Architecture section)
Pair Programming | `gsd-lite.md` | L252-350 (Questioning Philosophy section)
Context Engineering | `gsd-lite.md` | L85-120 (File Reading Strategy section)
Journalism Quality | `gsd-lite/PROJECT.md` | L45-70 (The Logging Standard section)

##### 3.2 Rubrics Directory — L2 Evaluation Criteria

Location: `src/gsd_lite/template/constitution/rubrics/`

Example: `pair-programming.yaml`

    # Rubric: Pair Programming Model Compliance
    # Used by: L2 Constitutional CI (LLM-as-judge)
    # Backlink: LOG-029 (Constitution Implementation Plan)

    rubric_id: pair-programming-v1
    pillar: 2
    description: |
      Agent operates as Navigator (proposes, challenges, teaches) while
      User remains Driver (decides, owns). Never auto-execute without
      explicit user approval.

    criteria:
      - id: grounding_loop
        description: "Agent follows Search → Echo → Verify → Execute pattern"
        scale: binary
        labels: ["Compliant", "Violation"]
        weight: 0.3
        examples:
          compliant: |
            Agent: "I searched for auth patterns. Found JWT setup in src/auth.ts at line 45.
                   It uses RS256 algorithm. Does this match your expectation?"
            User: "Yes, that's correct."
            Agent: "Based on this, my plan is to add refresh token logic. Approve?"
          violation: |
            Agent: "I found the auth file. Here's the implementation: [code]"
            # Skipped Echo and Verify steps

      - id: challenge_vagueness
        description: "Agent probes vague answers instead of accepting them"
        scale: binary
        labels: ["Compliant", "Violation"]
        weight: 0.2
        examples:
          compliant: |
            User: "Make it fast"
            Agent: "Fast how? Sub-second response? Handles large datasets? Quick to build?"
          violation: |
            User: "Make it fast"
            Agent: "I'll optimize the code for performance." # Accepted vague answer

      - id: no_auto_write
        description: "Agent asks before writing to artifacts"
        scale: binary
        labels: ["Compliant", "Violation"]
        weight: 0.25
        examples:
          compliant: |
            Agent: "This is a key decision. Want me to capture it to WORK.md?"
            User: "Yes, log it."
          violation: |
            Agent: "I've updated WORK.md with our decision." # Wrote without asking

      - id: user_owns_completion
        description: "Agent signals readiness, user decides completion"
        scale: binary
        labels: ["Compliant", "Violation"]
        weight: 0.25
        examples:
          compliant: |
            Agent: "TASK-001 implementation complete. Ready for your review."
            User: "Approved, mark complete."
          violation: |
            Agent: "TASK-001 is done. Moving to TASK-002." # Decided completion

    pass_threshold: 0.8
    judge_model: "claude-sonnet-4-20250514"

Other rubric files to create:

File | Pillar | Key Criteria
--- | --- | ---
`stateless-handoff.yaml` | 1 | Handoff presence, L1/L2 layers, fork paths
`context-engineering.yaml` | 3 | Grep-first behavior, token awareness
`journalism-quality.yaml` | 4 | Narrative framing, WHY not just WHAT, code snippets, backlinks

##### 3.3 Golden Tests Directory — L3 Behavioral Trajectories

Location: `src/gsd_lite/template/constitution/golden-tests/`

Example: `onboarding.test.json`

    {
      "eval_set_id": "gsd-lite-onboarding",
      "name": "Universal Onboarding Sequence",
      "description": "Validates agent follows PROTOCOL → PROJECT → ARCHITECTURE → WORK.md boot sequence",
      "backlink": "LOG-029, Pillar 5 (Universal Onboarding)",
      "eval_cases": [
        {
          "eval_id": "fresh_session_start",
          "scenario": "User starts fresh session with direct task request",
          "conversation": [
            {
              "invocation_id": "turn_1",
              "user_content": {
                "role": "user",
                "parts": [{ "text": "let's work on the auth feature" }]
              },
              "intermediate_data": {
                "tool_uses": [
                  { "name": "read_files", "args_contains": "PROJECT.md" },
                  { "name": "read_files", "args_contains": "ARCHITECTURE.md" },
                  { "name": "grep_content", "args_contains": "WORK.md" }
                ],
                "tool_order": "sequential"
              },
              "final_response_must_contain": [
                "PROJECT.md",
                "ARCHITECTURE.md",
                "Current Understanding"
              ],
              "final_response_must_not_contain": [
                "Let me start implementing",
                "Here's the code"
              ]
            }
          ]
        }
      ]
    }

Other golden test files to create:

File | Tests | Key Assertions
--- | --- | ---
`grounding-loop.test.json` | Search → Echo → Verify | Tool use before action, echo findings
`scope-discipline.test.json` | INBOX capture | Defers scope creep to INBOX.md
`checkpoint.test.json` | Session handoff | Updates Current Understanding correctly

#### Part 4: Implementation Sequence

    gantt
        title Constitution v0.1 Implementation
        dateFormat  YYYY-MM-DD
        section Phase 1: Pillars
        Extract from gsd-lite.md     :p1a, 2026-02-09, 1d
        Write CONSTITUTION.md        :p1b, after p1a, 1d
        section Phase 2: Rubrics
        pair-programming.yaml        :p2a, after p1b, 1d
        stateless-handoff.yaml       :p2b, after p2a, 1d
        journalism-quality.yaml      :p2c, after p2b, 1d
        section Phase 3: Golden Tests
        onboarding.test.json         :p3a, after p2c, 1d
        grounding-loop.test.json     :p3b, after p3a, 1d
        section Phase 4: CI Integration
        L1 structural checks         :p4a, after p3b, 1d
        L2 LLM-as-judge scaffold     :p4b, after p4a, 2d

Task breakdown:

ID | Task | Depends On | Est. Tokens | Output
--- | --- | --- | --- | ---
TASK-CONST-001a | Extract Pillar 1-4 from source files | LOG-029 | ~50k | Draft pillars
TASK-CONST-001b | Write CONSTITUTION.md with examples | TASK-CONST-001a | ~30k | CONSTITUTION.md
TASK-CONST-002a | Write pair-programming.yaml | TASK-CONST-001b | ~20k | rubrics/ file
TASK-CONST-002b | Write remaining rubrics | TASK-CONST-002a | ~40k | 3 more rubrics
TASK-CONST-003a | Write onboarding.test.json | TASK-CONST-002b | ~20k | golden-tests/ file
TASK-CONST-003b | Write remaining golden tests | TASK-CONST-003a | ~40k | 3 more tests
TASK-CI-L1-001 | Implement L1 structural checks | TASK-CONST-001b | ~30k | CI script
TASK-CI-L2-001 | Scaffold L2 LLM-as-judge | TASK-CONST-002b | ~50k | CI integration

#### Part 5: Sources and Citations

Finding | Source | Specific Location
--- | --- | ---
Anthropic 4-tier priority | anthropic.com/research/claudes-constitution | Section "Priority Hierarchy"
Anthropic hardcoded/soft-coded | Same | Section "Hardcoded and Softcoded Behaviors"
LLM-as-judge rubric structure | promptfoo.dev llm-rubric | "Defining custom rubrics" section
DeepEval GEval pattern | confident-ai.com GEval | "Custom Criteria" section
Google ADK test.json format | google.github.io/adk-docs/evaluate | "Test Datasets" section
Google ADK trajectory testing | github.com/google/adk-python | `examples/eval/` directory

GSD-Lite source file references:

Content | File | Lines
--- | --- | ---
Stateless-First Architecture | `src/gsd_lite/template/agents/gsd-lite.md` | 168-250
Questioning Philosophy | `src/gsd_lite/template/agents/gsd-lite.md` | 252-350
Grounding Loop | `src/gsd_lite/template/agents/gsd-lite.md` | 320-340
File Reading Strategy | `src/gsd_lite/template/agents/gsd-lite.md` | 85-120
Logging Standard | `gsd-lite/PROJECT.md` | 45-70

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-029 (Constitution v0.1 Implementation Plan)

→ Dependency chain: LOG-029 ← LOG-028 (Six Pillars) ← LOG-020 (token budget)

→ Next action: Approve plan, then begin TASK-CONST-001a (extract pillars from source)

Layer 2 — Global Context:

→ Architecture: 2 agents + 6 workflows + Constitution (new)

→ Patterns: Hybrid format (Markdown pillars + YAML rubrics + JSON golden tests)

→ Key decisions: DECISION-029a (Hybrid constitution format)

Fork paths:

- Approve plan → Begin TASK-CONST-001a (pillar extraction)

- Adjust plan → Discuss changes to format or scope

- Research more → Deep dive specific rubric patterns

- Defer → Checkpoint and resume later

### [LOG-030] - [EXEC] - Created CONSTITUTION.md v0.1 with Four Pillars Extracted from Source - Task: TASK-CONST-001a

Timestamp: 2026-02-08

Status: Complete

Depends On: LOG-029 (Constitution Implementation Plan)

Output: `src/gsd_lite/template/constitution/CONSTITUTION.md` (~550 lines)

#### What Was Done

Extracted the four pillars from source files and compiled into a machine-auditable constitution:

Pillar | Source Files | Key Sections Extracted
--- | --- | ---
Stateless-First | `agents/gsd-lite.md` L356-466 | Handoff structure, two-layer format, anti-patterns
Pair Programming | `agents/gsd-lite.md` L514-660, `references/questioning.md` | Grounding Loop, Challenge Tone Protocol, Teaching Detour
Context Engineering | `agents/gsd-lite.md` L121-172, L33-53 | Grep-first pattern, Universal Onboarding sequence
Journalism Quality | `gsd-lite/PROJECT.md` L81-115 | Logging Standard, log entry elements table

#### Structure of CONSTITUTION.md

    # GSD-Lite Constitution v0.1
    ├── Preamble (why this exists, priority hierarchy)
    ├── Pillar 1: Stateless-First
    │   ├── The Principle (WHY)
    │   ├── Hardcoded Behaviors (S1-H1 through S1-H5)
    │   ├── Soft-coded Defaults (S1-S1 through S1-S3)
    │   ├── Two-Layer Handoff Structure
    │   ├── Canonical Format
    │   ├── Violation Examples
    │   ├── Compliance Examples
    │   └── Source Reference
    ├── Pillar 2: Pair Programming Model
    │   ├── [same structure]
    │   └── Includes: Grounding Loop, Challenge Tone Protocol, Teaching Detour
    ├── Pillar 3: Context Engineering
    │   ├── [same structure]
    │   └── Includes: Grep-First Pattern, Universal Onboarding Sequence
    ├── Pillar 4: Journalism Quality
    │   ├── [same structure]
    │   └── Includes: Log Entry Elements, Milestone/Standard formats
    └── Appendix: Quick Reference
        ├── Golden Rules (spans all pillars)
        ├── Behavior ID Index
        └── Source File Mapping

#### Key Design Decisions

1. Behavior IDs: Each hardcoded/soft-coded rule gets a unique ID (e.g., `S1-H1`, `P2-H3`) for CI traceability

2. Violation + Compliance Examples: Every pillar includes concrete examples of both wrong and right behavior

3. Source References: Each pillar backlinks to exact source file + line numbers

4. Priority Hierarchy: Explicit ordering when pillars conflict (Stateless > Pair Programming > Context > Journalism)

#### Files Created

File | Size | Purpose
--- | --- | ---
`src/gsd_lite/template/constitution/CONSTITUTION.md` | ~550 lines | The four pillars with examples

#### Next Steps

Ready for TASK-CONST-001b (review/polish) or skip to TASK-CONST-002a (write rubrics).

### [LOG-031] - [EXEC] - Created pair-programming.yaml Rubric with Hardcoded Behaviors (P2-H1 to P2-H5) - Task: TASK-CONST-002a

Timestamp: 2026-02-08

Status: Complete

Depends On: LOG-030 (CONSTITUTION.md), LOG-028 (CI Framework Design)

Output: `src/gsd_lite/template/constitution/rubrics/pair-programming.yaml` (~350 lines)

#### Research Summary

Evaluated three major LLM evaluation frameworks to determine cutting-edge rubric format:

Framework | Format | Key Pattern | Verdict
--- | --- | --- | ---
Vertex AI Gen AI Eval | Python SDK | `PointwiseMetricPromptTemplate` with criteria + rating_rubric | GCP-coupled, no native YAML
Promptfoo | Native YAML | `llm-rubric` assertion with freeform text | True YAML-first, but unstructured
DeepEval | Python SDK | `GEval` with `Rubric` class (explicit score ranges) | Most structured, Python-first

Key Insight: All frameworks converge on same core pattern: `criteria` + `rating_rubric` + `evaluation_params`. Vertex AI recommends "Adaptive Rubrics" (dynamically generated per prompt) as cutting-edge.

Decision: Hybrid YAML format that:

1. Is native YAML (like Promptfoo) — rubrics live in `.yaml` files

2. Has structured scoring (like DeepEval's `Rubric`) — explicit score ranges per criterion

3. Maps directly to CONSTITUTION.md — each behavior ID becomes a criterion

4. Is portable — can transform to any target framework

#### Structure of pair-programming.yaml

    metadata:
      pillar: "Pair Programming Model"
      pillar_id: "P2"
      scope: "hardcoded"  # P2-H1 through P2-H5

    evaluation_params:
      - agent_response
      - user_input
      - conversation_history (optional)
      - artifacts_state (optional)

    criteria:
      - id: P2-H1 (Why Before How)
      - id: P2-H2 (No Auto-Write)
      - id: P2-H3 (User Owns Completion)
      - id: P2-H4 (Grounding Loop)
      - id: P2-H5 (Challenge Vagueness)

    aggregation:
      method: "minimum"  # ANY violation = fail

#### Each Criterion Contains

Field | Purpose | Example
--- | --- | ---
`id` | Traceability to CONSTITUTION.md | `P2-H1`
`name` | Human-readable title | "Why Before How"
`type` | `hardcoded` or `softcoded` | `hardcoded`
`description` | What the rule means | Full description
`evaluation_steps` | Step-by-step guide for LLM judge | 1. Check if action... 2. If yes, verify...
`scoring` | Binary (0=violation, 1=pass) | Explicit criteria for each score
`violation_examples` | Concrete bad behavior | Input → Response → Reason
`compliance_examples` | Concrete good behavior | Input → Response → Reason

#### Files Created

File | Size | Purpose
--- | --- | ---
`src/gsd_lite/template/constitution/rubrics/` | (dir) | New rubrics directory
`.../rubrics/pair-programming.yaml` | ~350 lines | Pillar 2 hardcoded behaviors

#### Design Rationale

1. Binary scoring for hardcoded: 0 (violation) or 1 (pass) — no grey area for non-negotiables

2. Minimum aggregation: If ANY hardcoded rule fails, the entire pillar fails

3. Rich examples: Both violation and compliance examples with reasoning, enabling weaker models to pattern-match

4. Portable format: YAML structure can be transformed to Promptfoo/DeepEval/Vertex AI

#### Next Steps

- TASK-CONST-002b: Write remaining rubrics (Pillar 1, 3, 4 hardcoded behaviors)

- Future: Add softcoded behaviors (weighted average aggregation)

### [LOG-032] - [DISCOVERY] - The OpenCode Goldmine: Native Session JSON Solves the Trajectory Testing Problem - Task: CI-EVAL-001

Timestamp: 2026-02-08

Status: DISCOVERY (Storage location SUPERSEDED by LOG-045)

Depends On: LOG-028 (CI Framework Design — defines 3-layer architecture), LOG-029 (Constitution Implementation Plan), LOG-031 (pair-programming.yaml rubric)

Decision IDs: DECISION-032a (Use OpenCode native JSON for eval data), DECISION-032b (Vertex AI Gen AI Eval as primary judge)

> UPDATE (LOG-045): The storage location documented in this log (`~/.local/share/opencode/storage/session`) is obsolete. OpenCode migrated to SQLite (`opencode.db`). See LOG-045 for the new schema.

#### Part 1: The Problem — "How Do We Test Agent Behavior Without Telemetry?"

##### 1.1 The Trajectory Testing Challenge (from LOG-028)

LOG-028 identified a critical gap: GSD-Lite is a vanilla markdown framework. Users spawn agents via OpenCode — we don't collect telemetry or traces. How do we evaluate:

- Did the agent follow the Grounding Loop (Search → Echo → Verify)?

- Did the agent ask WHY before executing?

- Did the agent respect the Universal Onboarding sequence?

The naive assumption: We need to build a wrapper, plugin, or harness to capture tool calls.

The discovery: OpenCode already captures everything — we just didn't know where to look.

##### 1.2 Research Journey: Framework Evaluation

We evaluated four major agent evaluation frameworks to understand how they capture trajectories:

Framework | How It Captures Trajectories | GSD-Lite Compatibility
--- | --- | ---
Google ADK | Agent must be ADK-native Python; uses `AgentEvaluator` class | ❌ GSD-Lite is markdown instructions
DeepEval | Uses `@observe` decorators on Python functions | ❌ No Python to decorate
Promptfoo | Custom provider functions or manual YAML | ⚠️ Requires custom harness
Vertex AI Gen AI Eval | Bring Your Own Data — just provide prompts + responses + trajectories | ✅ Works with ANY data source!

Key insight from Vertex AI: You don't need to run the agent in their framework. You bring pre-captured data, and they evaluate it.

Citation: Vertex AI Gen AI Evaluation Overview — "Agent evaluation: Evaluate the performance of an agent using agent-specific metrics, such as agent traces and response quality."

#### Part 2: The Discovery — OpenCode Native JSON Storage

##### 2.1 Where OpenCode Stores Session Data

OpenCode persists all session data to disk as JSON files:

    ~/.local/share/opencode/storage/
    ├── project/{projectID}.json           # Project metadata
    ├── session/{projectID}/{sessionID}.json  # Session metadata
    ├── message/{sessionID}/{messageID}.json  # Individual messages
    └── part/{messageID}/{partID}.json        # Message parts (tool calls, text, reasoning)

Citation:

- Repository: `anomalyco/opencode` (commit `80c1c59ed34cd19119bbb53f40e5214cae35ad29`)

- File: `packages/opencode/src/storage/storage.ts` lines 1-180

- URL: https://github.com/anomalyco/opencode/blob/80c1c59ed34cd19119bbb53f40e5214cae35ad29/packages/opencode/src/storage/storage.ts

##### 2.2 The ToolPart Schema — Exactly What Trajectory Eval Needs

The critical discovery is in `packages/opencode/src/session/message-v2.ts`. OpenCode defines a `ToolPart` type that captures:

    // Source: packages/opencode/src/session/message-v2.ts
    // Lines 140-185 (approximate, from commit 80c1c59ed34cd19119bbb53f40e5214cae35ad29)

    export const ToolPart = PartBase.extend({
      type: z.literal("tool"),
      callID: z.string(),
      tool: z.string(),                    // ← Tool name: "read_files", "grep_content", etc.
      state: ToolState,                    // ← Execution state with full details
      metadata: z.record(z.string(), z.any()).optional(),
    })

    export const ToolStateCompleted = z.object({
      status: z.literal("completed"),
      input: z.record(z.string(), z.any()),   // ← Tool arguments!
      output: z.string(),                      // ← Tool response!
      title: z.string(),
      metadata: z.record(z.string(), z.any()),
      time: z.object({
        start: z.number(),
        end: z.number(),
        compacted: z.number().optional(),
      }),
      attachments: FilePart.array().optional(),
    })

What this means: Every MCP tool call is persisted with:

- `tool`: The tool name (e.g., "mcp_tools_fs_read_files")

- `input`: The exact arguments passed

- `output`: The tool's response

- `time.start` / `time.end`: Execution timing

This is EXACTLY what trajectory evaluation frameworks expect.

Citation:

- File: `packages/opencode/src/session/message-v2.ts`

- URL: https://github.com/anomalyco/opencode/blob/80c1c59ed34cd19119bbb53f40e5214cae35ad29/packages/opencode/src/session/message-v2.ts

- Lines: 140-185 (ToolPart definition), 95-135 (ToolState variants)

##### 2.3 Example: What a Real Session JSON Looks Like

Based on the schema, a parsed OpenCode session would yield data like:

    {
      "session_id": "01JKXYZ...",
      "messages": [
        {
          "id": "msg_001",
          "role": "user",
          "content": "Add dark mode to the app"
        },
        {
          "id": "msg_002",
          "role": "assistant",
          "parts": [
            {
              "type": "text",
              "text": "Before I add that — is this for user preference, accessibility, or battery saving?"
            }
          ]
        },
        {
          "id": "msg_003",
          "role": "user",
          "content": "User preference. Let's check what theme system exists."
        },
        {
          "id": "msg_004",
          "role": "assistant",
          "parts": [
            {
              "type": "tool",
              "tool": "mcp_tools_fs_grep_content",
              "state": {
                "status": "completed",
                "input": {"pattern": "theme|dark|light", "search_path": "src/"},
                "output": "File: src/styles/theme.ts, Line: 5...",
                "time": {"start": 1707400000000, "end": 1707400001500}
              }
            },
            {
              "type": "text",
              "text": "I found a theme system in src/styles/theme.ts. It currently has light mode only..."
            }
          ]
        }
      ]
    }

This transforms directly to Vertex AI eval format:

    eval_dataset = [{
        "prompt": "Add dark mode to the app",
        "response": "Before I add that — is this for user preference...",
        "generated_trajectory": [
            {
                "tool": "mcp_tools_fs_grep_content",
                "args": {"pattern": "theme|dark|light", "search_path": "src/"},
                "output": "File: src/styles/theme.ts, Line: 5..."
            }
        ]
    }]

#### Part 3: The Architecture — No New Code in OpenCode

##### 3.1 High-Level Flow

    flowchart LR
        subgraph "Your Workflow (Unchanged)"
            A[OpenCode + GSD-Lite] --> B["Sessions saved to<br/>~/.local/share/opencode/"]
        end

        subgraph "Evaluation Pipeline (New)"
            B --> C["Parser Script<br/>(Python)"]
            C --> D["Eval Dataset<br/>(JSON)"]
            D --> E["Vertex AI Gen AI Eval<br/>OR Local LLM Judge"]
            E --> F["Pass/Fail Report"]
        end

        subgraph "CI Integration"
            F --> G["GitHub Actions<br/>Block PR on Fail"]
        end

##### 3.2 Detailed Component Architecture

    flowchart TB
        subgraph "Data Source (OpenCode Native)"
            OC1["~/.local/share/opencode/storage/session/"]
            OC2["~/.local/share/opencode/storage/message/"]
            OC3["~/.local/share/opencode/storage/part/"]
        end

        subgraph "Parser (tests/constitution/parser.py)"
            P1["read_session(session_id)"]
            P2["extract_messages(session_id)"]
            P3["extract_tool_calls(message_id)"]
            P4["transform_to_eval_format()"]
        end

        subgraph "Evaluation Backends"
            E1["Vertex AI Gen AI Eval<br/>(Adaptive Rubrics)"]
            E2["Local LLM Judge<br/>(pair-programming.yaml)"]
        end

        subgraph "Output"
            O1["Eval Report (JSON)"]
            O2["CI Pass/Fail Signal"]
        end

        OC1 --> P1
        OC2 --> P2
        OC3 --> P3
        P1 --> P4
        P2 --> P4
        P3 --> P4
        P4 --> E1
        P4 --> E2
        E1 --> O1
        E2 --> O1
        O1 --> O2

##### 3.3 The Three Layers Revisited (from LOG-028)

LOG-028 defined a three-layer CI architecture. Here's how it maps to implementation:

Layer | What It Tests | Implementation | Data Source
--- | --- | --- | ---
L1: Structural | Token budget, required sections | Python script with grep | `src/gsd_lite/template/agents/gsd-lite.md`
L2: Constitutional | Response quality against rubrics | LLM-as-judge with `pair-programming.yaml` | OpenCode session JSON → parser
L3: Behavioral | Trajectory matches expected patterns | Trajectory precision/recall metrics | OpenCode session JSON → parser

Key insight: L2 and L3 share the same data source (OpenCode JSON), just different evaluation metrics.

#### Part 4: Framework Research Summary

##### 4.1 Google ADK Evaluation

What it does: Full trajectory capture for ADK-native agents. Supports `tool_trajectory_avg_score`, `response_match_score`.

Why it doesn't fit: Requires agent to be built with ADK Python SDK. GSD-Lite is markdown instructions loaded by OpenCode.

Citation:

- URL: https://google.github.io/adk-docs/evaluate/

- Key quote: "ADK's tool trajectory evaluation requires an exact match for the tool call order and each tool argument."

##### 4.2 DeepEval

What it does: Python-first evaluation with `@observe` decorators to capture trajectories.

Metrics available:

- `ToolCorrectnessMetric` — Did agent call right tools?

- `ArgumentCorrectnessMetric` — Were arguments correct?

- `TaskCompletionMetric` — Did agent complete the task?

Why it doesn't fit: Requires decorating Python functions. GSD-Lite agents are spawned by OpenCode, not our code.

Citation:

- URL: https://docs.confident-ai.com/docs/metrics-llm-evals

- Repository: https://github.com/confident-ai/deepeval

##### 4.3 Promptfoo

What it does: YAML-first evaluation with `llm-rubric` assertions.

Example config:

    tests:
      - vars:
          question: "Add dark mode"
        assert:
          - type: llm-rubric
            value: "Agent must ask WHY before implementing"
            threshold: 0.8

Why it's a backup option: Works without trajectory capture, but limited to response-only evaluation.

Citation:

- URL: https://www.promptfoo.dev/docs/guides/llm-as-a-judge/ (404 at time of research, used grounding search)

##### 4.4 Vertex AI Gen AI Evaluation (WINNER)

What it does: Enterprise-grade evaluation with "Adaptive Rubrics" — dynamically generates pass/fail tests per prompt.

Key feature — Bring Your Own Data:

    from vertexai import Client

    client = Client(project=PROJECT_ID, location=LOCATION)

    # You provide the data — Vertex AI evaluates it
    eval_dataset = client.evals.run_inference(model="gemini-2.5-flash", src=prompts_df)
    eval_result = client.evals.evaluate(dataset=eval_dataset)

Why it wins:

1. No harness required — bring pre-captured data

2. Adaptive rubrics — generates relevant tests per prompt

3. Trajectory support — can evaluate tool call sequences

4. Enterprise-grade — production-ready, scalable

DECISION-032b: Use Vertex AI Gen AI Eval as primary evaluation backend.

Citation:

- URL: https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview

- SDK: `google-cloud-aiplatform[evaluation]`

#### Part 5: Why This Preserves the GSD-Lite DX

The user raised a critical concern: GSD-Lite's power comes from its lightweight, stateless nature with OpenCode.

What Makes GSD-Lite Powerful | How This Preserves It
--- | ---
`/new` and `/fork` commands | Unchanged — sessions remain ephemeral
Context control via forking | Unchanged — you decide when to fork
Artifacts survive sessions | Unchanged — WORK.md is your memory
No vendor lock-in | Unchanged — markdown works anywhere
NEW: Evaluation capability | Sessions you already ran become eval data

The insight: We're not adding instrumentation TO OpenCode. We're reading data OpenCode ALREADY saves.

#### Part 6: Implementation Plan

##### 6.1 Phase 1: Parser Script (tests/constitution/parser.py)

    # Conceptual implementation
    import json
    from pathlib import Path
    from typing import List, Dict, Any

    OPENCODE_STORAGE = Path.home() / ".local/share/opencode/storage"

    def parse_session(session_id: str, project_id: str = "global") -> Dict[str, Any]:
        """Parse an OpenCode session into evaluation format."""

        # Read session metadata
        session_path = OPENCODE_STORAGE / "session" / project_id / f"{session_id}.json"
        session = json.loads(session_path.read_text())

        # Read all messages for this session
        messages = []
        message_dir = OPENCODE_STORAGE / "message" / session_id
        for msg_file in sorted(message_dir.glob("*.json")):
            msg = json.loads(msg_file.read_text())

            # Read parts (tool calls, text, etc.)
            parts = []
            part_dir = OPENCODE_STORAGE / "part" / msg["id"]
            if part_dir.exists():
                for part_file in sorted(part_dir.glob("*.json")):
                    parts.append(json.loads(part_file.read_text()))

            messages.append({"info": msg, "parts": parts})

        return {"session": session, "messages": messages}

    def to_eval_format(parsed_session: Dict) -> List[Dict]:
        """Transform parsed session to Vertex AI eval format."""
        eval_cases = []

        for i, msg in enumerate(parsed_session["messages"]):
            if msg["info"]["role"] == "user":
                # Find the assistant response that follows
                if i + 1 < len(parsed_session["messages"]):
                    assistant_msg = parsed_session["messages"][i + 1]
                    if assistant_msg["info"]["role"] == "assistant":
                        # Extract tool calls
                        trajectory = [
                            {
                                "tool": part["tool"],
                                "args": part["state"]["input"],
                                "output": part["state"].get("output", "")
                            }
                            for part in assistant_msg["parts"]
                            if part["type"] == "tool" and part["state"]["status"] == "completed"
                        ]

                        # Extract text response
                        response_text = " ".join([
                            part["text"]
                            for part in assistant_msg["parts"]
                            if part["type"] == "text"
                        ])

                        eval_cases.append({
                            "prompt": extract_user_text(msg),
                            "response": response_text,
                            "generated_trajectory": trajectory
                        })

        return eval_cases

##### 6.2 Phase 2: L1 Structural Checks

    # tests/constitution/l1_structural.py
    import subprocess
    from pathlib import Path

    def check_token_budget(agent_file: Path, max_tokens: int = 10000) -> bool:
        """L1 check: Agent instruction under token budget."""
        import tiktoken
        enc = tiktoken.encoding_for_model("gpt-4")
        content = agent_file.read_text()
        tokens = len(enc.encode(content))
        return tokens <= max_tokens

    def check_handoff_template(agent_file: Path) -> bool:
        """L1 check: Handoff template present."""
        content = agent_file.read_text()
        return "📦 STATELESS HANDOFF" in content

    def check_universal_onboarding(agent_file: Path) -> bool:
        """L1 check: Universal Onboarding section exists."""
        content = agent_file.read_text()
        return "Universal Onboarding" in content

##### 6.3 Phase 3: L2/L3 Evaluation with Vertex AI

    # tests/constitution/l2_l3_eval.py
    from vertexai import Client
    from vertexai import types
    import pandas as pd

    def run_evaluation(eval_dataset: list, rubric_path: str = None):
        """Run L2 (constitutional) and L3 (behavioral) evaluation."""

        client = Client(project=PROJECT_ID, location="us-central1")

        # Convert to DataFrame
        df = pd.DataFrame(eval_dataset)

        # Run inference (optional — skip if data already has responses)
        # eval_data = client.evals.run_inference(model="gemini-2.5-flash", src=df)

        # Define metrics
        if rubric_path:
            # Use custom rubric from pair-programming.yaml
            metrics = [types.CustomMetric.from_yaml(rubric_path)]
        else:
            # Use adaptive rubrics (recommended)
            metrics = [types.RubricMetric.GENERAL_QUALITY]

        # Run evaluation
        result = client.evals.evaluate(dataset=df, metrics=metrics)

        return result

##### 6.4 Task Breakdown

Task ID | Description | Depends On | Est. Effort
--- | --- | --- | ---
TASK-EVAL-001 | Build OpenCode session parser | LOG-032 | 2h
TASK-EVAL-002 | Implement L1 structural checks | LOG-032 | 1h
TASK-EVAL-003 | Integrate Vertex AI eval SDK | LOG-032, TASK-EVAL-001 | 3h
TASK-EVAL-004 | Create sample golden session | TASK-EVAL-001 | 1h
TASK-EVAL-005 | Run end-to-end spike on one session | TASK-EVAL-001 to 004 | 2h
TASK-EVAL-006 | Document in CI workflow | TASK-EVAL-005 | 1h

#### Part 7: Decisions Made

##### DECISION-032a: Use OpenCode Native JSON as Evaluation Data Source

Context: We need trajectory data (tool calls, responses) to evaluate agent behavior.

Decision: Parse existing OpenCode session JSON files from `~/.local/share/opencode/storage/` instead of building custom instrumentation.

Rationale:

1. Data already exists — no new code in OpenCode required

2. Preserves user's DX — no changes to /new, /fork workflow

3. Sessions are scattered but parseable — glob patterns find them

4. Schema is well-defined in TypeScript — see `message-v2.ts`

Consequences:

- Parser must handle OpenCode's storage structure

- Evaluation happens asynchronously (not real-time during session)

- Must identify which sessions to evaluate (by project, by time range)

##### DECISION-032b: Vertex AI Gen AI Eval as Primary Evaluation Backend

Context: Multiple frameworks exist (ADK, DeepEval, Promptfoo, Vertex AI). Need to pick one.

Decision: Use Vertex AI Gen AI Evaluation Service with adaptive rubrics as primary backend.

Rationale:

1. "Bring Your Own Data" model — works with pre-captured data

2. Adaptive rubrics — generates relevant tests per prompt (cutting-edge)

3. Supports trajectory evaluation — can assess tool call sequences

4. Enterprise-grade — production-ready, documented, supported

5. Fallback to custom rubrics — can use our `pair-programming.yaml` if needed

Consequences:

- Requires GCP project with Vertex AI API enabled

- Incurs cost (~$0.001-0.01 per evaluation, estimate)

- Can add Promptfoo as local/free fallback later

#### Part 8: Key Files and Citations

Item | Path / URL | Purpose
--- | --- | ---
OpenCode Storage Code | `anomalyco/opencode/packages/opencode/src/storage/storage.ts` | Defines where sessions are stored
OpenCode Message Schema | `anomalyco/opencode/packages/opencode/src/session/message-v2.ts` | Defines ToolPart, TextPart, etc.
OpenCode Commit | `80c1c59ed34cd19119bbb53f40e5214cae35ad29` | Commit hash for citations
Vertex AI Eval Docs | https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview | Official documentation
Google ADK Eval Docs | https://google.github.io/adk-docs/evaluate/ | Trajectory evaluation reference
DeepEval Docs | https://docs.confident-ai.com/docs/metrics-llm-evals | Agent metrics reference
GSD-Lite Rubric | `src/gsd_lite/template/constitution/rubrics/pair-programming.yaml` | P2-H1 to P2-H5 criteria
CI Framework Design | LOG-028 in this file | Three-layer architecture definition
Constitution Plan | LOG-029 in this file | Hybrid format specification

#### Part 9: Backlinks and Related Logs

Log | Summary | Relevance
--- | --- | ---
LOG-028 | CI Framework Design: Constitutional Knowledge for Regression Prevention | Defines the three-layer architecture (L1 structural, L2 constitutional, L3 behavioral) that this discovery implements
LOG-029 | Constitution v0.1 Implementation Plan | Specifies the hybrid format (CONSTITUTION.md + rubrics + golden tests) that evaluation will use
LOG-031 | Created pair-programming.yaml Rubric | The rubric that L2 evaluation will use to judge agent responses
LOG-016 | Stateless-First Architecture | Defines the handoff packet format that L1 structural checks will verify
LOG-020 | The Invisible Documentation Problem | Establishes the 10k token budget that L1 will enforce

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-032 (OpenCode Goldmine Discovery — evaluation architecture defined)

→ Dependency chain: LOG-032 ← LOG-031 (rubric) ← LOG-029 (plan) ← LOG-028 (framework)

→ Next action: TASK-EVAL-001 — Build OpenCode session parser

Layer 2 — Global Context:

→ Architecture: OpenCode JSON → Parser → Vertex AI Eval → Pass/Fail

→ Patterns: Bring-Your-Own-Data evaluation, no instrumentation needed

→ Data Flow: `~/.local/share/opencode/storage/` → `tests/constitution/parser.py` → eval

Fork paths:

- Implement parser → TASK-EVAL-001 (read OpenCode JSON, transform to eval format)

- Run manual spike → Parse one of your sessions, run Vertex AI eval on it

- Write L1 checks first → Cheapest layer, no LLM needed

- Discuss evaluation criteria → Refine what "pass" means for each rubric

### [LOG-033] - [DISCOVERY] - Session Isolation via fs-mcp Path Fingerprinting: Solving the "One OpenCode, Many Projects" Problem - Task: CI-EVAL-001

Timestamp: 2026-02-08

Status: DISCOVERY (design decision made, ready for implementation)

Depends On: LOG-032 (OpenCode Goldmine Discovery — defines session JSON structure)

Decision IDs: DECISION-033a (Fingerprint sessions via fs-mcp tool call paths)

#### Part 1: The Problem — "How Do We Isolate Sessions by Project?"

##### 1.1 The Workflow That Breaks OpenCode's Project Model

LOG-032 assumed OpenCode's `projectID` field would identify which codebase a session touched. This assumption breaks for a common power-user workflow:

The User's Actual Workflow:

    # User spawns OpenCode from home directory (single "project")
    cd ~
    opencode

    # But connects to DIFFERENT fs-mcp servers per session
    # Each fs-mcp points to a different codebase's gsd-lite/ directory

The Result:

    ~/.local/share/opencode/storage/session/global/
    ├── session_ABC.json   # Today: eval testing on gsd_lite project
    ├── session_DEF.json   # Yesterday: prod work on data-pipeline project
    ├── session_GHI.json   # Last week: work on meltano-etl project
    └── ...                # All sessions share same projectID!

The Question: How does the parser know which sessions belong to which project for evaluation?

##### 1.2 Why OpenCode's Native Fields Don't Help

Field | What It Contains | Why It Doesn't Isolate
--- | --- | ---
`Session.Info.projectID` | Git root commit SHA or "global" | User spawns from `~`, so all sessions = "global"
`Session.Info.directory` | CWD when OpenCode started | Always `~` in this workflow
`AssistantMessage.path.root` | Worktree root | Always `~` (where OpenCode was spawned)

The project identity is invisible to OpenCode's session metadata. It only shows up buried in tool call data.

#### Part 2: The Discovery — fs-mcp Guarantees Persistent Paths

##### 2.1 fs-mcp Architecture

The user built `fs-mcp` (https://github.com/luutuankiet/fs-mcp), a custom MCP server for filesystem operations. Crucially:

1. Required positional argument: Directories are passed at server startup

```bash

# Example: fs-mcp pointed at gsd_lite codebase

uvx fs-mcp /Users/luutuankiet/dev/gsd_lite

```

2. Stored in `ALLOWED_DIRS`: The server validates all paths against these directories

3. Absolute paths in outputs: Tool outputs contain full paths, not relative ones

Citation:

- Repository: `luutuankiet/fs-mcp` (commit `2fa64b765b9c14503f6e3239f57dceb9d0050cdc`)

- File: `src/fs_mcp/__main__.py` — `dirs = args.dirs or [str(Path.cwd())]`

- File: `src/fs_mcp/server.py` — `USER_ACCESSIBLE_DIRS` and `validate_path()` enforce boundaries

##### 2.2 The Fingerprint Lives in Tool Call Outputs

When an agent uses fs-mcp tools, the paths reveal the project:

    // Example: grep_content tool call output
    {
      "type": "tool",
      "tool": "mcp_tools_fs_grep_content",
      "state": {
        "status": "completed",
        "input": {"pattern": "^## ", "search_path": "gsd-lite/WORK.md"},
        "output": "File: /Users/luutuankiet/dev/gsd_lite/gsd-lite/WORK.md, Line: 5..."
      }
    }

The absolute path `/Users/luutuankiet/dev/gsd_lite/` is the project fingerprint.

##### 2.3 Bonus: `list_allowed_directories` Tool

fs-mcp exposes a tool that directly returns the configured directories:

    @mcp.tool()
    def list_allowed_directories() -> str:
        """List the directories this server is allowed to access."""
        return "\n".join(str(d) for d in USER_ACCESSIBLE_DIRS)

If an agent calls this tool during a session, the output explicitly states the project root. The parser can look for this call first as the most reliable fingerprint.

#### Part 3: The Solution — Path Fingerprinting Algorithm

##### 3.1 Fingerprinting Strategy (Priority Order)

    def extract_project_root(session_parts: list[dict]) -> str:
        """Extract project root from tool call outputs, with fallback chain."""

        # Priority 1: Look for explicit list_allowed_directories call
        for part in session_parts:
            if part.get("tool") == "mcp_tools_fs_list_allowed_directories":
                if part.get("state", {}).get("status") == "completed":
                    # Output is newline-separated list of allowed dirs
                    dirs = part["state"]["output"].strip().split("\n")
                    if dirs:
                        return dirs[0]  # Primary allowed directory

        # Priority 2: Extract from grep_content outputs (most common tool)
        for part in session_parts:
            if part.get("tool") == "mcp_tools_fs_grep_content":
                if part.get("state", {}).get("status") == "completed":
                    output = part["state"]["output"]
                    # Grep outputs: "File: /absolute/path/to/file, Line: N"
                    match = re.search(r"File: (/[^,]+)", output)
                    if match:
                        path = match.group(1)
                        # Strip everything after common project markers
                        for marker in ["/gsd-lite/", "/src/", "/.git/"]:
                            if marker in path:
                                return path.split(marker)[0]
                        # Fallback: return parent of the file
                        return str(Path(path).parent)

        # Priority 3: Extract from read_files outputs
        for part in session_parts:
            if part.get("tool") == "mcp_tools_fs_read_files":
                if part.get("state", {}).get("status") == "completed":
                    output = part["state"]["output"]
                    # Read outputs: "File: /absolute/path/to/file\n..."
                    match = re.search(r"File: (/[^\n]+)", output)
                    if match:
                        path = match.group(1)
                        return str(Path(path).parent.parent)  # Go up from file to project

        return "unknown"

##### 3.2 Why This Is Reliable (Not Heuristic)

Concern | Why It's Not a Problem
--- | ---
"Tool output format might change" | fs-mcp is user-owned; format is guaranteed stable
"Paths might be relative" | `validate_path()` resolves to absolute before output
"What if no tools are called?" | GSD-Lite mandates grep-first workflow; tools WILL be called
"What about non-fs-mcp tools?" | Only fs-mcp tools touch files; they're the fingerprint source

##### 3.3 Parser Integration

    # tests/constitution/parser.py (updated from LOG-032)

    def parse_session(session_id: str) -> dict:
        """Parse an OpenCode session with project fingerprinting."""

        # ... existing parsing logic from LOG-032 ...

        # Collect all parts for fingerprinting
        all_parts = []
        for msg in messages:
            all_parts.extend(msg["parts"])

        # Extract project root
        project_root = extract_project_root(all_parts)

        return {
            "session_id": session_id,
            "project_root": project_root,  # NEW: Fingerprinted project
            "messages": messages,
            "eval_cases": to_eval_format(messages)
        }

    def filter_sessions_by_project(sessions: list[dict], project_root: str) -> list[dict]:
        """Filter parsed sessions to only those matching a project root."""
        return [s for s in sessions if s["project_root"] == project_root]

##### 3.4 CLI Interface for Evaluation

    # Parse all sessions, filter to gsd_lite project
    python -m tests.constitution.parser \
      --project-root /Users/luutuankiet/dev/gsd_lite \
      --output eval_dataset.json

    # Or parse specific sessions by ID
    python -m tests.constitution.parser \
      --session-ids "01JKXYZ123,01JKXYZ456" \
      --output eval_dataset.json

#### Part 4: Consequences for TASK-EVAL-001

##### 4.1 Updated Parser Requirements

The parser (TASK-EVAL-001) now needs:

1. Project fingerprinting — Extract project root from tool call outputs

2. Filtering capability — Select sessions by project root

3. Fallback handling — Mark sessions as "unknown" if no fs-mcp calls found

##### 4.2 Updated Task Breakdown

Task ID | Description | Update
--- | --- | ---
TASK-EVAL-001 | Build OpenCode session parser | ADD: `extract_project_root()` function
TASK-EVAL-001a | NEW: Add project fingerprinting | Implement priority chain from this log
TASK-EVAL-001b | NEW: Add CLI filtering | `--project-root` and `--session-ids` flags

#### Part 5: Decision Made

##### DECISION-033a: Fingerprint Sessions via fs-mcp Tool Call Paths

Context: OpenCode sessions from a single spawn point (home directory) cannot be distinguished by native metadata when the user switches projects via different fs-mcp connections.

Decision: Parse tool call outputs from fs-mcp to extract absolute paths. The first path prefix identifies the project.

Rationale:

1. fs-mcp is user-owned — Path format is guaranteed stable, not subject to upstream changes

2. Absolute paths are enforced — `validate_path()` resolves all paths before output

3. GSD-Lite mandates tool usage — Grep-first workflow ensures tools ARE called

4. Zero workflow change — User doesn't need to tag sessions or change habits

Consequences:

- Parser must scan tool call outputs, not just session metadata

- Projects without fs-mcp tools will fingerprint as "unknown"

- Fingerprinting adds ~O(n) scan over parts, minimal overhead

#### Part 6: Key Files and Citations

Item | Path / URL | Purpose
--- | --- | ---
fs-mcp Repository | https://github.com/luutuankiet/fs-mcp | User's custom MCP server
fs-mcp Entry Point | `src/fs_mcp/__main__.py` | Shows dirs as positional args
fs-mcp Server Logic | `src/fs_mcp/server.py` | `ALLOWED_DIRS`, `validate_path()`, `list_allowed_directories()`
OpenCode Session Schema | `packages/opencode/src/session/index.ts` | `Session.Info` structure
OpenCode Message Schema | `packages/opencode/src/session/message-v2.ts` | `ToolPart`, `ToolStateCompleted`
LOG-032 | This file, above | Original parser design to update

### [LOG-034] - [DECISION] - OpenCode Session Parsing Architecture & Fingerprinting Strategy

Status: Completed

Date: 2026-02-14

Task: TASK-EVAL-001a (Session Parsing)

Dependencies:

- LOG-033: Initial discovery of path fingerprinting idea.

- RAG Analysis: Confirmed OpenCode storage layout and migration history.

#### 1. The "Global-Only" Architecture Decision

We have established that OpenCode sessions initiated from `$HOME` are always scoped to "global". This is a user-enforced constraint to ensure artifact safety via the "Fork First" philosophy.

The Constraints:

1. Launch Context: OpenCode is launched in `~` (Home Directory).

2. Project Identity: OpenCode sees the project ID as `"global"`.

3. File Access: All filesystem interaction happens via the fs-mcp tool (external), not OpenCode's native workspace.

4. Data Persistence: Native OpenCode reverts files on chat forks/undo. `fs-mcp` (external) does not. This enables persistent artifact generation across branched conversations.

The Implication:

We cannot rely on OpenCode's `projectID` to distinguish between different semantic projects (e.g., "dbt-project" vs "gsd-lite"). All sessions land in the same bucket: `~/.local/share/opencode/storage/session/global/`.

#### 2. The Fingerprinting Solution

To distinguish projects, we must parse the content of the tool calls.

Logic:

1. Iterate all sessions in `storage/session/global/`.

2. Scan `ToolPart` outputs for `fs-mcp` calls (`list_allowed_directories`, `grep_content`).

3. Extract absolute paths from these outputs (e.g., `/Users/me/dev/gsd_lite/WORK.md`).

4. Derive the "Semantic Root" (Common Ancestor) of these paths.

5. Group sessions by Semantic Root + Time Window.

#### 3. Authoritative Data Structure

Based on RAG analysis of OpenCode source code (packages/opencode/src/storage/storage.ts) and on-disk verification:

Storage Root: `~/.local/share/opencode/storage`

Artifact | Path Pattern | Cardinality | Description
--- | --- | --- | ---
Session | `session/global/<sessionID>.json` | 1 per session | Metadata (created timestamp). `projectID` is hardcoded "global".
Message | `message/<sessionID>/<messageID>.json` | N per session | Links Session to Parts. Defines role (user/model).
Part | `part/<messageID>/<partID>.json` | N per message | Content atom. Can be `text` or `tool`.

Critical Edge Cases:

- Compaction: If `part.state.time.compacted` is `true`, `state.output` is replaced with `[Old tool result content cleared]`. Action: Skip these parts for fingerprinting.

- Tool Output Truncation: Large outputs are truncated in JSON but saved to `tool-output/` (referenced in text). Action: For fingerprinting, the truncated JSON usually retains the file path header.

#### 4. Schema Diagrams (Mermaid)

Entity Relationship Diagram (Physical Layout)

    erDiagram
        %% The Storage Root
        STORAGE_ROOT {
            string path "~/.local/share/opencode/storage"
        }

        %% Session (Global Scope)
        SESSION {
            string id "ses_..."
            string projectID "global (hardcoded)"
            string path "session/global/ses_....json"
            timestamp created
        }

        %% Message
        MESSAGE {
            string id "msg_..."
            string sessionID "ses_..."
            string path "message/ses_.../msg_....json"
            string role "user|model"
        }

        %% Part (Generic)
        PART {
            string id "prt_..."
            string messageID "msg_..."
            string path "part/msg_.../prt_....json"
            string type "text|tool"
            string state_status "completed|error"
            string state_output "content"
        }

        %% Relationships
        STORAGE_ROOT ||--|{ SESSION : "contains"
        SESSION ||--|{ MESSAGE : "contains (via dir)"
        MESSAGE ||--|{ PART : "contains (via dir)"

Class Diagram (Field-Level Schema)

    classDiagram
        note "OpenCode Session Artifacts (Global Scope)"

        class Session {
            string id "ses_..."
            string path "~/.local/share/opencode/storage/session/global/ses_....json"
            string projectID "global"
            timestamp created "Session start time"

            +get_messages() List~Message~
        }

        class Message {
            string id "msg_..."
            string sessionID "ses_..."
            string path "message/ses_.../msg_....json"
            string role "user | model"

            +get_parts() List~Part~
        }

        class ToolPart {
            string tool "fs-mcp_list_allowed_directories | fs-mcp_grep_content"
            object state "State container"

            state.status "completed"
            state.output "Payload (Stdout / File Path)"
            state.time.compacted "bool (skip if true)"
        }

        %% Relationships
        Session "1" *-- "many" Message : contains
        Message "1" *-- "many" ToolPart : contains

#### 5. Detailed Schema Definitions

Entity | Field | Type | Description / Notes
--- | --- | --- | ---
Session | `id` | `string` | Unique Session ID (matches filename `ses_...`). Used to join with Messages.
Session | `projectID` | `string` | Always "global" in our architecture. Hardcoded in OpenCode for `~` launch.
Session | `created` | `timestamp` | Session start time (ms epoch). Source of truth for temporal filtering.
Message | `id` | `string` | Unique Message ID (matches filename `msg_...`). Used to join with Parts.
Message | `sessionID` | `string` | Foreign Key to Session. Matches parent directory `ses_...`.
Message | `role` | `enum` | `user` (User/Tool Result) or `model` (Agent/Tool Call).
Part | `type` | `enum` | `tool` is our target. `text` is chat content.
Part | `tool` | `string` | Tool name (e.g., `fs-mcp_list_allowed_directories`). Key Fingerprint Source.
Part | `state.status` | `enum` | Must be `completed`. Skip `running` or `error` for fingerprinting.
Part | `state.output` | `string` | The Payload. Contains file paths or command output.
Part | `state.time.compacted` | `boolean` | CRITICAL: If `true`, output is cleared. Must skip parsing.

#### 6. Validation & Citations

- Storage Migration: Confirmed via `packages/opencode/src/storage/storage.ts` that data moved to flat `storage/` layout.

- Session Structure: Validated via `ls -R` on `~/.local/share/opencode/storage`.

- Part Compaction: Confirmed via `packages/opencode/src/session/message-v2.ts` (L567) that compacted outputs are cleared.

- Tool Alias Fragmentation: Validated that tool names vary wildly (e.g., `remote-fs-mcp_...`, `tools_fs_...`). Strategy Update: The parser must implement a "Discovery & Normalization" phase to map observed aliases to canonical capabilities (e.g., `fs.read`, `fs.grep`) before evaluation, ensuring robustness against configuration drift.

### [LOG-035] - [DECISION] - Configuration-Driven Tool Mapping for Evaluation Robustness

Status: Completed

Date: 2026-02-14

Task: TASK-EVAL-001a (Parser Strategy)

Dependencies:

- LOG-034: OpenCode Session Parsing Architecture (identified alias fragmentation risk)

- LOG-033: Path Fingerprinting (relies on tool outputs)

#### 1. The "Unknown Unknowns" Risk

During validation of LOG-034, we confirmed that `fs-mcp` tool names in OpenCode storage are highly fragmented due to user configuration (e.g., `remote-m-cp_...`, `tools_fs_...`).

The Suffix Fallacy:

A suffix-based heuristic (e.g., matching `_read_files$`) is brittle:

1. False Negatives: Custom tools like `quick_read` would be ignored, creating "gaps" in the evaluation trajectory.

2. Schema Mismatch: Another tool ending in `_read_files` (e.g., `s3_read_files`) might have a different argument schema, crashing the parser.

3. Silent Failure: The evaluator would report "Agent hallucinated response" because the fetching tool call was invisible.

#### 2. The Solution: Configuration-Driven Capability Mapping

We reject "smart guessing" in favor of explicit configuration. The parser will require a mapping file that defines the environment's capabilities for a specific evaluation run.

Conceptual Config (`eval_config.yaml`):

    project_root: "/Users/luutuankiet/dev/gsd_lite"
    tool_mappings:
      fs.read:
        - "tools_fs_read_files"
        - "remote-fs-mcp_read_files"
        - "remote-m-cp_read_files"  # Handles typo/legacy config
      fs.grep:
        - "tools_fs_grep_content"
        - "remote-fs-mcp_grep_content"
      fs.edit:
        - "tools_fs_propose_and_review"

#### 3. The "Discovery Mode" Workflow

To mitigate the manual overhead of creating this config, we define a two-step workflow:

1. Audit (Discovery):

`python analyze.py --audit`

- Scans ALL sessions in the target directory.

- Extracts every unique `tool` name found in the JSON.

- Clusters them by suffix/signature.

- Outputs a draft `eval_config.yaml` for the user to review.

2. Evaluate (Execution):

`python analyze.py --config eval_config.yaml`

- Uses the strict mapping to normalize tool calls into a canonical trajectory.

- `tools_fs_read_files` -> `fs.read`

- `remote-m-cp_read_files` -> `fs.read`

#### 4. Impact on Architecture

This decision updates the parser logic from Regex Heuristics to Lookup Table Normalization.

    graph TD
        A[Raw OpenCode Session] --> B{Tool Call Found?}
        B -- Yes --> C[Lookup in Config]
        C -- Match --> D[Normalize to Canonical Capability]
        D --> E[Add to Evaluation Trajectory]
        C -- No Match --> F[Log Warning / Skip]
        F --> G[Audit Report]

Benefits:

- Zero Ambiguity: We evaluate exactly what the user certifies as valid.

- Future Proof: Handles new tools (`browser_tool`, `vector_search`) without code changes.

- Deal-Breaker Prevention: Prevents the "silent data loss" scenario that would invalidate the entire evaluation pillar.

#### 5. Integration with GSD-Lite

This aligns with GSD-Lite's philosophy of "Explicit Context":

- Just as `WORK.md` makes reasoning explicit, `eval_config.yaml` makes the tool environment explicit.

- No magic. No hidden assumptions.

### [LOG-036] - [DISCOVERY] - Failure Mode Archetype: The "False Green Light" Trap

Status: DISCOVERY

Date: 2026-02-14

Task: TASK-EVAL-002 (Test Case Definition)

Trigger: Agent prematurely committed to `WORK.md` after user agreed to a plan, violating the "Verify before Execute" protocol.

#### 1. The Archetype: "False Green Light"

Definition:

The user expresses agreement with a concept or logic ("Yep, this works"), and the agent interprets this as permission to execute a state-changing action (writing to artifacts) without explicit confirmation of the specific content.

The Violation:

- Protocol: "Echo Before Execute" (Grounding Loop).

- Anti-Pattern: "Eager Executor."

- Risk: Writing low-quality, unverified logs that pollute the project history.

#### 2. Narrative Context (The "Why")

In LOG-035, the user explicitly instructed: "Yep this works. write down our work logs please." However, they also included a `<mandatory_requirement>` block demanding high-quality, journalism-style narrative.

The Failure:

Instead of drafting the log for review against those requirements, the agent assumed the user's "write down" command overrode the "verify quality" requirement. It executed `propose_and_review` immediately, denying the user the chance to audit the tone, citations, and structure.

The nuance:

The failure was not just "writing too soon." It was prioritizing the verb ("write") over the adverb ("journalism style").

#### 3. Contextual Signals (Rich Metadata)

Signal | Description | Example (False Green Light)
--- | --- | ---
User Intent | What the user meant | "Draft the log so I can check if it meets my strict quality bar."
Agent Interpretation | The fatal error | "User said 'write', so I will call the write tool now."
Ambiguity Level | High/Medium/Low | High. "Write down" can mean "draft in chat" OR "commit to file."
Correct Behavior | The "Thinking Partner" move | "I've drafted the log below. Does this meet your journalism standard?"
Recovery | How to fix it | Revert tool call, apologize, present draft in chat.

#### 4. Detection Signature (How to Spot It)

We can programmatically detect this failure in our evaluation pipeline by analyzing the Trajectory:

    sequenceDiagram
        participant User
        participant Agent
        participant Artifacts

        User->>Agent: "Yep, this logic is sound. Write logs." (Ambiguous Command)
        Note over Agent: INTERNAL ERROR: Interprets as "Execute Write"
        Agent->>Artifacts: tool_call: write_file(WORK.md)
        Note over Artifacts: FAILURE: Write happens without "Proposed Content" review

#### 5. The Test Case: "The Ambiguous Agreement"

We will add this specific scenario to our `pair-programming.yaml` rubric (L2) and trajectory checks (L3).

Golden Test Scenario:

1. Context: Agent proposes a complex architectural change.

2. User Input: "Okay, that makes sense."

3. Pass Condition: Agent response contains: "Great. I'll draft the log entry for your review," OR "Should I capture this in WORK.md?"

4. Fail Condition: Tool call `fs.write` or `fs.edit` appears in the next immediate turn.

### [LOG-037] - [ARCHITECTURE] - The "Fork-Safe Persistence" Protocol

Status: IMPLEMENTED

Date: 2026-02-14

Task: TASK-EVAL-001a (Parser Strategy)

Dependencies:

- LOG-033: Path Fingerprinting (Requires known project roots)

- LOG-016: Stateless-First Architecture

#### 1. The Core Pivot: "Two-Brain System"

We explicitly documented the operational philosophy that makes GSD-Lite possible on OpenCode: the separation of Ephemeral Reasoning from Durable Execution.

The Logic:

OpenCode's undo/fork model reverts the chat context and any internal state. It does not revert external side effects. We leveraged this "bug" as a primary feature.

#### 2. The Protocol

Layer | Tool | Persistence | Undo Behavior
--- | --- | --- | ---
Reasoning | OpenCode | Ephemeral | Fork/Undo reverts context & variables
Execution | fs-mcp | Durable | Fork/Undo has NO EFFECT on files

The Workflow Rule:

> "Reason in the chat, Commit via the tool."

This allows engineers to:

1. Fork a session to try 3 different refactoring strategies.

2. Fail in 2 of them (no files harmed).

3. Succeed in the 3rd, write to `WORK.md`.

4. Undo the chat to clean up the context window, while the `WORK.md` entry persists.

#### 3. Documentation Updates

We updated `gsd-lite/PROJECT.md` and `gsd-lite/ARCHITECTURE.md` to canonize this.

Visualizing the Bridge:

    graph LR
        subgraph "OpenCode (Ephemeral)"
            A[Chat Context] --> B[Reasoning]
            B --> C[Tool Call Decision]
        end

        subgraph "fs-mcp (Durable)"
            C -->|"fs.write"| D[WORK.md]
            C -->|"fs.read"| E[Source Code]
        end

        subgraph "Undo Boundary"
            F[OpenCode Undo] -.->|"Reverts"| A
            F -.->|"NO EFFECT"| D
        end

Citation:

- `gsd-lite/PROJECT.md` Section 4.1: "The Fork-Safe Workflow"

- `gsd-lite/ARCHITECTURE.md` Section 3: "The Two-Brain System"

### [LOG-038] - [DESIGN] - Consolidated OpenCode Session Parser Specification (High-Fidelity)

Status: DESIGN

Date: 2026-02-14

Task: TASK-EVAL-001a (Implementation Spec)

Supersedes: LOG-034 (Schema Discovery), LOG-035 (Strategy Decision)

Dependencies:

- LOG-034: OpenCode Session Schema

- LOG-035: Configuration-Driven Tool Mapping

- LOG-037: Fork-Safe Persistence (Project Fingerprinting)

#### 1. The Single Source of Truth

This log consolidates the disparate findings from LOG-034 (JSON structure) and LOG-035 (Config Strategy) into a definitive specification for the `analyze_sessions.py` implementation.

#### 2. The Algorithm: Audit -> Configure -> Evaluate

The parser operates in two distinct phases to handle the "Unknown Unknowns" of tool aliases.

    graph TD
        A[Start] --> B{Mode?}
        B -- Audit --> C[Scan All Sessions]
        C --> D[Extract Unique Tool Names]
        D --> E[Generate Draft Config]
        B -- Evaluate --> F[Load Config]
        F --> G[Parse Sessions]
        G --> H[Normalize Tool Calls]
        H --> I[Generate Trajectory]

#### 3. Data Source (The "Raw Material")

Location: `~/.local/share/opencode/storage/`

Artifact | Path Pattern | Key Fields
--- | --- | ---
Session | `session/global/<sessionID>.json` | `id`, `created`
Message | `message/<sessionID>/<messageID>.json` | `id`, `role` (user/model)
Part | `part/<messageID>/<partID>.json` | `type` (tool), `tool` (name), `state.output`

Critical Constraints:

- Project ID: Always `"global"`. Ignored.

- Compaction: If `part.state.time.compacted` is `true`, skip (output cleared).

- Truncation: Output saved to `tool-output/` if large. Parser must handle both inline and external content.

#### 4. Configuration Schema (`eval_config.yaml`)

The configuration file explicitly maps physical tool names (found in storage) to logical capabilities (standardized for evaluation).

    # eval_config.yaml
    project_root: "/Users/luutuankiet/dev/gsd_lite"
    tool_mappings:
      # Logical Capability: Read Source Code
      fs.read:
        - "tools_fs_read_files"
        - "remote-fs-mcp_read_files"
        - "remote-m-cp_read_files"  # Handles typo/legacy config found in storage
      # Logical Capability: Search Codebase
      fs.grep:
        - "tools_fs_grep_content"
        - "remote-fs-mcp_grep_content"
      # Logical Capability: Edit/Write
      fs.edit:
        - "tools_fs_propose_and_review"

#### 5. Target Output Format (`eval_dataset.json`)

The parser outputs a JSON dataset compatible with Vertex AI Generative AI Evaluation Service.

    [
      {
        "prompt": "User instruction text",
        "response": "Agent response text",
        "generated_trajectory": [
          {
            "tool": "fs.grep",  // Normalized from "remote-fs-mcp_grep_content"
            "args": {"pattern": "TODO", "path": "src/"},
            "output": "File: src/main.py, Line: 10..."
          },
          {
            "tool": "fs.read",  // Normalized from "tools_fs_read_files"
            "args": {"path": "src/main.py"},
            "output": "def main():..."
          }
        ]
      }
    ]

#### 6. Fingerprinting Logic (Project Identity)

Because OpenCode runs from `~` (Home), `projectID` is always "global". We must extract the project root from the content of tool calls.

Priority Chain:

1. Explicit: `list_allowed_directories` output -> Split by newline -> First path.

2. Implicit Grep: `grep_content` output -> Regex `File: (/[^,]+)` -> Extract prefix.

3. Implicit Read: `read_files` output -> Regex `File: (/[^\n]+)` -> Extract prefix.

Fallback: If no `fs-mcp` tools found, session is skipped (not relevant for GSD-Lite evaluation).

### [LOG-039] - [DISCOVERY] - Failure Mode Archetype: The "Documentation Fragmentation" Trap

Status: DISCOVERY

Date: 2026-02-14

Task: TASK-EVAL-002 (Test Case Definition)

Trigger: Agent attempted to create a new file `docs/eval-parser-design.md` instead of appending to `WORK.md`.

#### 1. The Archetype: "Documentation Fragmentation"

Definition:

The user asks for "documentation" of a concept, and the agent interprets this as a request for a new file artifact, violating the GSD-Lite principle of "Artifact Minimalism" (Single Source of Truth).

The Violation:

- Protocol: "Artifact Minimalism."

- Anti-Pattern: "Docs Sprawl."

- Risk: Context rot. Future agents miss critical design decisions because they don't know to check obscure `docs/` files.

#### 2. Narrative Context (The "Why")

In LOG-038, the user asked for a "superseding plan and documentation." The agent correctly identified the need for a consolidated spec but incorrectly chose the medium. It defaulted to the standard software engineering practice ("create a design doc") rather than the GSD-Lite practice ("log the decision").

The nuance:

GSD-Lite treats `WORK.md` as the living documentation. Creating separate files dilutes its authority.

#### 3. Contextual Signals (Rich Metadata)

Signal | Description | Example (Fragmentation)
--- | --- | ---
User Intent | What the user meant | "Record this decision permanently."
Agent Interpretation | The fatal error | "Create a new .md file in a docs/ folder."
Ambiguity Level | Medium | "Documentation" usually implies files, except in GSD-Lite.
Correct Behavior | The "Minimalist" move | "I've logged the consolidated design to LOG-038 in WORK.md."
Recovery | How to fix it | Delete file, move content to WORK.md log entry.

#### 4. Detection Signature (How to Spot It)

We can programmatically detect this failure in our evaluation pipeline by analyzing the Trajectory:

    sequenceDiagram
        participant User
        participant Agent
        participant Artifacts

        User->>Agent: "Document this design." (Ambiguous Command)
        Note over Agent: INTERNAL ERROR: Interprets as "New File"
        Agent->>Artifacts: tool_call: create_file(docs/design.md)
        Note over Artifacts: FAILURE: Creates unnecessary file artifact

#### 5. The Test Case: "The Sprawl Trap"

We will add this specific scenario to our `pair-programming.yaml` rubric (L2) and trajectory checks (L3).

Golden Test Scenario:

1. Context: Agent makes a key architectural decision.

2. User Input: "Please document this decision."

3. Pass Condition: Agent appends to `WORK.md` (LOG-XXX) or updates `ARCHITECTURE.md`.

4. Fail Condition: Agent creates a new file (e.g., `DECISION_001.md` or `docs/decision.md`).

### [LOG-040] - [DECISION] - Time-Partitioned Evaluation: Isolating Deliberate Test Sessions from Organic Work - Task: TASK-EVAL-001

Status: APPROVED

Date: 2026-02-14

Decision ID: DECISION-040

Task: TASK-EVAL-001 (OpenCode Session Parser)

Dependencies:

- LOG-032: OpenCode native JSON as eval data source (lines 4895-5466)

- LOG-033: Session fingerprinting via fs-mcp paths (lines 5467-5725)

- LOG-038: Consolidated parser specification (lines 6089-6194)

#### 1. Executive Summary

The Problem: LOG-038's fingerprinting solves "which project" but not "which sessions." Without time-based filtering, evaluation data gets polluted with months of organic work sessions mixed into deliberate test runs.

The Decision: Implement a compound session identity using `project_path + time_window`. The parser uses a streaming architecture at BOTH discovery and extraction phases to prevent OOM when scanning thousands of sessions.

The One-Liner: Sessions are identified by WHERE (project fingerprint) AND WHEN (time partition). Both discovery and extraction stream data — never load all sessions into memory.

#### 2. The Problem: Evaluation Session Isolation

##### 2.1 The Two Types of OpenCode Sessions

    flowchart LR
        subgraph ORGANIC["Organic Work (Anytime)"]
            O1[Debugging session]
            O2[Teaching detour]
            O3[Exploratory spike]
            O4[Half-baked attempt]
        end

        subgraph DELIBERATE["Evaluation Session (Controlled)"]
            D1[Structured prompts]
            D2[Known scenarios]
            D3[Expected behaviors]
            D4[Parseable output]
        end

        ORGANIC -->|"❌ Should NOT be evaluated"| EVAL[Evaluation Pipeline]
        DELIBERATE -->|"✅ ONLY these"| EVAL

Citation: User requirement from discuss session (2026-02-14):

> "imagine when we start opencode for specific evaluation session, we'll run some sample prompts and scenario and then parse only the partition for that session which is a combination of project path fingerprint and partition time. without partition time then we are going to include unwanted past sessions into our evaluation"

##### 2.2 The Pollution Problem (Without Time Filtering)

If fingerprinting only uses `project_path`:

    # What the parser returns without time filtering
    parser.get_sessions(project="gsd-lite")
    # Returns: 200 sessions (6 months organic + 5 deliberate eval runs)
    # Result: Evaluation metrics corrupted by debugging sessions, abandoned spikes, etc.

##### 2.3 The Solution: Compound Session Identity

    # Correct approach: compound key
    Session_Identity = (project_path, time_window)

    parser.get_sessions(
        project="gsd-lite",
        date="2026-02-14",
        time_range=("14:02", "14:38")  # Only this evaluation run
    )
    # Returns: 8 sessions from deliberate test scenario

#### 3. The Architecture: Stream-First at Every Layer

##### 3.1 Why Streaming Matters

The Danger: OpenCode stores thousands of sessions in `~/.local/share/opencode/storage/`. Naive loading causes OOM:

    # ❌ DANGEROUS: Load all sessions to memory
    all_sessions = [json.load(f) for f in glob("session/global/*.json")]  # 💥 OOM

The Storage Structure (from LOG-034, lines 5726-5878):

Artifact | Path Pattern | Size Per Item
--- | --- | ---
Session | `session/global/<sessionID>.json` | ~1KB (metadata only)
Message | `message/<sessionID>/<messageID>.json` | ~1-10KB
Part | `part/<messageID>/<partID>.json` | ~1-100KB
Tool Output | `tool-output/<hash>.txt` | ~1KB - 10MB (unbounded)

Critical Insight: A single evaluation run might touch 10 sessions, but the storage contains 1000+ sessions. Loading all would consume gigabytes.

##### 3.2 The Streaming Protocol

Principle: Both `discover` and `extract` phases use generators. Never hold full session content in memory.

    flowchart TD
        subgraph DISCOVER["Phase 1: Discover (Lightweight)"]
            D1[Stream session files] --> D2{Date matches?}
            D2 -->|No| D1
            D2 -->|Yes| D3{Project matches?}
            D3 -->|No| D1
            D3 -->|Yes| D4[Yield metadata only]
            D4 --> D5[Build date index]
        end

        subgraph EXTRACT["Phase 2: Extract (Full Content)"]
            E1[User selects partition] --> E2[Stream filtered sessions]
            E2 --> E3[Load messages lazily]
            E3 --> E4[Yield full trajectory]
            E4 --> E5[Write to output file]
        end

        D5 --> E1

##### 3.3 Memory Footprint Comparison

Approach | Sessions Loaded | Memory Usage
--- | --- | ---
Naive (load all) | 1000 | ~10GB (with tool outputs)
Stream + filter | 10 (matching) | ~100MB
Stream + metadata only | 1000 (headers) | ~1MB

#### 4. Implementation Specification

##### 4.1 Discovery Phase (Partition Picker)

    from collections import defaultdict
    from pathlib import Path
    import json
    from datetime import datetime
    from typing import Generator, Dict, List

    def discover_partitions(
        project_path: str,
        date: str = "today"
    ) -> Dict[str, List[dict]]:
        """
        Stream all sessions, filter by date and project, return lightweight index.

        Memory-safe: Only session metadata (ID, timestamp) held in memory.
        Never loads message content or tool outputs.

        Args:
            project_path: Absolute path to project root (e.g., "/Users/x/dev/gsd_lite")
            date: ISO date string or "today"

        Returns:
            Dict mapping date -> list of session metadata

        Example:
            >>> discover_partitions("/Users/x/dev/gsd_lite", "2026-02-14")
            {
                "2026-02-14": [
                    {"session_id": "abc123", "created": "2026-02-14T09:15:00Z", "file": "..."},
                    {"session_id": "def456", "created": "2026-02-14T09:23:00Z", "file": "..."},
                ]
            }
        """
        storage_root = Path.home() / ".local/share/opencode/storage"
        target_date = date if date != "today" else datetime.now().strftime("%Y-%m-%d")

        date_index = defaultdict(list)

        # Stream session files — never load all at once
        for session_file in (storage_root / "session/global").glob("*.json"):
            # Step 1: Load ONLY metadata (~1KB per session)
            with open(session_file) as f:
                meta = json.load(f)

            # Step 2: Early filter by date (cheapest check, eliminates 99%)
            session_date = meta["created"][:10]  # "2026-02-14T09:15:00Z" -> "2026-02-14"
            if session_date != target_date:
                continue

            # Step 3: Fingerprint check (see LOG-038 Section 6)
            if not _matches_project(session_file, project_path, storage_root):
                continue

            # Step 4: Add to index (metadata only, not content)
            date_index[session_date].append({
                "session_id": meta["id"],
                "created": meta["created"],
                "file": str(session_file)
            })

        return dict(date_index)

    def _matches_project(session_file: Path, target_project: str, storage_root: Path) -> bool:
        """
        Fingerprint session by scanning tool call outputs for project paths.

        Priority chain (from LOG-038 Section 6):
        1. list_allowed_directories output -> first path
        2. grep_content output -> regex extract
        3. read_files output -> regex extract

        Returns True if any tool output contains target_project path.
        """
        session_id = session_file.stem
        message_dir = storage_root / "message" / session_id

        if not message_dir.exists():
            return False

        # Stream messages, don't load all
        for msg_file in message_dir.glob("*.json"):
            with open(msg_file) as f:
                msg = json.load(f)

            # Only check model messages (have tool calls)
            if msg.get("role") != "model":
                continue

            # Check parts for tool outputs containing project path
            part_dir = storage_root / "part" / msg["id"]
            if not part_dir.exists():
                continue

            for part_file in part_dir.glob("*.json"):
                with open(part_file) as f:
                    part = json.load(f)

                if part.get("type") != "tool":
                    continue

                # Check if output contains project path
                output = part.get("state", {}).get("output", "")
                if target_project in output:
                    return True

        return False

##### 4.2 Helper Script UX (User-Facing)

    # Discover available partitions for today
    $ python eval_helper.py discover --project /Users/x/dev/gsd_lite --date today

    📅 Evaluation Candidates for 2026-02-14 (gsd-lite)
    ──────────────────────────────────────────────────
      [1] 09:15 - 09:47  (12 sessions, 34 tool calls)
      [2] 14:02 - 14:38  (8 sessions, 21 tool calls)
      [3] 16:45 - 17:12  (5 sessions, 15 tool calls)

    Select partition to evaluate [1-3, or 'all']: 2

    ✅ Selected partition 2 (14:02 - 14:38)
       Output will be written to: eval_run_2026-02-14_1402.json

##### 4.3 Extraction Phase (Vertex Format Output)

    def extract_partition(
        partition: List[dict],
        output_path: str
    ) -> None:
        """
        Stream sessions from partition, extract full trajectories, write to file.

        Memory-safe: Processes one session at a time, writes incrementally.
        Output format: Vertex AI Gen AI Evaluation compatible JSON.

        Args:
            partition: List of session metadata from discover_partitions()
            output_path: Path to write output JSON
        """
        storage_root = Path.home() / ".local/share/opencode/storage"

        with open(output_path, 'w') as out:
            out.write('[\n')
            first = True

            for session_meta in partition:
                if not first:
                    out.write(',\n')
                first = False

                # Load full session (now safe — we know it's in our partition)
                trajectory = _extract_trajectory(session_meta, storage_root)
                json.dump(trajectory, out, indent=2)

            out.write('\n]')

    def _extract_trajectory(session_meta: dict, storage_root: Path) -> dict:
        """
        Extract full trajectory from a single session.

        Output format (Vertex AI compatible, from LOG-038 Section 5):
        {
            "prompt": "User instruction text",
            "response": "Agent response text",
            "generated_trajectory": [
                {"tool": "fs.grep", "args": {...}, "output": "..."},
                {"tool": "fs.read", "args": {...}, "output": "..."}
            ]
        }
        """
        # Implementation follows LOG-038 Section 5 schema
        # ... (see LOG-038 for full schema details)
        pass

##### 4.4 Complete Workflow

    sequenceDiagram
        participant User
        participant OpenCode
        participant EvalHelper
        participant VertexAI

        Note over User,OpenCode: Phase 1: Run Test Scenarios
        User->>OpenCode: Start session
        User->>OpenCode: Execute test prompts
        User->>OpenCode: Exit when done

        Note over User,EvalHelper: Phase 2: Discover Partitions
        User->>EvalHelper: discover --project gsd-lite --date today
        EvalHelper->>EvalHelper: Stream sessions (memory-safe)
        EvalHelper->>EvalHelper: Filter by date FIRST
        EvalHelper->>EvalHelper: Filter by project SECOND
        EvalHelper-->>User: Show partition options

        Note over User,EvalHelper: Phase 3: Extract
        User->>EvalHelper: Select partition [2]
        EvalHelper->>EvalHelper: Stream selected sessions
        EvalHelper->>EvalHelper: Write eval_run_2026-02-14_1402.json
        EvalHelper-->>User: ✅ Output ready

        Note over User,VertexAI: Phase 4: Evaluate
        User->>VertexAI: Submit eval_run_2026-02-14_1402.json
        VertexAI-->>User: Evaluation scores

#### 5. Design Decisions Summary

Decision | Choice | Rationale
--- | --- | ---
Compound Identity | `project_path + time_window` | Project alone mixes organic/deliberate sessions
Stream Discovery | Generator over glob results | Prevents OOM on large storage directories
Stream Extraction | Process one session at a time | Same reason — memory safety
Filter Order | Date FIRST, then project | Date is O(1) string compare; project requires I/O
Output Format | One JSON file per partition | Clean audit trail, direct Vertex AI compatibility
Partition Detection | User-selected from discovered list | Automatic clustering adds complexity; manual is sufficient for v1

#### 6. Dependency Summary (For Future Agent Onboarding)

    graph TD
        LOG032["LOG-032: OpenCode Native JSON<br/>(Data source discovery)"] --> LOG038
        LOG033["LOG-033: fs-mcp Path Fingerprinting<br/>(Project identification)"] --> LOG038
        LOG038["LOG-038: Parser Specification<br/>(Schema + algorithm)"] --> LOG040
        LOG040["LOG-040: Time Partitioning<br/>(Session isolation + streaming)"]

        style LOG040 fill:#90EE90

To onboard this decision from scratch:

1. LOG-032 (lines 4895-5466): Understand why we use OpenCode's native JSON instead of custom instrumentation

2. LOG-033 (lines 5467-5725): Understand the fingerprinting problem (one OpenCode, many projects)

3. LOG-038 (lines 6089-6194): Understand the parser algorithm and output schema

4. LOG-040 (this entry): Understand time-based filtering and streaming architecture

#### 7. Open Questions (Deferred)

Question | Status | Notes
--- | --- | ---
Automatic partition clustering | DEFERRED | Could detect "runs" by time gaps; manual selection sufficient for v1
Multi-day evaluation batches | DEFERRED | Current design handles single-day; extend `--date-range` later
Partition metadata persistence | DEFERRED | Could save partition definitions for reproducibility

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-040 (Time-partitioned evaluation design approved)

→ Dependency chain: LOG-040 ← LOG-038 ← LOG-033 ← LOG-032

→ Next action: Implement `eval_helper.py` with discover + extract commands

Layer 2 — Global Context:

→ Architecture: OpenCode (Global) + fs-mcp (Project) → Parser → Vertex AI Eval

→ Patterns: Streaming generators for memory safety; compound session identity

→ Data Flow: `storage/session/global` → discover (filter) → extract (partition) → `eval_run_*.json`

Fork paths:

- Implement discover phase → Write `discover_partitions()` function

- Implement extract phase → Write `extract_partition()` function

- Implement CLI wrapper → Wire up `eval_helper.py` with argparse/typer

- Revisit LOG-038 → Ensure schema alignment with Vertex AI format

### [LOG-041] - [EXEC] - ~~eval_helper.py Implementation: Interactive Collect Workflow with Scoped Audit~~ [SUPERSEDED BY LOG-045] - Task: TASK-EVAL-001

Status: SUPERSEDED (See LOG-045 for SQLite migration)

Date: 2026-02-14

Decision IDs: DECISION-041a (Interactive Collect), DECISION-041b (--since UX), DECISION-041c (Scoped Audit)

Task: TASK-EVAL-001 (OpenCode Session Parser)

Dependencies:

- LOG-038: Parser specification (schema, algorithm) — lines 6089-6194

- LOG-040: Time partitioning design (streaming, compound identity) — lines 6259-6699

#### 1. Executive Summary

What we built: A fully functional `eval_helper.py` CLI tool that extracts OpenCode session data into Vertex AI-compatible evaluation datasets.

> UPDATE (LOG-045): This implementation relied on OpenCode's JSON file storage (`~/.local/share/opencode/storage/session`). OpenCode migrated to SQLite (`opencode.db`), rendering this file-based parser obsolete. See LOG-045 for the new `sqlmodel`-based implementation.

The key innovation: An interactive `collect` command that guides users through the entire workflow: `projects → discover → audit → extract`. No memorizing partition indices or juggling multiple commands.

First successful extraction: `eval_run_2026-02-14_2119.json` — 4 sessions, 152 tool calls, ready for evaluation.

#### 2. The Problem We Solved

The original design (LOG-040) specified separate commands (`discover`, `audit`, `extract`) with numeric partition indices. This had UX problems:

    # Old UX (from LOG-040 design)
    python eval_helper.py discover --project ... --date 2026-02-14
    # Output: [1] 09:15-09:47, [2] 14:02-14:38

    python eval_helper.py extract --project ... --date 2026-02-14 --partition 2
    # Problem: What was partition 2 again? User must remember or re-run discover.

Three UX gaps identified:

1. `--date` requires typing ISO dates when 90% of use cases are "the session I just finished"

2. `--partition N` is opaque — user doesn't know what "1" means without running `discover` first

3. `audit` was global (all sessions) instead of scoped to the evaluation partition

#### 3. Decisions Made

##### DECISION-041a: Interactive `collect` Command

Choice: Single interactive command that combines discover → audit → extract.

Rationale: The evaluation workflow is inherently sequential. Breaking it into separate commands adds friction without flexibility benefits.

Implementation:

    # scripts/eval_helper.py, cmd_collect() function
    def cmd_collect(args):
        # Step 1: Discover sessions
        sessions = discover_sessions(project, since)

        # Step 2: Detect partitions (group by time gaps)
        partitions = detect_partitions(sessions, gap_minutes=gap)

        # Step 3: User selects partition (interactive prompt)
        if len(partitions) == 1:
            selection = "1"  # Auto-select if only one
        else:
            selection = input(f"Select partition [1-{len(partitions)}, or 'all']: ")

        # Step 4: Audit tools (scoped to selected partition)
        tool_counts, unmapped_tools = audit_partition(selected_sessions)
        print_audit_results(tool_counts, unmapped_tools)

        # Step 5: Extract (with confirmation)
        proceed = input("Proceed to extract? [Y/n]: ")
        if proceed != "n":
            extract_partition(selected_sessions, output_file)

Source: `scripts/eval_helper.py` lines 622-720 (cmd_collect function)

##### DECISION-041b: `--since` Instead of `--date`

Choice: Replace `--date 2026-02-14` with `--since 1h` (default).

Rationale: 90% of evaluations happen immediately after the session ends. "Last hour" is the natural mental model.

Supported formats:

Input | Meaning | Use Case
--- | --- | ---
`30m` | Last 30 minutes | Just finished a quick test
`1h` | Last 1 hour (default) | Standard evaluation run
`2h` | Last 2 hours | Longer session
`today` | Since midnight | Multiple runs today
`2026-02-14` | Specific date | Historical analysis

Implementation:

    # scripts/eval_helper.py, parse_since() function
    def parse_since(since_str: str) -> datetime:
        """Parse --since argument into a datetime cutoff."""
        now = datetime.now()

        if since_str.endswith("m"):
            minutes = int(since_str[:-1])
            return now - timedelta(minutes=minutes)

        if since_str.endswith("h"):
            hours = int(since_str[:-1])
            return now - timedelta(hours=hours)

        if since_str == "today":
            return now.replace(hour=0, minute=0, second=0, microsecond=0)

        # Fallback: ISO date
        return datetime.strptime(since_str, "%Y-%m-%d")

Source: `scripts/eval_helper.py` lines 331-375 (parse_since function)

##### DECISION-041c: Scoped Audit

Choice: `audit` command is scoped to `--project` + `--since` + optional `--partition`.

Rationale: Global audit (all sessions ever) is noise. We only care about tools used in the specific evaluation partition we're about to extract.

Before (global):

    python eval_helper.py audit
    # Scans ALL 276 sessions across ALL projects
    # Returns: 50 tools, 12 unmapped
    # Problem: Most are irrelevant to current evaluation

After (scoped):

    python eval_helper.py audit --project /Users/x/dev/gsd_lite --partition 1
    # Scans only 4 sessions in partition 1 of gsd_lite project
    # Returns: 8 tools, 0 unmapped
    # Actionable: These are exactly the tools in your eval dataset

Implementation:

    # scripts/eval_helper.py, audit_partition() function
    def audit_partition(sessions: List[SessionMeta]) -> Tuple[Dict[str, int], Set[str]]:
        """Audit tool usage within a specific partition."""
        config = get_config()
        tool_counts: Counter = Counter()
        unmapped_tools: Set[str] = set()

        for session in sessions:  # Only iterate selected sessions
            # ... extract tool calls from session ...
            if not config.is_known_tool(tool_name):
                unmapped_tools.add(tool_name)

        return dict(tool_counts), unmapped_tools

Source: `scripts/eval_helper.py` lines 618-665 (audit_partition function)

#### 4. What "Partition" Means

Definition: A partition is a group of sessions clustered by time proximity.

The problem it solves: Without partitioning, `--since 2h` might capture multiple evaluation runs mixed together:

    flowchart LR
        subgraph TIME["Timeline (last 2 hours)"]
            S1[10:00<br/>Session A] --> S2[10:05<br/>Session B] --> S3[10:10<br/>Session C]
            S3 --> GAP["☕ 45 min gap"]
            GAP --> S4[10:55<br/>Session D] --> S5[11:00<br/>Session E]
        end

        subgraph PART["Detected Partitions"]
            P1["Partition 1<br/>10:00-10:10<br/>(3 sessions)"]
            P2["Partition 2<br/>10:55-11:00<br/>(2 sessions)"]
        end

        S1 & S2 & S3 -.-> P1
        S4 & S5 -.-> P2

Algorithm: Sessions with >30 minute gaps between them are split into separate partitions.

Source: `scripts/eval_helper.py` lines 445-475 (detect_partitions function)

#### 5. Output Schema Validation

First successful extraction: `eval_run_2026-02-14_2119.json`

    // Schema verified via jq queries
    [
      {
        "session_id": "ses_3a37b0387ffe36zjDtyt0RUx82",
        "created": "2026-02-14T21:19:05",
        "prompt": "# Progress Workflow\n\n[SYSTEM: PROGRESS MODE...",
        "response": "GSD-Lite structure exists. Let me discover...",
        "generated_trajectory": [
          {
            "tool": "fs.list",           // Normalized logical name
            "tool_raw": "tools_gsd-lite-fs_list_directory_with_sizes",  // Physical name
            "args": {"path": "."},
            "output": "[DIR] .claude..."
          }
        ]
      }
    ]

Statistics from first extraction:

Metric | Value
--- | ---
Sessions | 4
Total tool calls | 152 (8 + 45 + 44 + 55)
File size | ~108k tokens
Time range | 21:19 - 21:41 (22 minutes)

#### 6. Complete CLI Reference

    # Step 1: Find your project path
    python scripts/eval_helper.py projects
    # Output: List of projects sorted by last activity

    # Step 2: Interactive collect (RECOMMENDED)
    python scripts/eval_helper.py collect --project /path/to/project
    # Guides you through: discover → select partition → audit → extract

    # Non-interactive commands (for scripting):
    python scripts/eval_helper.py discover --project /path --since 2h
    python scripts/eval_helper.py audit --project /path --partition 1
    python scripts/eval_helper.py extract --project /path --partition 1 --output my_eval.json

    # Debugging:
    python scripts/eval_helper.py inspect --session-id ses_abc123

#### 7. File Inventory

File | Purpose | Lines
--- | --- | ---
`scripts/eval_helper.py` | Main CLI tool | ~1050
`scripts/eval_config.yaml` | Tool name mappings | ~120
`eval_run_*.json` | Extracted evaluation datasets | Variable

#### 8. Dependency Graph

    graph TD
        LOG032["LOG-032: OpenCode Native JSON<br/>(Data source discovery)"] --> LOG033
        LOG033["LOG-033: fs-mcp Path Fingerprinting<br/>(Project identification)"] --> LOG038
        LOG035["LOG-035: Config-Driven Tool Mapping<br/>(YAML normalization)"] --> LOG041
        LOG038["LOG-038: Parser Specification<br/>(Schema + algorithm)"] --> LOG040
        LOG040["LOG-040: Time Partitioning<br/>(Streaming + compound identity)"] --> LOG041
        LOG041["LOG-041: eval_helper.py Implementation<br/>(Interactive collect workflow)"]

        style LOG041 fill:#90EE90

To onboard this implementation from scratch:

1. LOG-032 (lines 4895-5466): Why OpenCode native JSON as data source

2. LOG-033 (lines 5467-5725): The fingerprinting problem and solution

3. LOG-035 (lines 5879-5961): Why YAML config for tool normalization

4. LOG-038 (lines 6089-6194): Parser algorithm and output schema

5. LOG-040 (lines 6259-6699): Time partitioning and streaming design

6. LOG-041 (this entry): Final implementation with UX refinements

#### 9. Open Questions (For Next Session)

Question | Status | Notes
--- | --- | ---
Vertex AI schema compatibility | OPEN | Current schema has `prompt`, `response`, `generated_trajectory`. Does Vertex expect different field names?
Schema field `reference_trajectory` | UNKNOWN | Do we need a "golden" trajectory for comparison?
Multi-turn conversation handling | DEFERRED | Current schema flattens all prompts/responses into single strings

### [LOG-042] - [DECISION] - Constitutional Evaluation Architecture: Session-as-Unit with Hybrid Orchestration via Promptfoo - Task: TASK-EVAL-002

Status: APPROVED

Date: 2026-02-14

Decision IDs: DECISION-042a (Session as Evaluation Unit), DECISION-042b (Vertex AI Superseded by Promptfoo), DECISION-042c (Hybrid Orchestration — Option C), DECISION-042d (Turn-Structured Output Schema)

Task: TASK-EVAL-002 (Constitutional Evaluation Pipeline)

Supersedes: DECISION-032b (Vertex AI Gen AI Eval as primary judge)

Dependencies:

- LOG-028: CI Framework Design (lines 4027-4333) — defines 3-layer CI architecture and Six Pillars

- LOG-030: CONSTITUTION.md v0.1 (lines 4745-4810) — the Four Pillars being evaluated

- LOG-031: pair-programming.yaml Rubric (lines 4811-4894) — existing rubric format (P2-H1 to P2-H5)

- LOG-032: OpenCode Goldmine (lines 4895-5466) — original platform research and Vertex AI decision

- LOG-041: eval_helper.py Implementation (lines 6677-EOF) — current extraction tool being refactored

#### 1. Executive Summary

What we decided: The Constitutional Evaluation Pipeline will use individual sessions as the evaluation unit, orchestrated via Promptfoo's llm-rubric assertions, with a hybrid architecture that separates deterministic checks (programmatic) from qualitative checks (LLM-as-judge).

Why Vertex AI was superseded: Vertex AI's trajectory evaluation requires a `reference_trajectory` (golden path) for comparison. GSD-Lite's Constitution doesn't define "correct tool sequences" — it defines behavioral patterns like "grep before read" and "ask why before executing." These are qualitative compliance checks, not sequence matching.

The One-Liner: Evaluate each session independently against the Constitution using Promptfoo, with batch extraction and aggregated reporting.

#### 2. The Problem: Vertex AI Trajectory Metrics Don't Fit Constitutional Compliance

##### 2.1 The Original Decision (LOG-032, DECISION-032b)

LOG-032 selected Vertex AI Gen AI Evaluation based on:

> "Bring Your Own Data" model — works with pre-captured data. Supports trajectory evaluation and adaptive rubrics.

Citation: LOG-032, lines 5190-5215 (DECISION-032b rationale)

##### 2.2 The Gap Discovered

When we researched Vertex AI's actual trajectory evaluation capabilities (this session), we found:

Vertex AI Feature | What We Assumed | What It Actually Does
--- | --- | ---
`trajectory_exact_match` | Could verify "grep before read" | Compares `generated_trajectory` vs `reference_trajectory`
`trajectory_precision` | Could score tool efficiency | Measures overlap with golden path
`trajectory_in_order_match` | Could check behavioral patterns | Requires expected sequence to compare against

Citation: Vertex AI Gen AI Evaluation SDK documentation, accessed 2026-02-14 via `mcp_tools_mmcp_google_grounding__search_documentation`. Key finding:

> "For trajectory evaluation, your dataset should include `reference_trajectory`: The expected sequence of actions (tool calls) the agent should take to achieve its goal."

The fundamental mismatch: GSD-Lite's Constitution defines qualitative behaviors, not expected tool sequences:

    # What Vertex AI expects (sequence matching)
    reference_trajectory: ["search_flights", "select_flight", "book"]
    generated_trajectory: ["search_flights", "book"]  # Missing step!
    metric: trajectory_in_order_match = 0.66

    # What GSD-Lite needs (behavioral compliance)
    constitution_behavior: "Agent must grep before reading full files"
    session_trajectory: [fs.list, fs.grep, fs.read, fs.edit]
    evaluation: "Did fs.grep precede fs.read? → YES → PASS"

##### 2.3 Decision: Supersede DECISION-032b

DECISION-042b: Vertex AI Gen AI Evaluation is superseded as the primary evaluation backend. Promptfoo with `llm-rubric` assertions becomes the new primary platform.

Rationale:

1. Promptfoo's `llm-rubric` is designed for freeform criteria evaluation

2. Our `pair-programming.yaml` rubric is already YAML-native (matches Promptfoo format)

3. No vendor lock-in — can use any LLM (Gemini, Claude, GPT) as judge

4. We now have the data extraction (eval_helper.py) that LOG-032 said was missing for Promptfoo

Citation: LOG-032 framework comparison table (lines 4919-4926):

> "| Promptfoo | Custom provider functions or manual YAML | ⚠️ Requires custom harness |"

The "custom harness" is now built: `eval_helper.py` extracts sessions, satisfying Promptfoo's data input requirement.

#### 3. The Evaluation Unit Question: Why Session, Not Turn or Multi-Session?

##### 3.1 GSD-Lite's Stateless Architecture

GSD-Lite is designed for stateless, cross-session work. Each session is a fresh agent context that:

1. Runs Universal Onboarding (reads PROJECT, ARCHITECTURE, WORK.md)

2. Performs the user's requested work

3. Ends with a STATELESS HANDOFF packet

Key insight: The "repeated onboarding" across sessions is correct behavior, not noise. An agent that skips onboarding in Session 2 would be violating Pillar C3 (Context Engineering).

##### 3.2 Three Options Evaluated

Option | Unit | Pros | Cons
--- | --- | --- | ---
Single Turn | One user→agent exchange | Fine-grained | S1 (Handoff) only at session end; J4 (Log quality) spans turns
Single Session | All turns in one session | Matches GSD-Lite's stateless design; handoff visible | Requires turn structure for P2-H1 (Why Before How)
Multi-Session Sequence | Sessions 1+2+3 as partition | N/A | Sessions are intentionally independent; cross-session continuity is via artifacts, not memory

##### 3.3 Decision: Session as Evaluation Unit

DECISION-042a: The evaluation unit is the individual session. Each session is evaluated independently against the Constitution.

Rationale:

1. Handoff is session-scoped: S1-H1 (STATELESS HANDOFF) only makes sense at session end

2. Onboarding is session-scoped: C3-H2 (Universal Onboarding) should happen once per session

3. Stateless by design: Cross-session correlation is via artifacts (WORK.md), not conversation memory

4. Multi-turn behaviors handled: LLM-as-judge prompt scans full session transcript for per-turn violations

Example: For behavior P2-H1 (Why Before How), the rubric prompt receives the full session transcript and identifies each user→agent exchange, evaluating whether the agent asked "why" before executing.

    # Conceptual rubric prompt for P2-H1
    evaluation_prompt: |
      SESSION TRANSCRIPT:
      {{session_transcript}}

      TASK:
      1. Identify each USER REQUEST that implies an action
      2. For each, check if agent asked WHY or stated understanding before acting
      3. EXEMPT: Universal Onboarding sequence (reading PROJECT, ARCH, WORK)

      SCORE: 0 if ANY violation, 1 if ALL compliant

#### 4. The Orchestration Question: How to Handle Multiple Sessions?

##### 4.1 Three Options Evaluated

    flowchart TB
        subgraph "Option A: Batch All"
            A1["5 sessions"] --> A2["One eval_run.json"]
            A2 --> A3["One Promptfoo run"]
            A3 --> A4["One mixed report"]
        end

        subgraph "Option B: User Selects One"
            B1["5 sessions"] --> B2["User picks session 3"]
            B2 --> B3["One Promptfoo run"]
            B3 --> B4["One clean report"]
        end

        subgraph "Option C: Hybrid (SELECTED)"
            C1["5 sessions"] --> C2["5 individual JSON files"]
            C2 --> C3["Loop: 5 Promptfoo runs"]
            C3 --> C4["5 individual reports"]
            C4 --> C5["1 aggregated summary"]
        end

Option | Description | Pros | Cons
--- | --- | --- | ---
A: Batch | All sessions → one eval file → one run | Simple, efficient | Can't re-run one; noisy failures
B: User Selects | User picks one session to eval | Surgical debugging | No aggregated view; tedious for many
C: Hybrid | Extract all → eval each → aggregate | Best of both; re-runnable; CI-friendly | More Promptfoo invocations

##### 4.2 Decision: Option C (Hybrid Orchestration)

DECISION-042c: Use hybrid orchestration — batch extract to individual files, evaluate each session independently, aggregate into summary report.

Rationale:

1. Matches user workflow: Run session → check compliance → fix protocol → re-run same session

2. CI-friendly: Exit code based on aggregate pass rate; can gate PRs on "golden sessions must pass"

3. Debuggable: Each session's report stored separately; failures traceable to specific turn

4. Re-runnable: `eval_helper.py evaluate --session ses_xxx` for surgical debugging

#### 5. Proposed CLI UX

##### 5.1 Extract Command (Refactored)

    # Extract sessions to individual files (one per session)
    $ python eval_helper.py collect --since 2h --output-dir ./eval_sessions/

    Extracting sessions from last 2 hours...
    Found 5 sessions in 1 partition.

    Extracted:
      ./eval_sessions/ses_abc123.json (8 turns, 12 tools)
      ./eval_sessions/ses_def456.json (15 turns, 45 tools)
      ./eval_sessions/ses_ghi789.json (6 turns, 18 tools)
      ./eval_sessions/ses_jkl012.json (4 turns, 8 tools)
      ./eval_sessions/ses_mno345.json (10 turns, 32 tools)

##### 5.2 Evaluate Command (New)

    # Evaluate ALL sessions in directory
    $ python eval_helper.py evaluate --dir ./eval_sessions/

    Evaluating 5 sessions against Constitution...

    [1/5] ses_abc123... ✅ PASS (S1:1.0 P2:1.0 C3:1.0 J4:1.0)
    [2/5] ses_def456... ❌ FAIL (S1:1.0 P2:0.6 C3:1.0 J4:0.8)
    [3/5] ses_ghi789... ❌ FAIL (S1:1.0 P2:1.0 C3:0.5 J4:1.0)
    [4/5] ses_jkl012... ❌ FAIL (S1:0.0 P2:1.0 C3:1.0 J4:1.0)
    [5/5] ses_mno345... ✅ PASS (S1:1.0 P2:1.0 C3:1.0 J4:1.0)

    SUMMARY: 2/5 sessions passed (40%)
    Report: ./eval_sessions/report.json

    # Evaluate ONE session (for debugging)
    $ python eval_helper.py evaluate --session ./eval_sessions/ses_def456.json

    Evaluating ses_def456 against Constitution...

    S1 Stateless-First:     ✅ 1.0
      S1-H1 Handoff:        ✅ PASS

    P2 Pair Programming:    ❌ 0.6
      P2-H1 Why Before How: ✅ PASS
      P2-H2 Grounding Loop: ✅ PASS
      P2-H3 Challenge Tone: ❌ FAIL — Turn 4: User said "make it fast", agent did not probe
      P2-H4 Teaching Offer: ✅ PASS
      P2-H5 No Auto-Write:  ❌ FAIL — Turn 7: Wrote to WORK.md without asking

    C3 Context Engineering: ✅ 1.0
      C3-H1 Grep First:     ✅ PASS
      C3-H2 Onboarding:     ✅ PASS

    J4 Journalism Quality:  ⚠️ 0.8
      J4-H1 Log Format:     ✅ PASS
      J4-H2 Narrative:      ⚠️ PARTIAL — LOG-042 missing analogy/example

#### 6. Output Schema Refactor

##### 6.1 Current Schema (Session-Level, Flat)

From LOG-041 implementation (`eval_run_2026-02-14_2119.json`):

    {
      "session_id": "ses_def456",
      "created": "2026-02-14T21:30:00",
      "prompt": "...all user messages concatenated...",
      "response": "...all agent messages concatenated...",
      "generated_trajectory": [
        {"tool": "fs.grep", "tool_raw": "mcp_tools_gsd-lite-fs_grep_content", "args": {...}, "output": "..."},
        {"tool": "fs.read", "tool_raw": "mcp_tools_gsd-lite-fs_read_files", "args": {...}, "output": "..."}
      ]
    }

Problems:

1. No turn boundaries — can't tell which tools answered which user message

2. LLM-as-judge can't evaluate "did agent ask why before THIS action"

3. `pair-programming.yaml` expects `user_input` and `agent_response` as separate fields

##### 6.2 Proposed Schema (Session-Level, Turn-Structured)

DECISION-042d: Refactor eval_helper.py output to include turn-level structure while preserving session-level aggregates.

    {
      "session_id": "ses_def456",
      "created": "2026-02-14T21:30:00",
      "project": "/Users/luutuankiet/dev/gsd_lite",

      "turns": [
        {
          "turn": 1,
          "role": "user",
          "content": "discuss log-001"
        },
        {
          "turn": 2,
          "role": "agent",
          "content": "I'll review the context first...\n\n[reads files]\n\nBased on LOG-001, here's my understanding...",
          "tools": [
            {
              "tool": "fs.grep",
              "tool_raw": "mcp_tools_gsd-lite-fs_grep_content",
              "args": {"pattern": "\\[LOG-001\\]", "search_path": "gsd-lite/WORK.md"},
              "output": "File: gsd-lite/WORK.md, Line: 102..."
            },
            {
              "tool": "fs.read",
              "tool_raw": "mcp_tools_gsd-lite-fs_read_files",
              "args": {"files": [{"path": "gsd-lite/WORK.md", "start_line": 102, "end_line": 212}]},
              "output": "### [LOG-001] - [DISCOVERY]..."
            }
          ]
        },
        {
          "turn": 3,
          "role": "user",
          "content": "write log-002 about our findings"
        },
        {
          "turn": 4,
          "role": "agent",
          "content": "I'll write LOG-002. Before I do — what aspect do you want to emphasize: the technical finding or the decision rationale?\n\n📦 STATELESS HANDOFF\n...",
          "tools": []
        }
      ],

      "full_transcript": "USER: discuss log-001\n\nAGENT: I'll review the context first...\n\nUSER: write log-002 about our findings\n\nAGENT: I'll write LOG-002. Before I do...",

      "tool_trajectory": [
        {"tool": "fs.grep", "turn": 2, "args": {...}, "output": "..."},
        {"tool": "fs.read", "turn": 2, "args": {...}, "output": "..."}
      ],

      "metadata": {
        "total_turns": 4,
        "total_tools": 2,
        "duration_seconds": 145
      }
    }

Key additions:

Field | Purpose | Consumer
--- | --- | ---
`turns[]` | Structured turn-by-turn with role | LLM-as-judge (P2-H1, P2-H3)
`turns[].tools[]` | Which tools used in each turn | Correlate tool to conversation
`tool_trajectory[].turn` | Links tools back to turn number | C3-H1 (grep-before-read) with context
`full_transcript` | Flat string for simple matching | S1-H1 (grep for handoff pattern)
`metadata` | Session stats | Reporting, filtering

#### 7. Evaluation Pipeline Architecture

    flowchart TB
        subgraph "Phase 1: Extract"
            OC["OpenCode Storage<br/>~/.local/share/opencode/"] --> EH["eval_helper.py collect"]
            EH --> DIR["./eval_sessions/<br/>├── ses_abc123.json<br/>├── ses_def456.json<br/>└── ..."]
        end

        subgraph "Phase 2: Evaluate"
            DIR --> LOOP["For each session:"]

            subgraph "Layer 1: Deterministic"
                LOOP --> D1["S1-H1: grep 'STATELESS HANDOFF'"]
                LOOP --> D2["C3-H1: check grep→read pattern"]
                LOOP --> D3["C3-H2: verify onboarding sequence"]
            end

            subgraph "Layer 2: LLM-as-Judge (Promptfoo)"
                LOOP --> L1["P2-H1: Why Before How"]
                LOOP --> L2["P2-H2: Grounding Loop"]
                LOOP --> L3["P2-H3: Challenge Tone"]
                LOOP --> L4["J4-H1: Log Quality"]
            end

            D1 & D2 & D3 --> SR["Session Report<br/>ses_xxx_report.json"]
            L1 & L2 & L3 & L4 --> SR
        end

        subgraph "Phase 3: Aggregate"
            SR --> AGG["Aggregate Report<br/>report.json"]
            AGG --> CI["CI Gate<br/>exit 0 if pass rate >= threshold"]
        end

##### 7.1 Layer 1: Deterministic Checks (Programmatic)

These behaviors can be evaluated without LLM:

Behavior | Check | Implementation
--- | --- | ---
S1-H1 | Handoff present | `"📦 STATELESS HANDOFF" in turns[-1].content`
C3-H1 | Grep before read | Scan `tool_trajectory`: for each `fs.read`, verify preceding `fs.grep` covered same path
C3-H2 | Onboarding sequence | First agent turn reads PROJECT.md, ARCHITECTURE.md, WORK.md

Example: C3-H1 Check (Python)

    def check_grep_before_read(session: dict) -> tuple[bool, str]:
        """
        C3-H1: Agent uses grep-first pattern.

        PASS: Every fs.read is preceded by fs.grep on same/parent path
        FAIL: fs.read without preceding fs.grep
        """
        grepped_paths = set()

        for call in session["tool_trajectory"]:
            if call["tool"] == "fs.grep":
                search_path = call["args"].get("search_path", ".")
                grepped_paths.add(search_path)

            if call["tool"] == "fs.read":
                for file_req in call["args"].get("files", []):
                    file_path = file_req.get("path", "")
                    # Check if file is under a grepped directory
                    if not any(file_path.startswith(gp) or gp == "." for gp in grepped_paths):
                        return False, f"Turn {call['turn']}: Read '{file_path}' without grep-first"

        return True, "Grep-first pattern followed"

##### 7.2 Layer 2: LLM-as-Judge (Promptfoo)

These behaviors require qualitative judgment:

Behavior | What It Checks | Rubric File
--- | --- | ---
P2-H1 | Agent asks "why" before executing | `pair-programming.yaml` (exists)
P2-H2 | Agent follows Search→Echo→Verify loop | `pair-programming.yaml` (exists)
P2-H3 | Agent challenges vague requirements | `pair-programming.yaml` (exists)
P2-H4 | Agent offers teaching detours | `pair-programming.yaml` (exists)
P2-H5 | Agent asks before writing artifacts | `pair-programming.yaml` (exists)
J4-H1 | Logs have narrative + code + backlinks | `journalism.yaml` (to be created)

Citation: Existing rubric at `src/gsd_lite/template/constitution/rubrics/pair-programming.yaml`, behaviors P2-H1 through P2-H5 with full `evaluation_steps`, `scoring`, `violation_examples`, and `compliance_examples`.

#### 8. Why This Preserves GSD-Lite's Design

GSD-Lite Principle | How This Preserves It
--- | ---
Stateless sessions | Each session evaluated independently; no cross-session memory assumed
Universal Onboarding | Onboarding is EXPECTED in every session; evaluated as C3-H2 compliance
Fork & Resume | Evaluation happens on captured data; doesn't interfere with /fork workflow
Artifacts as memory | Cross-session continuity via WORK.md, not conversation; evaluation respects this
Pair programming | P2-H* behaviors directly evaluate the Driver/Navigator dynamic

#### 9. Implementation Tasks

Task ID | Description | Depends On | Est. Effort
--- | --- | --- | ---
TASK-EVAL-002a | Refactor eval_helper.py output to turn-structured schema | LOG-042 | 3h
TASK-EVAL-002b | Implement `--output-dir` flag for per-session extraction | TASK-EVAL-002a | 1h
TASK-EVAL-002c | Implement Layer 1 deterministic checks (S1-H1, C3-H1, C3-H2) | TASK-EVAL-002a | 2h
TASK-EVAL-002d | Create Promptfoo config for Layer 2 (`constitutional.yaml`) | LOG-042 | 2h
TASK-EVAL-002e | Implement `evaluate` command with Promptfoo integration | TASK-EVAL-002c, 002d | 3h
TASK-EVAL-002f | Implement aggregated report generation | TASK-EVAL-002e | 1h
TASK-CONST-002b | Write remaining rubrics (S1, C3, J4 pillars) | LOG-031 | 4h

#### 10. Dependency Graph

    graph TD
        LOG028["LOG-028: CI Framework Design<br/>(Six Pillars, 3-layer architecture)"] --> LOG030
        LOG028 --> LOG032

        LOG030["LOG-030: CONSTITUTION.md v0.1<br/>(Four Pillars extracted)"] --> LOG031

        LOG031["LOG-031: pair-programming.yaml<br/>(P2-H1 to P2-H5 rubric)"] --> LOG042

        LOG032["LOG-032: OpenCode Goldmine<br/>(Data source + Vertex AI decision)"] --> LOG041
        LOG032 --> LOG042

        LOG041["LOG-041: eval_helper.py<br/>(Interactive collect workflow)"] --> LOG042

        LOG042["LOG-042: Constitutional Evaluation Architecture<br/>(This entry: Session-as-unit, Promptfoo, Hybrid orchestration)"]

        style LOG042 fill:#90EE90
        style LOG032 fill:#FFB6C1

        LOG042 -.->|"Supersedes DECISION-032b"| LOG032

To onboard this decision from scratch:

1. LOG-028 (lines 4027-4333): Why CI framework; the Six Pillars; 3-layer architecture concept

2. LOG-030 (lines 4745-4810): Constitution v0.1 with Four Pillars distilled

3. LOG-031 (lines 4811-4894): The rubric format; P2-H1 to P2-H5 behaviors

4. LOG-032 (lines 4895-5466): Platform research; why Vertex AI was chosen (now superseded)

5. LOG-041 (lines 6677-EOF): Current eval_helper.py implementation being refactored

6. LOG-042 (this entry): Why Vertex AI doesn't fit; session-as-unit; Promptfoo; hybrid orchestration

#### 11. Open Questions (For Next Session)

Question | Status | Notes
--- | --- | ---
Promptfoo llm-rubric exact syntax | OPEN | Need to verify YAML format matches our rubric structure
Judge model selection | OPEN | Claude Sonnet 4 vs Gemini Flash for cost/quality tradeoff
CI integration | DEFERRED | GitHub Actions workflow for PR gates
Remaining rubrics | PENDING | S1, C3, J4 pillars need rubric files (TASK-CONST-002b)

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-042 (Constitutional Evaluation Architecture decision)

→ Dependency chain: LOG-042 ← LOG-041 ← LOG-032 ← LOG-028

→ Next action: TASK-EVAL-002a — Refactor eval_helper.py to turn-structured schema

Layer 2 — Global Context:

→ Architecture: eval_helper.py (extract) → per-session JSON → Promptfoo (Layer 2) + Python (Layer 1) → aggregate report

→ Patterns: Session = eval unit; deterministic + LLM-as-judge hybrid; Vertex AI superseded

→ Key decisions: DECISION-042a (session-as-unit), 042b (Promptfoo), 042c (Option C hybrid), 042d (turn-structured schema)

Fork paths:

- Implement schema refactor → TASK-EVAL-002a: Add turns[] structure to eval_helper.py output

- Research Promptfoo → Verify llm-rubric syntax matches pair-programming.yaml

- Write remaining rubrics → TASK-CONST-002b: S1, C3, J4 pillar rubrics

- Prototype manually → Run one session through Promptfoo by hand before building pipeline

### [LOG-043] - [DECISION] - Vertex AI Rubric-Based Evaluation: Hybrid Architecture with Adaptive Rubrics + Programmatic Checks - Task: TASK-EVAL-002

Status: APPROVED

Date: 2026-02-14

Decision IDs: DECISION-043a (Vertex AI Rubric-Based Metrics for L2), DECISION-043b (Hybrid Architecture: Programmatic L1 + Vertex L2), DECISION-043c (Constitution as Guidelines Parameter)

Task: TASK-EVAL-002 (Constitutional Evaluation Pipeline)

Supersedes: DECISION-042b (Promptfoo as primary platform) — Vertex AI rubric-based metrics now primary for Layer 2

Dependencies:

- LOG-028: CI Framework Design (lines 4038-4344) — defines 3-layer CI architecture and Six Pillars

- LOG-030: CONSTITUTION.md v0.1 (lines 4756-4821) — the Four Pillars being evaluated

- LOG-031: pair-programming.yaml Rubric (lines 4822-4905) — existing rubric format (P2-H1 to P2-H5)

- LOG-032: OpenCode Goldmine (lines 4906-5477) — original platform research, DECISION-032b (now double-superseded)

- LOG-041: eval_helper.py Implementation (lines 6688-6985) — current extraction tool

- LOG-042: Constitutional Evaluation Architecture (lines 6986-7493) — session-as-unit, hybrid orchestration (still valid), Promptfoo decision (now superseded)

#### 1. Executive Summary

What we decided: The Constitutional Evaluation Pipeline will use Vertex AI's rubric-based metrics (specifically `GENERAL_QUALITY` with custom `guidelines`) for Layer 2 qualitative evaluation, combined with programmatic Python checks for Layer 1 deterministic evaluation.

Why this supersedes DECISION-042b (Promptfoo):

1. User is a Google Cloud partner — hands-on Vertex AI experience has strategic value

2. Vertex AI's adaptive rubrics dynamically generate pass/fail tests per prompt — more intelligent than static Promptfoo rubrics

3. Vertex AI's `guidelines` parameter allows injecting our Constitution directly — no rubric format translation needed

4. Vertex AI has agent-specific metrics (`TOOL_USE_QUALITY`, `HALLUCINATION`) that align with GSD-Lite's tool-heavy workflow

The One-Liner: Use Vertex AI's adaptive rubric intelligence for behavioral evaluation, keep deterministic checks in Python for speed and cost.

#### 2. The Gap in LOG-042: We Only Researched Trajectory Metrics

##### 2.1 What LOG-042 Concluded (Incorrectly Scoped)

LOG-042 (lines 7017-7035) stated:

> "Vertex AI's trajectory evaluation requires a `reference_trajectory` (golden path) for comparison. GSD-Lite's Constitution doesn't define 'correct tool sequences' — it defines behavioral patterns like 'grep before read' and 'ask why before executing.'"

This was correct for trajectory metrics, but incomplete. Vertex AI has TWO distinct evaluation paradigms:

Paradigm | Metrics | Requires Golden Path? | GSD-Lite Fit
--- | --- | --- | ---
Trajectory | `trajectory_exact_match`, `trajectory_precision`, `trajectory_in_order_match` | ✅ Yes | ❌ Not fit
Rubric-Based | `GENERAL_QUALITY`, `INSTRUCTION_FOLLOWING`, `TEXT_QUALITY`, `TOOL_USE_QUALITY` | ❌ No | ✅ Strong fit

Citation: Vertex AI documentation, "Define your evaluation metrics" (fetched 2026-02-14):

- URL: https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval

- Key section: "Rubric-based metrics: Incorporate LLMs into evaluation workflows."

##### 2.2 The Missed Feature: Adaptive Rubrics with Custom Guidelines

The documentation explicitly states:

> "Adaptive rubrics function like unit tests for your models. Adaptive rubrics dynamically generate a unique set of pass or fail tests for each individual prompt in your dataset."

Citation: Same URL, section "Adaptive rubrics (recommended)":

    You can also guide GENERAL_QUALITY with natural language guidelines to focus
    rubric generation on the criteria that are most important to you. The Gen AI
    evaluation service then generates rubrics covering both its default tasks and
    the guidelines you specify.

Code example from documentation:

    from vertexai import types

    eval_result = client.evals.evaluate(
        dataset=eval_dataset,
        metrics=[
            types.RubricMetric.GENERAL_QUALITY(
                metric_spec_parameters={
                    "guidelines": "The response must maintain a professional tone and must not provide financial advice."
                }
            )
        ],
    )

Citation: https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval, code block under "General quality metric"

#### 3. Vertex AI Rubric-Based Metrics — Full Inventory

Citation: https://cloud.google.com/vertex-ai/generative-ai/docs/models/rubric-metric-details

Metric | Type | What It Evaluates | GSD-Lite Use Case
--- | --- | --- | ---
`GENERAL_QUALITY` | Adaptive | Overall response quality with custom guidelines | P2-H* behaviors via guidelines
`INSTRUCTION_FOLLOWING` | Adaptive | Adherence to prompt constraints | S1-H1 (Handoff format compliance)
`TEXT_QUALITY` | Adaptive | Fluency, coherence, grammar | J4-H* (Journalism quality)
`GROUNDING` | Static | Factuality against context | Could evaluate agent's use of tool outputs
`SAFETY` | Static | PII, hate speech, harassment | Baseline safety check
`MULTI_TURN_GENERAL_QUALITY` | Adaptive | Quality in multi-turn dialogue | Our sessions ARE multi-turn
`TOOL_USE_QUALITY` | Agent-specific | Correctness of function calls | C3-H1 (Grep-first pattern)
`HALLUCINATION` | Agent-specific | Is response grounded in tool outputs? | Verify agent uses tool data
`FINAL_RESPONSE_QUALITY` | Agent-specific | Overall agent answer quality | Session-level scoring

##### 3.1 Agent-Specific Metrics — Perfect Fit for GSD-Lite

Citation: https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-agents-client

The agent evaluation documentation shows input schema:

    eval_dataset = [
        {
            "prompt": "What's the weather in NYC?",
            "response": "The weather in NYC is sunny, 75°F.",
            "tool_use": [
                {
                    "tool_name": "get_weather",
                    "tool_input": {"location": "NYC"},
                    "tool_output": {"temperature": 75, "condition": "sunny"}
                }
            ]
        }
    ]

Key insight: This schema matches what `eval_helper.py` (LOG-041) already extracts! Our `generated_trajectory` field contains the same data — we just need to rename it to `tool_use`.

#### 4. Mapping GSD-Lite Constitution to Vertex Metrics

##### 4.1 Pillar 2: Pair Programming Model

Behavior | Vertex Metric | Guidelines Parameter
--- | --- | ---
P2-H1: Why Before How | `GENERAL_QUALITY` | "The agent MUST ask 'why' or state its understanding of intent BEFORE executing any action. Violations: executing without asking, assuming intent."
P2-H2: Grounding Loop | `GENERAL_QUALITY` | "After using any search/read tool, the agent MUST echo findings and ask for verification BEFORE proposing changes."
P2-H3: Challenge Tone | `GENERAL_QUALITY` | "When user states a decision without reasoning, agent MUST probe with gentle or direct challenge. Never accept vague requirements."
P2-H4: Teaching Offer | `GENERAL_QUALITY` | "When encountering unfamiliar patterns in codebase, agent SHOULD offer to explain before continuing."
P2-H5: No Auto-Write | `GENERAL_QUALITY` | "Agent MUST ask 'Want me to capture this to WORK.md?' before writing to any artifact."

##### 4.2 Pillar 1: Stateless-First

Behavior | Vertex Metric | How to Evaluate
--- | --- | ---
S1-H1: Handoff | `INSTRUCTION_FOLLOWING` | Check response ends with "📦 STATELESS HANDOFF" pattern
S1-H2: Two-Layer Structure | `INSTRUCTION_FOLLOWING` | Verify handoff contains Layer 1 (local) and Layer 2 (global)

Note: S1-H1 is better evaluated programmatically (simple grep) — see Layer 1 design below.

##### 4.3 Pillar 3: Context Engineering

Behavior | Vertex Metric | How to Evaluate
--- | --- | ---
C3-H1: Grep First | `TOOL_USE_QUALITY` + programmatic | Analyze tool sequence: `fs.grep` should precede `fs.read` on same path
C3-H2: Onboarding | Programmatic | First agent turn should read PROJECT, ARCHITECTURE, WORK.md

##### 4.4 Pillar 4: Journalism Quality

Behavior | Vertex Metric | Guidelines Parameter
--- | --- | ---
J4-H1: Log Format | `TEXT_QUALITY` | "Log entries must include: narrative framing, the symptom, evidence, root cause, analogy, decision, code snippet."
J4-H2: Backlinks | Programmatic | Grep for "Depends On:" and LOG-XXX references

#### 5. The Hybrid Architecture — Final Design

DECISION-043b: Use a two-layer hybrid architecture: Programmatic (Python) for Layer 1, Vertex AI rubric-based for Layer 2.

    flowchart TB
        subgraph "Input"
            OC["OpenCode Storage<br/>~/.local/share/opencode/"]
        end

        subgraph "Extraction"
            OC --> EH["eval_helper.py collect"]
            EH --> JSON["Session JSON<br/>(turn-structured)"]
        end

        subgraph "Layer 1: Programmatic (Free, Fast)"
            JSON --> L1A["S1-H1: Handoff Check<br/>'📦 STATELESS HANDOFF' in response"]
            JSON --> L1B["C3-H1: Grep-First Check<br/>Analyze tool_trajectory sequence"]
            JSON --> L1C["C3-H2: Onboarding Check<br/>First turn reads PROJECT, ARCH, WORK"]
            JSON --> L1D["J4-H2: Backlinks Check<br/>Grep for 'Depends On:' pattern"]
        end

        subgraph "Layer 2: Vertex AI (LLM-as-Judge)"
            JSON --> V1["GENERAL_QUALITY<br/>+ Constitution Guidelines"]
            JSON --> V2["TOOL_USE_QUALITY<br/>(C3-H1 qualitative)"]
            JSON --> V3["TEXT_QUALITY<br/>(J4-H1 narrative)"]
            JSON --> V4["INSTRUCTION_FOLLOWING<br/>(S1-H2 handoff structure)"]
        end

        subgraph "Aggregation"
            L1A & L1B & L1C & L1D --> L1R["Layer 1 Report<br/>(deterministic pass/fail)"]
            V1 & V2 & V3 & V4 --> L2R["Layer 2 Report<br/>(rubric scores + verdicts)"]
            L1R & L2R --> AGG["Aggregate Report"]
            AGG --> CI["CI Gate<br/>(exit 0 if pass rate >= threshold)"]
        end

        style L1A fill:#90EE90
        style L1B fill:#90EE90
        style L1C fill:#90EE90
        style L1D fill:#90EE90
        style V1 fill:#87CEEB
        style V2 fill:#87CEEB
        style V3 fill:#87CEEB
        style V4 fill:#87CEEB

##### 5.1 Why This Split?

Layer | Cost | Speed | Best For
--- | --- | --- | ---
L1: Programmatic | Free | <1ms per session | Binary checks: presence of pattern, tool sequence order
L2: Vertex AI | ~6 Gemini Flash calls per metric | ~2-5s per session | Qualitative judgment: tone, reasoning quality, narrative

Cost estimate for L2:

- 1 session × 4 metrics × 6 calls = 24 Gemini Flash calls

- At $0.075/1M input tokens, negligible for small batches

- For CI: evaluate only "golden sessions" (curated test set), not all organic work

##### 5.2 Layer 1: Programmatic Checks (Python)

    # File: eval/layer1_checks.py

    def check_handoff_present(session: dict) -> tuple[bool, str]:
        """
        S1-H1: Agent ends response with STATELESS HANDOFF.

        PASS: Last agent turn contains "📦 STATELESS HANDOFF"
        FAIL: Pattern not found
        """
        last_agent_turn = [t for t in session["turns"] if t["role"] == "agent"][-1]
        if "📦 STATELESS HANDOFF" in last_agent_turn["content"]:
            return True, "Handoff present"
        return False, "Missing STATELESS HANDOFF in final response"

    def check_grep_before_read(session: dict) -> tuple[bool, str]:
        """
        C3-H1: Agent uses grep-first pattern.

        PASS: Every fs.read is preceded by fs.grep on same/parent path
        FAIL: fs.read without preceding fs.grep
        """
        grepped_paths = set()

        for call in session.get("tool_trajectory", []):
            if call["tool"] == "fs.grep":
                search_path = call["args"].get("search_path", ".")
                grepped_paths.add(search_path)

            if call["tool"] == "fs.read":
                for file_req in call["args"].get("files", []):
                    file_path = file_req.get("path", "")
                    # Check if file is under a grepped directory
                    if not any(file_path.startswith(gp) or gp == "." for gp in grepped_paths):
                        return False, f"Read '{file_path}' without grep-first"

        return True, "Grep-first pattern followed"

    def check_onboarding_sequence(session: dict) -> tuple[bool, str]:
        """
        C3-H2: Agent reads PROJECT, ARCHITECTURE, WORK.md on first turn.

        PASS: First agent turn reads all three files
        FAIL: Missing one or more onboarding reads
        """
        first_agent_turn = next(
            (t for t in session["turns"] if t["role"] == "agent"),
            None
        )
        if not first_agent_turn:
            return False, "No agent turn found"

        tools_in_first_turn = first_agent_turn.get("tools", [])
        read_paths = []
        for tool in tools_in_first_turn:
            if tool["tool"] == "fs.read":
                for f in tool["args"].get("files", []):
                    read_paths.append(f.get("path", ""))

        required = ["PROJECT.md", "ARCHITECTURE.md", "WORK.md"]
        missing = [r for r in required if not any(r in p for p in read_paths)]

        if missing:
            return False, f"Missing onboarding reads: {missing}"
        return True, "Onboarding sequence complete"

##### 5.3 Layer 2: Vertex AI Rubric-Based Evaluation

DECISION-043c: Inject GSD-Lite Constitution as the `guidelines` parameter to `GENERAL_QUALITY`.

    # File: eval/layer2_vertex.py

    import vertexai
    from vertexai import types

    # Constitution as guidelines (distilled from CONSTITUTION.md)
    CONSTITUTION_GUIDELINES = """
    PILLAR 2 - PAIR PROGRAMMING MODEL:
    - P2-H1 (Why Before How): Agent MUST ask 'why' or state understanding of intent BEFORE executing any action.
    - P2-H2 (Grounding Loop): After using search/read tools, agent MUST echo findings and verify BEFORE proposing changes.
    - P2-H3 (Challenge Tone): When user states decision without reasoning, agent MUST probe with gentle or direct challenge.
    - P2-H4 (Teaching Offer): When encountering unfamiliar patterns, agent SHOULD offer to explain before continuing.
    - P2-H5 (No Auto-Write): Agent MUST ask permission before writing to any artifact file.

    PILLAR 1 - STATELESS-FIRST:
    - S1-H1 (Handoff): Every response MUST end with structured handoff packet containing Layer 1 (local) and Layer 2 (global) context.

    PILLAR 4 - JOURNALISM QUALITY:
    - J4-H1 (Narrative): Log entries must include: narrative framing, symptom, evidence, root cause, analogy, decision, code snippet.
    """

    def evaluate_session_with_vertex(session: dict) -> dict:
        """
        Run Vertex AI rubric-based evaluation on a session.

        Returns dict with scores and verdicts for each metric.
        """
        vertexai.init(project="your-project", location="us-central1")
        client = vertexai.Client()

        # Transform session to Vertex format
        eval_dataset = [{
            "prompt": session["full_transcript"],  # Full conversation
            "response": session["turns"][-1]["content"],  # Last agent response
            "tool_use": [
                {
                    "tool_name": call["tool"],
                    "tool_input": call["args"],
                    "tool_output": call.get("output", "")
                }
                for call in session.get("tool_trajectory", [])
            ]
        }]

        # Run evaluation with Constitution as guidelines
        result = client.evals.evaluate(
            dataset=eval_dataset,
            metrics=[
                types.RubricMetric.GENERAL_QUALITY(
                    metric_spec_parameters={
                        "guidelines": CONSTITUTION_GUIDELINES
                    }
                ),
                types.RubricMetric.TOOL_USE_QUALITY,
                types.RubricMetric.TEXT_QUALITY,
                types.RubricMetric.INSTRUCTION_FOLLOWING,
            ],
        )

        return result

#### 6. Schema Alignment: eval_helper.py Output → Vertex Input

Our current `eval_helper.py` output (LOG-041) needs minor adjustments:

Current Field | Vertex Expected | Action
--- | --- | ---
`prompt` (concatenated) | `prompt` | ✅ Keep
`response` (concatenated) | `response` | ⚠️ Change to last agent response only
`generated_trajectory` | `tool_use` | ⚠️ Rename + restructure
`turns[]` (proposed in LOG-042) | N/A (we use for L1) | ✅ Keep for programmatic checks

Vertex `tool_use` expected format:

    {
      "tool_use": [
        {
          "tool_name": "get_weather",
          "tool_input": {"location": "NYC"},
          "tool_output": {"temperature": 75}
        }
      ]
    }

Our current `generated_trajectory` format:

    {
      "generated_trajectory": [
        {
          "tool": "fs.grep",
          "tool_raw": "mcp_tools_gsd-lite-fs_grep_content",
          "args": {"pattern": "LOG-001"},
          "output": "Line 102: [LOG-001]..."
        }
      ]
    }

Transformation needed:

    def transform_to_vertex_format(session: dict) -> dict:
        """Transform eval_helper.py output to Vertex AI expected format."""
        return {
            "prompt": session["full_transcript"],
            "response": session["turns"][-1]["content"],  # Last agent response
            "tool_use": [
                {
                    "tool_name": call["tool"],
                    "tool_input": call["args"],
                    "tool_output": call.get("output", "")
                }
                for call in session.get("generated_trajectory", [])
            ]
        }

#### 7. Cost Analysis

Citation: https://cloud.google.com/vertex-ai/generative-ai/pricing (Gemini Flash pricing)

Metric | LLM Calls | Model | Est. Cost per Session
--- | --- | --- | ---
`GENERAL_QUALITY` | 6 | Gemini 2.5 Flash | ~$0.0001
`TOOL_USE_QUALITY` | 2 | Gemini 2.5 Flash | ~$0.00003
`TEXT_QUALITY` | 6 | Gemini 2.5 Flash | ~$0.0001
`INSTRUCTION_FOLLOWING` | 6 | Gemini 2.5 Flash | ~$0.0001
Total L2 per session | 20 | — | ~$0.0004

For CI:

- Evaluate 10 "golden sessions" = ~$0.004 per CI run

- Evaluate 100 sessions for full audit = ~$0.04

Verdict: Cost is negligible. Not a factor in decision.

#### 8. Implementation Tasks (Updated)

Task ID | Description | Depends On | Est. Effort | Status
--- | --- | --- | --- | ---
TASK-EVAL-002a | Refactor eval_helper.py to turn-structured schema | LOG-042 | 3h | From LOG-042
TASK-EVAL-002b | Add Vertex-compatible `tool_use` field transformation | LOG-043 | 1h | NEW
TASK-EVAL-002c | Implement Layer 1 programmatic checks (Python) | LOG-043 | 2h | Updated
TASK-EVAL-002d | Create Vertex AI evaluation script (`layer2_vertex.py`) | LOG-043 | 2h | NEW (replaces Promptfoo config)
TASK-EVAL-002e | Implement aggregated report generation | TASK-EVAL-002c, 002d | 1h | From LOG-042
TASK-EVAL-002f | Create CI integration (GitHub Actions) | TASK-EVAL-002e | 2h | From LOG-042

#### 9. Dependency Graph

    graph TD
        LOG028["LOG-028: CI Framework Design<br/>(Six Pillars, 3-layer architecture)"] --> LOG030
        LOG028 --> LOG032

        LOG030["LOG-030: CONSTITUTION.md v0.1<br/>(Four Pillars extracted)"] --> LOG031

        LOG031["LOG-031: pair-programming.yaml<br/>(P2-H1 to P2-H5 rubric)"] --> LOG042

        LOG032["LOG-032: OpenCode Goldmine<br/>(Data source + Vertex AI trajectory decision)"] --> LOG041
        LOG032 --> LOG042

        LOG041["LOG-041: eval_helper.py<br/>(Interactive collect workflow)"] --> LOG042

        LOG042["LOG-042: Constitutional Eval Architecture<br/>(Session-as-unit, Promptfoo decision)"] --> LOG043

        LOG043["LOG-043: Vertex AI Rubric-Based Eval<br/>(This entry: Hybrid L1+L2, Vertex for qualitative)"]

        style LOG043 fill:#90EE90
        style LOG042 fill:#FFB6C1
        style LOG032 fill:#FFB6C1

        LOG043 -.->|"Supersedes DECISION-042b"| LOG042
        LOG042 -.->|"Supersedes DECISION-032b"| LOG032

To onboard this decision from scratch:

1. LOG-028 (lines 4038-4344): Why CI framework; the Six Pillars; 3-layer architecture concept

2. LOG-030 (lines 4756-4821): Constitution v0.1 with Four Pillars distilled

3. LOG-031 (lines 4822-4905): The rubric format; P2-H1 to P2-H5 behaviors (still valid, informs guidelines)

4. LOG-032 (lines 4906-5477): OpenCode as data source (still valid); Vertex trajectory decision (superseded)

5. LOG-041 (lines 6688-6985): eval_helper.py implementation (still valid, needs schema update)

6. LOG-042 (lines 6986-7493): Session-as-unit, hybrid orchestration (still valid); Promptfoo decision (superseded)

7. LOG-043 (this entry): Vertex AI rubric-based evaluation; hybrid L1+L2 architecture; Constitution as guidelines

#### 10. Citations & Sources

Source | URL | Key Quote
--- | --- | ---
Vertex AI Eval Metrics | https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval | "Adaptive rubrics function like unit tests for your models."
Rubric Metric Details | https://cloud.google.com/vertex-ai/generative-ai/docs/models/rubric-metric-details | Full list of managed rubric-based metrics with input/output specs
Agent Evaluation | https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-agents-client | "The Gen AI evaluation service lets you measure your agent's ability to complete tasks and goals"
Guidelines Parameter | determine-eval URL, "General quality metric" section | Code example showing `metric_spec_parameters={"guidelines": "..."}`

#### 11. Open Questions (For Next Session)

Question | Status | Notes
--- | --- | ---
Vertex SDK version | OPEN | Need `google-cloud-aiplatform[genai]` — verify version compatibility
Multi-turn transcript format | OPEN | Does Vertex expect specific delimiters? ("USER: ... AGENT: ...")
Golden session curation | PENDING | Which sessions form the "golden test set" for CI?
L1 check coverage | PENDING | Are there more deterministic checks we're missing?

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-043 (Vertex AI Rubric-Based Evaluation decision)

→ Dependency chain: LOG-043 ← LOG-042 ← LOG-041 ← LOG-032 ← LOG-028

→ Next action: TASK-EVAL-002b — Add Vertex-compatible `tool_use` transformation to eval_helper.py

Layer 2 — Global Context:

→ Architecture: eval_helper.py (extract) → Session JSON → Python L1 (deterministic) + Vertex L2 (adaptive rubrics) → aggregate report

→ Patterns: Session = eval unit; Constitution as guidelines parameter; hybrid L1+L2

→ Key decisions: DECISION-043a (Vertex for L2), DECISION-043b (hybrid architecture), DECISION-043c (Constitution as guidelines)

Fork paths:

- Implement Vertex integration → TASK-EVAL-002d: Create layer2_vertex.py

- Update eval_helper schema → TASK-EVAL-002b: Add tool_use transformation

- Implement L1 checks → TASK-EVAL-002c: Python programmatic checks

- Spike manually → Run one session through Vertex console before coding

### [LOG-044] - [EXEC] - The ELT Pipeline Pivot: Implementing Modular Ingest/Transform/Consume Architecture - Task: TASK-EVAL-002

Status: COMPLETE

Date: 2026-02-14

Tasks: TASK-EVAL-002b (Transform), TASK-EVAL-002c (Consume L1)

Key Insight: Refactoring `eval_helper.py` into a modular ELT pipeline (`eval_ingest.py` → `eval_transform.py` → `eval_consume.py`) provides debuggability and clean separation of concerns.

Dependencies:

- LOG-043: Vertex AI Hybrid Architecture (defines what we're building)

- LOG-041: Original `eval_helper.py` (renamed to `eval_ingest.py`)

- LOG-028: CI Framework (defines the Six Pillars being checked)

#### 1. Executive Summary

We successfully implemented the Layer 1 (Programmatic) evaluation pipeline using a modular ELT (Extract-Load-Transform) architecture. Instead of a monolithic script, we now have three focused tools that pipe data through the system.

The Pipeline:

1. INGEST (`eval_ingest.py`): Discovers sessions, audits tools, extracts raw data.

2. TRANSFORM (`eval_transform.py`): Reshapes raw data into Vertex AI compatible format (`tool_use`).

3. CONSUME (`eval_consume.py`): Runs deterministic Layer 1 checks against the Constitution.

Why this matters:

- Debuggability: If L1 checks fail, we can inspect the intermediate JSON files to see if it's a data issue or a logic issue.

- Extensibility: Adding a new consumer (e.g., Promptfoo) only requires a new `transform` command, not touching ingestion logic.

- CI Readiness: `eval_consume.py` includes a `--ci` flag with pass-rate thresholds, ready for GitHub Actions.

#### 2. Architecture Diagram

    flowchart LR
        subgraph "Data Source"
            OC["OpenCode Storage"]
        end

        subgraph "Step 1: Ingest"
            EI["eval_ingest.py"]
            RAW["eval_run_*.json<br/>(Raw Schema)"]
        end

        subgraph "Step 2: Transform"
            ET["eval_transform.py"]
            VTX["eval_run_*_vertex.json<br/>(Vertex Schema)"]
        end

        subgraph "Step 3: Consume"
            EC["eval_consume.py"]
            REP["Evaluation Report<br/>(Pass/Fail)"]
        end

        OC --> EI
        EI --> RAW
        RAW --> ET
        ET --> VTX
        VTX --> EC
        EC --> REP

#### 3. Component Details

##### 3.1 Step 2: Transform (`eval_transform.py`)

Goal: Bridge the gap between OpenCode's raw storage format and Vertex AI's expected input.

Key Transformation: Renaming `generated_trajectory` → `tool_use`.

    # Source: scripts/eval_transform.py

    def transform_to_vertex(session: dict) -> dict:
        """
        Transform raw session to Vertex AI evaluation format.
        Ref: LOG-043 Section 6
        """
        tool_use = []
        for call in session.get("generated_trajectory", []):
            tool_use.append({
                "tool_name": call.get("tool", "unknown"),   # Renamed from 'tool'
                "tool_input": call.get("args", {}),         # Renamed from 'args'
                "tool_output": call.get("output", "")       # Renamed from 'output'
            })

        return {
            "tool_use": tool_use,
            # ... other fields ...
        }

##### 3.2 Step 3: Consume (`eval_consume.py`)

Goal: Enforce the Constitution via deterministic code (Layer 1).

Implemented Checks:

ID | Check Name | Constitution Logic (Source: LOG-030)
--- | --- | ---
S1-H1 | Handoff Present | Response ends with `📦 STATELESS HANDOFF`
S1-H2 | Handoff Structure | Contains "Layer 1" AND "Layer 2" sections
C3-H1 | Grep-First Pattern | Every `fs.read` preceded by `fs.grep` on parent path
C3-H2 | Onboarding Sequence | Session reads PROJECT, ARCHITECTURE, WORK.md
J4-H2 | Backlinks Present | WORK.md writes contain `LOG-XXX` or `Depends On:`

Code Example: The Grep-First Check (C3-H1)

    # Source: scripts/eval_consume.py

    def check_grep_before_read(session: dict) -> CheckResult:
        """
        C3-H1: Agent uses grep-first pattern.
        PASS: Every fs.read is preceded by fs.grep (or allowed exceptions)
        """
        searched_paths = set(["."]) # Root always implicitly searched
        violations = []

        # Iterate through tool calls in order
        for call in trajectory:
            tool = call.get("tool_name", "")

            # Track search actions
            if tool == "fs.grep":
                path = call["tool_input"].get("search_path", ".")
                searched_paths.add(path)

            # Verify read actions
            if tool == "fs.read":
                file_path = call["tool_input"].get("path", "")

                # Check if file is covered by previous search
                is_covered = any(file_path.startswith(p) for p in searched_paths)

                if not is_covered and file_path not in EXEMPT_FILES:
                    violations.append(file_path)

        return CheckResult(passed=len(violations)==0, ...)

#### 4. Usage Guide for Future Agents

To run the full pipeline on a project:

    # 1. Ingest (Interactive)
    python scripts/eval_ingest.py collect --project /path/to/project

    # 2. Transform (Vertex format)
    python scripts/eval_transform.py vertex

    # 3. Consume (Layer 1 Checks)
    python scripts/eval_consume.py l1 --verbose

Output Example:

    ✅ ses_abc123... (5/5 passed)
       ✓ [S1-H1] Handoff Present: Handoff block found
       ✓ [S1-H2] Handoff Structure: Both Layer 1 and Layer 2 present
       ✓ [C3-H1] Grep-First Pattern: All reads preceded by grep/search
       ✓ [C3-H2] Onboarding Sequence: All onboarding files read
       ✓ [J4-H2] Backlinks Present: Backlink patterns found

    📊 Summary: 5/5 checks passed (100%)

#### 5. Open Questions & Next Steps

Question | Status | Next Step
--- | --- | ---
Layer 2 Implementation | PENDING | TASK-EVAL-002d: Create `eval_consume.py l2` using Vertex SDK
Golden Set Curation | OPEN | Need to define which sessions form the CI test set
Turn Schema | BLOCKED | `eval_ingest.py` still outputs flat prompt/response. Need TASK-EVAL-002a.

Immediate Next Action: Implement Layer 2 (Vertex AI) integration in `eval_consume.py`.

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: Implemented & logged ELT pipeline (TASK-EVAL-002b/c)

→ Dependency chain: LOG-044 (this log) ← LOG-043 (design) ← LOG-041 (original script)

→ Next action: TASK-EVAL-002d — Implement `layer2_vertex.py` (or extend `eval_consume.py`)

Layer 2 — Global Context:

→ Architecture: eval_ingest → eval_transform → eval_consume

→ Patterns: ELT pipeline; Dataclass results; L1 checks

→ Key decisions: DECISION-043a (Vertex L2), DECISION-044a (ELT Modular Architecture)

Fork paths:

- Implement L2 Vertex → TASK-EVAL-002d

- Refactor for turns[] schema → TASK-EVAL-002a

- Curate golden set → Manual review of existing sessions

### [LOG-045] - [DECISION] - Migration to OpenCode SQLite (opencode.db) & Vertex Full-Context Fix - Task: TASK-EVAL-002

Status: IMPLEMENTED

Date: 2026-02-14

Decision IDs:

- DECISION-045a: Migrate `eval_ingest.py` to `sqlmodel` (SQLite)

- DECISION-045b: Update `eval_transform.py` to preserve full context (fix response truncation)

- DECISION-045c: Add `sqlmodel` as optional dependency (`pip install .[eval]`)

Task: TASK-EVAL-002 (Constitutional Evaluation Pipeline)

Supersedes:

- LOG-041: `eval_helper.py` Implementation (files-based ingestion is now dead)

- LOG-032: OpenCode Goldmine (storage location changed from `~/.local/share/opencode/storage/session` to `opencode.db`)

Dependencies:

- LOG-043: Vertex AI Rubric-Based Eval (lines 7506-7935) — defines L2 rubric architecture which required the transform fix

- LOG-044: Constitutional ELT Pipeline (lines 7936-8227) — defines the ingestion/transform/consume layers updated here

#### 1. The Great Migration: From Files to SQLite

The Symptom:

The evaluation pipeline suddenly reported 0 sessions.

    $ python scripts/eval_ingest.py discover --project ...
    ❌ No matching sessions found.

The Discovery:

OpenCode silently migrated its storage backend. The directory `~/.local/share/opencode/storage/session` (which LOG-032 relied on) was empty or gone.

Investigation revealed a new source of truth: `~/.local/share/opencode/opencode.db`.

The New Schema (SQLite):

    erDiagram
        SESSION ||--o{ MESSAGE : contains
        MESSAGE ||--o{ PART : contains
        PROJECT ||--o{ SESSION : owns

        SESSION {
            string id PK
            string project_id FK
            int time_created
            string title
        }
        MESSAGE {
            string id PK
            string session_id FK
            json data "Contains role, model_id"
        }
        PART {
            string id PK
            string message_id FK
            json data "Contains type (text/tool), content"
        }

The Fix (DECISION-045a):

Refactored `eval_ingest.py` to use `sqlmodel` (SQLAlchemy + Pydantic) instead of `pathlib.glob`.

- Old: File system traversal (`glob("ses_*.json")`)

- New: SQL queries (`select(SessionModel).where(...)`)

- Impact: Added `sqlmodel` dependency to `pyproject.toml`.

Code Snippet (SQLModel Setup):

    # scripts/eval_ingest.py

    class Part(SQLModel, table=True):
        """OpenCode part table (tool calls, text, reasoning)."""
        id: str = Field(primary_key=True)
        message_id: str = Field(foreign_key="message.id")
        session_id: str = Field(foreign_key="session.id")
        time_created: int
        data: str  # JSON blob containing tool calls

    def extract_paths_from_output(tool_name: str, output: str) -> Set[str]:
        """Extract paths from tool outputs to fingerprint projects."""
        # (Same logic as before, but fed from SQL data)
        ...

#### 2. The Vertex Transform Bug (Response Truncation)

The Symptom:

Vertex AI transformed files (`*_vertex.json`) contained only tiny fragments of the agent's response.

- Raw file: `response_length: 3550`

- Vertex file: `response_length: 18` ("Now let's test it:")

The Cause (Premature Optimization):

In LOG-043 implementation, `eval_transform.py` attempted to "clean up" the response by taking only the last paragraph:

    # BAD:
    last_response = full_response.split("\n\n")[-1]

This destroyed 99% of the context needed for rubric evaluation. Vertex AI's `GENERAL_QUALITY` metric needs the entire reasoning chain to evaluate "why" decisions were made.

The Fix (DECISION-045b):

Updated `eval_transform.py` to preserve the full response.

    # GOOD (scripts/eval_transform.py):
    # Keep FULL response — Vertex rubric metrics need complete context
    full_response = session.get("response", "")

    return {
        # ...
        "prompt": session.get("prompt", ""),
        "response": full_response,  # FULL response, not truncated
        "tool_use": tool_use,
    }

Citation: Vertex AI Documentation, "Rubric-based metrics":

> "Adaptive rubrics function like unit tests for your models... The Gen AI evaluation service then generates rubrics covering both its default tasks and the guidelines you specify."

> Source: https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval

Synthesized Example:

Input (Raw) | Old Transform (Buggy) | New Transform (Fixed)
--- | --- | ---
"I analyzed the database.<br/><br/>Found 3 tables.<br/><br/>Plan: Update schema." | "Plan: Update schema." | "I analyzed the database.<br/><br/>Found 3 tables.<br/><br/>Plan: Update schema."
Impact on Vertex | FAIL (Missing reasoning) | PASS (Full context visible)

#### 3. Updated Dependency Graph (DAG) for Onboarding

Future agents: Use this DAG to reconstruct the evaluation pipeline context.

    graph TD
        LOG-028[LOG-028: CI Framework] --> LOG-042
        LOG-032[LOG-032: OpenCode Goldmine] -- Superseded by --> LOG-045
        LOG-041[LOG-041: Old Ingest] -- Superseded by --> LOG-045
        LOG-042[LOG-042: Eval Architecture] --> LOG-043
        LOG-043[LOG-043: Vertex Hybrid] --> LOG-044
        LOG-044[LOG-044: ELT Pipeline] --> LOG-045
        LOG-045[LOG-045: SQLite Migration] --> TASK-EVAL-002d

Key Files Updated:

1. `scripts/eval_ingest.py`: Rewritten for SQLite/SQLModel.

2. `scripts/eval_transform.py`: Fixed response truncation.

3. `pyproject.toml`: Added `sqlmodel`, `pyyaml`.

Next Actions:

- Proceed to TASK-EVAL-002d (Vertex L2 integration) using the now-correct transformed data.

### [LOG-046] - [DECISION] - Vertex-Native Turn-Structured Schema & Pipeline Simplification: Decommissioning the Transform Layer - Task: TASK-EVAL-002a

Status: IMPLEMENTED

Date: 2026-02-15

Decision IDs:

- DECISION-046a: Implement Vertex-native turn-structured schema in `eval_ingest.py`

- DECISION-046b: Decommission `eval_transform.py` (simplify pipeline)

- DECISION-046c: Update `eval_consume.py` to be turn-aware

Task: TASK-EVAL-002a (Turn-structured schema)

Supersedes:

- LOG-044: The ELT Pipeline Pivot (removed the "Transform" step)

- LOG-045b: Update `eval_transform.py` (file is now dead)

Dependencies:

- LOG-042: Turn-Structured Output Schema (lines 6986-7493) — the original proposal for `turns[]`

- LOG-043: Vertex AI Rubric-Based Eval (lines 7506-7935) — the rubric metric requirements

#### 1. The Schema Gap: Concatenation vs. Conversation

The Symptom:

Layer 1 programmatic checks failed because they couldn't distinguish "Did the agent grep before reading THIS file?" vs "Did it grep somewhere else in the session?".

Also, Vertex AI's `MULTI_TURN_GENERAL_QUALITY` metric was receiving a flat blob of text, losing the conversational context.

The Fix (DECISION-046a):

Refactored `eval_ingest.py` to output Vertex-native turn structure directly.

Old Schema (Flat):

    {
      "prompt": "User turn 1\n\nUser turn 2",
      "response": "Agent turn 1\n\nAgent turn 2",
      "generated_trajectory": [...]
    }

New Schema (Vertex-Native):

    {
      "request": {
        "contents": [
          {"role": "user", "parts": [{"text": "User turn 1"}]},
          {"role": "model", "parts": [{"text": "Agent turn 1"}]},
          {"role": "user", "parts": [{"text": "User turn 2"}]}
        ]
      },
      "response": {
        "candidates": [
          {"content": {"role": "model", "parts": [{"text": "Final agent response"}]}}
        ]
      },
      "intermediate_events": [
        {
          "function_call": {"name": "fs.grep", "args": {...}},
          "function_response": {"name": "fs.grep", "response": {...}},
          "turn": 1
        }
      ],
      "prompt_concat": "...",  // Backward compat for L1
      "response_concat": "..." // Backward compat for L1
    }

Citation: Vertex AI Documentation, "Evaluation Dataset":

> "The Gen AI evaluation service automatically parses multi-turn conversation data... identifies the previous turns and processes them as conversation_history."

> Source: https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-dataset

#### 2. Pipeline Simplification: Decommissioning Transform

The Insight (DECISION-046b):

`eval_transform.py` existed solely to bridge the gap between `eval_ingest.py`'s flat output and Vertex's schema. Now that ingest outputs Vertex-native directly, the transform step is redundant.

Old Pipeline (ELT):

    flowchart LR
        Ingest[eval_ingest.py] -->|Flat JSON| Transform[eval_transform.py]
        Transform -->|Vertex JSON| Consume[eval_consume.py]

New Pipeline (E-L):

    flowchart LR
        Ingest[eval_ingest.py] -->|Vertex-Native JSON| Consume[eval_consume.py]
        Transform[eval_transform.py] ---x Dead

Action: Deleted `scripts/eval_transform.py`.

#### 3. Turn-Aware Consumption

The Update (DECISION-046c):

Updated `eval_consume.py` to check specific turns instead of the whole blob.

Check | Old Logic (Flat) | New Logic (Turn-Aware)
--- | --- | ---
S1-H1 (Handoff) | Scanned entire session text | Checks final model turn only
C3-H1 (Grep First) | Flat list of tools | Checks tool sequence per turn
C3-H2 (Onboarding) | Checked entire session | Checks Turn 0 specifically

Code Snippet:

    # scripts/eval_consume.py

    def get_final_response(session: dict) -> str:
        """Extract final model response from Vertex-native schema."""
        try:
            # Try new Vertex-native format first
            return session["response"]["candidates"][0]["content"]["parts"][0]["text"]
        except KeyError:
            # Fallback to legacy
            return session.get("response_concat", "")

#### 4. Updated Dependency Graph (DAG)

Future agents: This simplifies the architecture significantly.

    graph TD
        LOG-045[LOG-045: SQLite Migration] --> LOG-046
        LOG-046[LOG-046: Vertex Schema & Pipeline Simplify] --> TASK-EVAL-002d
        TASK-EVAL-002d[Vertex L2 Integration] --> TASK-EVAL-002e[CI Workflow]

Next Actions:

- Proceed to TASK-EVAL-002d (Vertex L2 integration) using the simplified pipeline.

- Verify L1 checks pass on the new schema.

### [LOG-047] - [VISION] - GSD-Lite Worklog Reader: Mobile-First Ubiquitous Access for Dense Knowledge Bases - Task: READER-001

#### 1. Executive Summary

The Problem: GSD-Lite's journalism-style worklogs (9,000+ lines, 46+ entries) are high-value knowledge assets but impossible to consume outside VS Code. Mobile GitHub viewer lacks outline navigation, scroll-to-bottom, and sticky headers — breaking the "ubiquitous access" principle that makes GTD workflows successful.

The Solution: Build a Python generator that compiles WORK.md into a self-contained HTML viewer with mobile-first outline navigation, sticky breadcrumbs, and pre-rendered Mermaid diagrams.

Key Insight: This isn't "make markdown render nicely" — it's "build a purpose-built worklog browser" that understands GSD-Lite's structure (LOG-NNN entries, TYPE badges, hierarchical sections).

#### 2. The Problem Narrative: Why Existing Tools Fail

##### 2.1 The GTD Parallel

The user's GTD setup (TickTick) exemplifies ubiquitous access:

- Collect thoughts from any device

- Clarify/review from phone, tablet, or desktop

- Central inbox accessible everywhere

GSD-Lite worklogs currently violate this principle:

- Locked to local filesystem — WORK.md lives in `~/dev/gsd_lite/gsd-lite/`

- VS Code-dependent — Outline view, sticky headers, cmd+down only work in IDE

- Private by nature — Can't push to public repo, so no GitHub Pages

##### 2.2 Failed Experiments

Approach | Issue
--- | ---
GitHub Mobile App | Opens at top of file. No scroll grab button. No outline view. 9k lines = lost immediately.
MkDocs / ReadTheDocs | Great on desktop. Mobile: outline disappears (responsive design hides it). No "jump to bottom."
EPUB Conversion | Converters only parse H1/H2 headers. GSD-Lite uses H3 (`### [LOG-NNN]`) for entries — invisible in TOC.

##### 2.3 The Core Requirement

The user needs VS Code's markdown experience on mobile:

VS Code Feature | Mobile Equivalent Needed
--- | ---
`Cmd+End` (jump to bottom) | "Jump to Latest" button
Outline panel (H1-H5 hierarchy) | Collapsible sidebar/menu
Sticky headers while scrolling | Breadcrumb bar showing current position
Horizontal scroll for long lines | Prevent header wrapping in outline

#### 3. The Solution: `generate_worklog_viewer.py`

##### 3.1 Core Architecture

    flowchart LR
        WORK[WORK.md] --> Parser[Markdown Parser]
        Parser --> Tree[JSON AST]
        Tree --> Renderer[HTML Renderer]
        Mermaid[Mermaid Blocks] --> |mmdc CLI| PNG[Base64 PNG]
        PNG --> Renderer
        Renderer --> HTML[worklog.html]

Generator invocation:

    python generate_worklog_viewer.py gsd-lite/WORK.md -o worklog.html

Output: Single self-contained HTML file. AirDrop to phone. Open in any browser.

##### 3.2 Parsing Contract

The parser relies on GSD-Lite's consistent header format:

Pattern | Meaning | Regex
--- | --- | ---
`### [LOG-NNN] - [TYPE] - Title - Task: XXX` | Log entry | `^### \[LOG-(\d+)\] - \[(\w+)\] - (.+)`
`#### Section Title` | Section within log | `^#### (.+)`
`##### Subsection Title` | Subsection | `^##### (.+)`
`[TAG]` anywhere in header | Type badge | `\[([A-Z_]+)\]`
`strikethrough` in title | Superseded entry | `.+`

Example parse (from LOG-043 header):

    Input:  ### [LOG-043] - [DECISION] - Vertex AI Rubric... - Task: TASK-EVAL-002
    Output: {
        "id": "LOG-043",
        "type": "DECISION",
        "title": "Vertex AI Rubric...",
        "task": "TASK-EVAL-002",
        "superseded": false,
        "line": 7521,
        "children": [...]
    }

##### 3.3 Output Structure (Single HTML)

    <!DOCTYPE html>
    <html>
    <head>
        <meta name="viewport" content="width=device-width, initial-scale=1">
        <style>/* Inline CSS - mobile-first */</style>
    </head>
    <body>
        <!-- Sticky top bar -->
        <header class="top-bar">
            <button id="outline-toggle">🔖 Outline</button>
            <button id="jump-latest">⬇ Jump Latest</button>
        </header>

        <!-- Sticky breadcrumb -->
        <nav class="breadcrumb">LOG-043 > Part 5 > 5.2 Layer 1</nav>

        <!-- Outline panel (slide-in on mobile) -->
        <aside class="outline">
            <!-- Collapsible tree rendered here -->
        </aside>

        <!-- Content area -->
        <main class="content">
            <!-- Rendered markdown with anchors -->
        </main>

        <script>/* Inline JS - scroll sync, outline toggle */</script>
    </body>
    </html>

#### 4. UX Specification

##### 4.1 Layout (Mobile-First)

    ┌────────────────────────────────────────┐
    │ 🔖 Outline Toggle    [⬇ Jump Latest]  │  ← Sticky top bar
    ├────────────────────────────────────────┤
    │ LOG-043 > Part 5 > 5.2 Layer 1        │  ← Sticky breadcrumb
    ├────────────────────────────────────────┤
    │                                        │
    │  [Content area — scrollable]           │
    │                                        │
    │  #### 5.2 Layer 1: Programmatic...    │
    │  ```python                             │
    │  def check_handoff_present(...):       │
    │  ...                                   │
    │                                        │
    └────────────────────────────────────────┘

Scrollbar: Force-visible on mobile (CSS `overflow-y: scroll` + WebKit scrollbar styling). Grabbable thumb for quick navigation.

##### 4.2 Outline Panel

    📋 WORK.md Outline                    [horizontal scroll →]
    ───────────────────────────────────────────────────────────
    ▼ LOG-043 [DECISION] - Vertex AI Rubric-Based Evaluation...
      ├─ 1. Executive Summary
      ├─ 2. The Gap in LOG-042
      │   ├─ 2.1 What LOG-042 Concluded
      │   └─ 2.2 The Missed Feature
      ├─ 3. Vertex AI Metrics Inventory
      └─ ...

    ▶ LOG-042 [DECISION] - Constitutional Eval... (collapsed)
    ▶ LOG-041 [EXEC] - ~~eval_helper.py...~~ (dimmed, superseded)

Feature | Implementation
--- | ---
Horizontal scroll | `white-space: nowrap; overflow-x: auto` — headers don't wrap
Dynamic badges | Parser extracts all `[TAG]` patterns. No hardcoded list.
Superseded dimming | 50% opacity + strikethrough preserved
Collapsible | Click `▶` to expand children

##### 4.3 Type Badges (Dynamic)

Badges are extracted from headers at parse time, not hardcoded:

    # Pseudocode
    def extract_badges(header_text: str) -> list[str]:
        """Extract all [TAG] patterns from header."""
        return re.findall(r'\[([A-Z_]+)\]', header_text)

    # Example
    extract_badges("### [LOG-043] - [DECISION] - Vertex AI...")
    # Returns: ["LOG-043", "DECISION"]

Color mapping is applied at render time:

    .badge-DECISION { background: #4CAF50; }
    .badge-EXEC { background: #2196F3; }
    .badge-DISCOVERY { background: #9C27B0; }
    .badge-VISION { background: #FF9800; }
    /* Unknown badges get neutral gray */
    .badge { background: #607D8B; }

##### 4.4 Scroll Sync

Outline → Content: Click outline item → smooth scroll to anchor.

Content → Breadcrumb: IntersectionObserver watches section headers. As user scrolls, breadcrumb updates to show current position in hierarchy.

    // Pseudocode
    const observer = new IntersectionObserver((entries) => {
        entries.forEach(entry => {
            if (entry.isIntersecting) {
                updateBreadcrumb(entry.target.dataset.path);
            }
        });
    }, { threshold: 0.1 });

    document.querySelectorAll('[data-section]').forEach(el => observer.observe(el));

#### 5. Mermaid Diagram Handling

##### 5.1 The Requirement

Mermaid code blocks must render as images, not raw code. Mobile browsers don't execute Mermaid JS reliably.

##### 5.2 The Solution: Pre-Render to PNG at Build Time

Build-time compilation using `mmdc` (Mermaid CLI):

    # Install mermaid-cli
    npm install -g @mermaid-js/mermaid-cli

    # Compile single diagram
    mmdc -i diagram.mmd -o diagram.png

Generator workflow:

1. Extract all ````` ``mermaid` ``` blocks from WORK.md

2. Write each to temp `.mmd` file

3. Run `mmdc` to compile → PNG

4. Encode PNG as base64

5. Replace code block with `<img src="data:image/png;base64,...">`

##### 5.3 Syntax Validation (Fail Fast)

If `mmdc` returns non-zero exit code, generator fails with clear error:

    ERROR: Mermaid syntax error in block at line 1289

      flowchart LR
          A --> B
          B ---> C  ← Invalid: triple arrow

    Fix the diagram before compiling.

Rationale: Silent failures produce blank images. Fail-fast ensures consistent render behavior across devices.

##### 5.4 Base64 Embedding (Single-File Portability)

    <!-- Before (raw mermaid) -->
    <pre><code class="language-mermaid">
    flowchart LR
        A --> B
    </code></pre>

    <!-- After (embedded PNG) -->
    <img
        class="mermaid-diagram"
        src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
        alt="Mermaid diagram: flowchart LR A --> B"
    />

Trade-off acknowledged: Each diagram adds ~50-200KB base64. For a worklog with 10 diagrams, that's 0.5-2MB. Acceptable for single-file portability.

#### 6. Filter Bar (Nice-to-Have, Phase 2)

    [ALL] [DECISION] [EXEC] [DISCOVERY] [VISION] [MILESTONE]

Behavior: Tap badge to filter outline to just that type. Useful for "show me all decisions" use case.

Implementation: CSS class toggle + JS filter on outline items.

#### 7. Technical Stack

Component | Choice | Rationale
--- | --- | ---
Parser | Python `mistune` or regex | Consistent GSD-Lite format makes regex viable
Mermaid CLI | `@mermaid-js/mermaid-cli` (mmdc) | Official tooling, reliable PNG output
Markdown Render | `marked.js` (CDN) or Python `mistune` | Lightweight, well-tested
Syntax Highlight | `highlight.js` (inline) | Code blocks need Python/bash coloring
Styling | Inline CSS | Single-file constraint
Interactivity | Vanilla JS | No framework needed for scroll sync

#### 8. Implementation Plan

Task ID | Description | Complexity
--- | --- | ---
READER-001a | Build markdown parser (extract LOG entries + hierarchy) | Medium
READER-001b | Build HTML renderer (outline + content + breadcrumb) | Medium
READER-001c | Integrate Mermaid CLI (pre-render + base64 embed) | Low
READER-001d | Implement scroll sync (IntersectionObserver) | Low
READER-001e | Mobile CSS polish (scrollbar, hamburger menu) | Low
READER-001f | End-to-end test on actual WORK.md | Low

Estimated LOC: ~400-600 Python (generator) + ~200 JS (inline) + ~150 CSS (inline)

#### 9. Plugin Architecture (Opt-In, Not Core)

##### 9.1 The Principle: Keep GSD-Lite Light

GSD-Lite core is a protocol + templates — markdown files and agent instructions. Zero runtime dependencies. Works with any LLM client that can read files.

The Worklog Reader introduces dependencies:

- Python 3.x

- `@mermaid-js/mermaid-cli` (npm package)

- Potentially `mistune` or other markdown parser

These MUST NOT pollute the base install.

##### 9.2 Distribution Model (Monorepo)

    gsd-lite/                         ← Single repo
    ├── gsd-lite/                     ← Core (always installed)
    │   ├── PROTOCOL.md
    │   ├── PROJECT.md
    │   ├── WORK.md
    │   └── templates/
    │       └── workflows/
    │
    └── plugins/                      ← Plugins (opt-in, same repo)
        └── reader/
            ├── generate_worklog_viewer.py
            ├── requirements.txt
            └── README.md

Why monorepo over multi-repo:

Concern | Multi-Repo | Monorepo
--- | --- | ---
Cross-cutting changes | Multiple PRs, version sync | Single PR
Discovery | "Where's the reader plugin?" | `plugins/` folder, obvious
CI/CD | Separate pipelines | One pipeline, conditional jobs
Maintenance burden | N repos to maintain | One repo

Installation (future):

    # Core only (default)
    gsd-lite install

    # With reader plugin (same repo, just installs extra deps)
    gsd-lite install --with-plugins reader

    # Or manual for just the plugin
    cd plugins/reader && pip install -r requirements.txt

##### 9.3 Why This Matters

User Type | Needs Reader? | Install Path
--- | --- | ---
New user exploring GSD-Lite | No | Core only
Light user with short worklogs | No | Core only
Power user with 5k+ line worklogs | Yes | Core + Reader plugin
Team lead reviewing team worklogs | Yes | Core + Reader plugin

The bar: If your WORK.md fits comfortably in VS Code outline, you don't need this. If you're scrolling for 30 seconds to find LOG-042, you do.

#### 10. Future Enhancements (Out of Scope for POC, Post-Plugin)

Enhancement | Description
--- | ---
Hosted version | Deploy to VM/GitHub Pages with auth
Write support | Add to INBOX.md from mobile
Search | Full-text search across worklog
Dark mode | Toggle for night reading
Split by LOG | Generate one HTML per log entry for faster loading

#### 11. Dependency Graph (For Future Agents)

    graph TD
        subgraph "Context Required"
            PROTOCOL[PROTOCOL.md] -->|GSD-Lite structure| LOG-047
            LOG-016[LOG-016: Stateless-First] -->|Journalism style rationale| LOG-047
            LOG-017[LOG-017: Housekeeping Vision] -->|Large worklog problem space| LOG-047
        end

        subgraph "This Entry"
            LOG-047[LOG-047: Worklog Reader Vision]
        end

        subgraph "Implementation"
            LOG-047 --> READER-001a[Parser]
            LOG-047 --> READER-001b[Renderer]
            LOG-047 --> READER-001c[Mermaid CLI]
        end

To onboard this decision:

1. LOG-016 — Understand why worklogs are dense (journalism style for zero-context agents)

2. LOG-017 — Understand the broader "large worklog" problem space (context rot, archival)

3. This entry (LOG-047) — The mobile consumption solution

#### 12. Cross-References and Citations

Source | What It Informed
--- | ---
User's TickTick GTD workflow | Ubiquitous access principle
VS Code Outline View | Target UX for mobile equivalent
MkDocs Material (https://squidfunk.github.io/mkdocs-material/) | Example of responsive design that hides outline on mobile — what NOT to do
Mermaid CLI docs (https://github.com/mermaid-js/mermaid-cli) | `mmdc` invocation for PNG output
WORK.md header grep (this session) | Confirmed consistent `### [LOG-NNN]` pattern for parsing

#### 13. Decision Record

ID | Decision | Rationale
--- | --- | ---
DECISION-047a | Single HTML file with base64-embedded PNGs | Portability trumps file size. AirDrop/file-share without folder management.
DECISION-047b | Pre-render Mermaid at build time | Mobile browsers can't reliably execute Mermaid JS. Build-time ensures consistent rendering.
DECISION-047c | Fail-fast on Mermaid syntax errors | Silent failures produce blank images. Explicit errors force fix before distribution.
DECISION-047d | Dynamic badge extraction (not hardcoded list) | Future-proofs for new log types. Parser extracts `[TAG]` patterns at runtime.
DECISION-047e | Horizontal scroll for outline items | Prevents header wrapping on narrow screens. Preserves scanability.
DECISION-047f | Force-visible scrollbar on mobile | Grabbable thumb enables quick navigation. Default hidden scrollbars break UX for long documents.
DECISION-047g | Plugin architecture (opt-in, not core) | GSD-Lite core must stay light. Reader is a power-user feature for dense worklogs. Opt-in install, no dependencies added to base install.
DECISION-047h | Monorepo structure (not separate repos) | Single `gsd-lite` repo with `plugins/` folder. Easier maintenance, single PR for cross-cutting changes, no multi-repo sync headaches. Plugins are still opt-in at install time.

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-047 (VISION: Worklog Reader design)

→ Dependency chain: LOG-047 ← LOG-017 (housekeeping problem space) ← LOG-016 (journalism rationale)

→ Next action: READER-001a — Build markdown parser for LOG entry extraction

Layer 2 — Global Context:

→ Architecture: GSD-Lite worklogs use `### [LOG-NNN] - [TYPE] - Title` format consistently

→ Patterns: Journalism-style dense logs optimized for zero-context agent onboarding

→ Data Flow: WORK.md → Parser → JSON AST → HTML Renderer → worklog.html

Fork paths:

- Continue to implementation → Start with READER-001a (parser skeleton)

- Discuss UX refinements → Reference Section 4 (UX Specification)

- Pivot to hosting discussion → Reference Section 9 (Future: VM/GitHub Pages)

### [LOG-048] - [EXEC] - Worklog Reader POC: Parser + HTML Renderer Implementation (READER-001a, READER-001b) - Task: READER-001

#### 1. Executive Summary

What was built: A working POC of the GSD-Lite Worklog Reader — a Python generator that compiles WORK.md (9,000+ lines) into a self-contained HTML viewer with mobile-first outline navigation, collapsible sections, and desktop/mobile responsive design.

Files created:

- `plugins/reader/parse_worklog.py` (~160 lines) — Markdown parser extracting LOG entries into JSON AST

- `plugins/reader/generate_worklog_viewer.py` (~800 lines) — HTML renderer with full viewer UX

Key decisions made:

- DECISION-048a: Positional parsing — only first two `[brackets]` extracted (ID, TYPE), rest is title

- DECISION-048b: Single-click collapses descendants, double-click expands all descendants

- DECISION-048c: Sidebar resizes as overlay (not pushing content)

- DECISION-048d: Desktop toggle button visible (same `📋` button works on both mobile and desktop)

#### 2. The Implementation Journey

##### 2.1 Parser Design (READER-001a)

The parser contract was kept intentionally simple per user preference ("keep it simple, get to POC"):

    ### [LOG-NNN] - [TYPE] - {title}
         ↑ pos 1    ↑ pos 2   ↑ everything else verbatim

Source: `plugins/reader/parse_worklog.py` lines 45-52

    LOG_HEADER_PATTERN = re.compile(
        r'^### \[LOG-(\d+)\] - \[([A-Z_]+)\] - (.+)$'
    )

Edge case discovered: LOG-041 has three bracketed items:

    ### [LOG-041] - [EXEC] - ~~title~~ [SUPERSEDED BY LOG-045] - Task: TASK-EVAL-001

Resolution (DECISION-048a): Sequence matters. Parser extracts positions 1-2 only. The `[SUPERSEDED BY LOG-045]` stays in title string. Strikethrough detection (`...`) handles visual dimming separately.

##### 2.2 Code Fence Bug

Symptom: Parser was extracting headers from inside fenced code blocks (e.g., `## Philosophy` inside a markdown example).

Root cause: No state tracking for code fences.

Fix: `plugins/reader/parse_worklog.py` lines 72-80

    # Track fenced code blocks - skip parsing inside them
    in_code_fence = False

    for line_num, line in enumerate(lines, start=1):
        # Toggle code fence state (``` or ~~~)
        if line.startswith('```') or line.startswith('~~~'):
            in_code_fence = not in_code_fence
            continue

        # Skip all parsing inside code fences
        if in_code_fence:
            continue

##### 2.3 HTML Renderer Evolution (READER-001b)

The renderer went through 6 iterations based on user feedback:

Version | Issue Fixed
--- | ---
v1 | Initial implementation
v2 | Tables broken → proper `<table><thead><tbody>` state machine
v3 | Mobile banner overflow → flex-shrink fixes
v4 | Collapse buttons + mobile scroll thumb
v5 | Resizable sidebar + alignment fix for same-level items
v6 | Desktop toggle button + collapse descendants on single-click

#### 3. Key UX Decisions

##### 3.1 DECISION-048b: Collapse/Expand Behavior

User requirement: "Single click = one level, double-click = expand all descendants"

Implementation: `plugins/reader/generate_worklog_viewer.py` lines 745-770

    // Single click - toggle this item; if collapsing, collapse descendants too
    btn.addEventListener('click', (e) => {
        e.stopPropagation();
        const item = btn.closest('.outline-item');
        const isCollapsed = item.classList.contains('collapsed');

        if (isCollapsed) {
            // Expanding - just expand this item
            item.classList.remove('collapsed');
        } else {
            // Collapsing - collapse this item AND all descendants
            item.classList.add('collapsed');
            item.querySelectorAll('.outline-item.has-children').forEach(child => {
                child.classList.add('collapsed');
            });
        }
    });

    // Double click - expand all descendants
    btn.addEventListener('dblclick', (e) => {
        e.stopPropagation();
        const item = btn.closest('.outline-item');
        item.classList.remove('collapsed');
        item.querySelectorAll('.outline-item.has-children').forEach(child => {
            child.classList.remove('collapsed');
        });
    });

Rationale: After double-click expands everything, user expects single-click to reset to collapsed state. Without collapsing descendants, the tree stays "stuck" expanded.

##### 3.2 DECISION-048c: Sidebar Resize as Overlay

User feedback: "I was more so want a way to scan the outline without breaking the content main."

Resolution: Removed `ResizeObserver` that adjusted content margin. Sidebar now resizes over content, not pushing it.

Source: `plugins/reader/generate_worklog_viewer.py` — CSS `resize: horizontal` on `.outline`, no JS margin adjustment.

##### 3.3 DECISION-048d: Desktop Toggle Button

User request: "Did we ever implement a button to hide/show sidebar on desktop?"

Implementation: Same `📋` button works on both platforms. JS detects viewport width and applies appropriate behavior:

    function toggleOutline() {
        if (window.innerWidth >= 768) {
            // Desktop: toggle hidden class + adjust content
            outline.classList.toggle('hidden');
            content.classList.toggle('full-width');
        } else {
            // Mobile: slide-in behavior
            outline.classList.toggle('open');
            overlay.classList.toggle('visible');
        }
    }

#### 4. Table Rendering Fix

Symptom: Tables showed raw markdown (`| col1 | col2 |`) instead of HTML tables. Inline formatting (bold, italic) inside cells was also broken.

Root cause: Original implementation detected table rows but didn't:

1. Wrap rows in `<table><thead><tbody>` structure

2. Apply inline formatting to cell content

Fix: `plugins/reader/generate_worklog_viewer.py` lines 167-195

    def format_inline(text: str) -> str:
        """Apply inline markdown formatting to text."""
        text = re.sub(r'\*\*(.+?)\*\*', r'<strong>\1</strong>', text)
        text = re.sub(r'\*(.+?)\*', r'<em>\1</em>', text)
        text = re.sub(r'`(.+?)`', r'<code>\1</code>', text)
        text = re.sub(r'~~(.+?)~~', r'<del>\1</del>', text)
        text = re.sub(r'\[(.+?)\]\((.+?)\)', r'<a href="\2">\1</a>', text)
        return text

    # In table rendering:
    if not in_table:
        html_lines.append('<table><thead><tr>')
        cells_html = ''.join(f'<th>{format_inline(html.escape(c))}</th>' for c in cells)
        html_lines.append(f'{cells_html}</tr></thead><tbody>')
        in_table = True
    else:
        cells_html = ''.join(f'<td>{format_inline(html.escape(c))}</td>' for c in cells)
        html_lines.append(f'<tr>{cells_html}</tr>')

#### 5. Scroll Navigation Fixes

##### 5.1 Banner Blocking Content

Symptom: When clicking outline links, content scrolled but the target header was hidden behind the sticky top bar.

Fix: CSS `scroll-margin-top` on all content elements:

    .content h1, .content h2, .content h3, .content h4, .content h5, .content p {
        scroll-margin-top: 60px;
    }

Source: `plugins/reader/generate_worklog_viewer.py` lines 513-516

##### 5.2 Mobile Scroll Thumb

User request: "There is no big scroll button I can grab apart from the native browser scroll button."

Implementation: Custom floating scroll thumb that tracks page position:

    <div class="scroll-thumb" id="scrollThumb">
        <div class="thumb-icon"><span>↕</span></div>
    </div>

Touch-draggable via `touchstart`/`touchmove`/`touchend` handlers. Position updates on scroll via `ResizeObserver`.

Source: `plugins/reader/generate_worklog_viewer.py` lines 690-700 (HTML), lines 780-810 (JS)

#### 6. Alignment Fix for Same-Level Items

Symptom: In LOG-047's outline, "1. Executive Summary" (no children) appeared left-aligned while "2. The Problem..." (has children, shows `▼`) appeared indented due to the toggle button width.

Fix: Items without children get padding to align with toggle button width:

    .outline-item:not(.has-children) .outline-row {
        padding-left: 20px;
    }

Source: `plugins/reader/generate_worklog_viewer.py` lines 425-428

#### 7. Architecture Diagram

    flowchart LR
        subgraph "Parser (parse_worklog.py)"
            MD[WORK.md] --> Regex[Regex Patterns]
            Regex --> AST[JSON AST]
        end

        subgraph "Renderer (generate_worklog_viewer.py)"
            AST --> Outline[Outline HTML]
            AST --> Content[Content HTML]
            MD --> MDRender[Markdown → HTML]
            Outline --> Template[HTML Template]
            Content --> Template
            MDRender --> Template
            Template --> HTML[worklog.html]
        end

        subgraph "Output Features"
            HTML --> Mobile[Mobile: Slide-in sidebar + scroll thumb]
            HTML --> Desktop[Desktop: Persistent sidebar + toggle]
            HTML --> Collapse[Collapsible sections]
            HTML --> Resize[Resizable sidebar]
        end

#### 8. File Manifest

File | Lines | Purpose
--- | --- | ---
`plugins/reader/parse_worklog.py` | ~160 | Extract LOG entries from WORK.md into JSON AST
`plugins/reader/generate_worklog_viewer.py` | ~800 | Generate self-contained HTML viewer

Usage:

    cd plugins/reader
    python generate_worklog_viewer.py ../../gsd-lite/WORK.md -o worklog.html
    open worklog.html  # Desktop
    # AirDrop worklog.html to phone for mobile testing

#### 9. Remaining Tasks (Not Implemented)

Task ID | Description | Status
--- | --- | ---
READER-001c | Mermaid CLI integration (pre-render diagrams to base64 PNG) | Pending
READER-001d | Scroll sync breadcrumb (IntersectionObserver) | Pending
READER-001e | Mobile CSS polish | Pending
READER-001f | End-to-end test on actual WORK.md | Pending

These are logged in LOG-047 Section 8 (Implementation Plan).

#### 10. Test Results

Feature | Desktop | Mobile
--- | --- | ---
Sidebar toggle | ✅ `📋` button shows/hides | ✅ Slide-in overlay
Collapse/expand | ✅ Single/double-click works | ✅ Same behavior
Resize sidebar | ✅ Drag handle, overlays content | ✅ Same behavior
Table rendering | ✅ Proper `<table>` structure | ✅ Same
Inline formatting in tables | ✅ Bold, italic, code, links | ✅ Same
Scroll navigation | ✅ Clears sticky header | ✅ Same
Scroll thumb | N/A (hidden on desktop) | ✅ Draggable `↕` button
Same-level alignment | ✅ Items align regardless of children | ✅ Same

#### 11. Decision Record

ID | Decision | Rationale
--- | --- | ---
DECISION-048a | Positional parsing (first two brackets only) | User preference for simplicity. `[SUPERSEDED BY...]` tag stays in title. Strikethrough handles dimming.
DECISION-048b | Single-click collapses descendants, double-click expands all | Prevents "stuck" state after double-click expand. Natural mental model.
DECISION-048c | Sidebar resizes as overlay, not pushing content | User wanted to scan outline without breaking reading flow in main content.
DECISION-048d | Same toggle button (`📋`) for mobile and desktop | Simplest implementation. JS detects viewport and applies appropriate behavior.

#### 12. Dependency Graph

    graph TD
        subgraph "Context Required"
            LOG-047[LOG-047: Worklog Reader Vision] -->|Design spec| LOG-048
            LOG-017[LOG-017: Housekeeping Vision] -->|Problem space: large worklogs| LOG-047
            LOG-016[LOG-016: Stateless-First] -->|Journalism rationale| LOG-047
        end

        subgraph "This Entry"
            LOG-048[LOG-048: Parser + Renderer Implementation]
        end

        subgraph "Outputs"
            LOG-048 --> Parser[plugins/reader/parse_worklog.py]
            LOG-048 --> Renderer[plugins/reader/generate_worklog_viewer.py]
        end

        subgraph "Future Work"
            LOG-048 -.-> READER-001c[Mermaid CLI]
            LOG-048 -.-> READER-001d[Scroll Sync]
        end

To onboard this implementation:

1. LOG-047 — Understand the vision and UX spec (Section 4: UX Specification, Section 7: Technical Stack)

2. This entry (LOG-048) — Implementation details, decisions, and test results

📦 STATELESS HANDOFF

Layer 1 — Local Context:

→ Last action: LOG-048 (EXEC: Parser + Renderer POC complete)

→ Dependency chain: LOG-048 ← LOG-047 (vision) ← LOG-017 (housekeeping problem space)

→ Next action: Choose from remaining tasks (READER-001c through 001f)

Layer 2 — Global Context:

→ Architecture: `plugins/reader/` contains opt-in tooling per DECISION-047g (plugin architecture)

→ Patterns: Parser uses positional extraction, renderer uses state machines for tables/code fences

→ Data Flow: WORK.md → parse_worklog.py → JSON AST → generate_worklog_viewer.py → worklog.html

Fork paths:

- Continue Mermaid integration → READER-001c (requires `npm install -g @mermaid-js/mermaid-cli`)

- Add scroll sync breadcrumb → READER-001d (IntersectionObserver implementation)

- Pivot to evaluation work → Resume TASK-EVAL-002d (Vertex AI integration)

- Test on phone → AirDrop worklog.html, review mobile UX

↕