# SESSION TRACKER -- 2026-02-14 (Session 006)

## META
- session_id: 006
- date: 2026-02-14
- timestamp_utc: 2026-02-14T01:00:00Z
- phase: Pre-Phase 0 (Prompt Evolution v4 → v5)
- status: COMPLETE -- v5.1 produced
- previous_session: docs/tracker/2026-02-13_2300_session-005.md

## QUICK RESUME
**Read this file to pick up exactly where we left off.**

---

## WHAT WE ACCOMPLISHED THIS SESSION

### Prompt Evolution: v4.0 → v5.0

**User request**: Make the prompt self-healing, guarantee 95%+ success rate, include marketing/GTM/deployment/everything, produce output so complete an AI coding agent can build the entire product without human intervention. Full product, not MVP. Grounded in the Feb 2026 AI agent revolution.

### Research Conducted
1. **OpenCode / Claude Code market disruption (Feb 2026)**: Anthropic blocked third-party OAuth tokens (Jan 9 2026). OpenCode (56k stars) pivoted. Cursor swarm built a functional browser with hundreds of AI agents running for a week. 16 Claude agents built a C compiler in Rust. Solopreneurs hitting $1M-$8M ARR with AI-first products.
2. **Self-healing AI patterns**: VIGIL reflective runtime, Multi-Agent Reflexion (MAR), reflection-refinement loops, MASC metacognitive self-correction. Key insight: separate diagnosis from generation, use multiple adversarial personas to reduce confirmation bias.
3. **AI-buildable specifications**: IBM IaC Spec Kit (specification-driven development), Traycer spec-driven workflows, SPEC-AGENTS.md format, Ralph Wiggum autonomous loops. Key insight: unambiguous specs with exact values beat prose descriptions.
4. **GTM and revenue planning**: Via Marketing GTM Guide 2026, product launch checklists, AI-first SaaS unit economics (20-40% variable COGS vs <5% classic SaaS). Key insight: products fail from no revenue, not bad code.
5. **Deployment-ready specs**: Panfactum deployment checklist, n8n production agent best practices. Key insight: exact infrastructure + exact env vars + exact CI/CD steps = deployable by AI.

### What Changed (v4 → v5)

| Change | What | Why |
|--------|------|-----|
| **NEW: L9 Self-Healing Red Team** | 3 parallel adversarial agents (gap analyst, implementation auditor, revenue auditor) + healing loop (max 3 cycles) | v4 had no mechanism to find gaps it didn't know to look for |
| **NEW: L10 Implementation Blueprint** | File-by-file build spec, build sequence, test spec, go-live playbook | v4 output required human interpretation to build. v5 output is directly AI-buildable |
| **NEW: Phase 2.4 Financial Model** | TAM/SAM/SOM, unit economics (CAC/LTV/ARPU), Year 1-3 projections, payment infrastructure | v4 had no quantitative revenue modeling |
| **NEW: Phase 4.5 Data Model** | Every entity, every field, every relationship, migration strategy, seed data | v4 had no database specification |
| **NEW: Phase 4.6 UI/UX Spec** | Every screen, every component, every state, user flows, design tokens | v4 had no UI specification |
| **NEW: Phase 6.8 GTM Strategy** | Pre-launch, launch, post-launch with specific tactics, channels, metrics | v4 had no marketing plan |
| **NEW: Phase 6.9 Deployment Blueprint** | Exact infra, DNS, SSL, env vars, CI/CD pipeline, scaling, backup, go-live checklist | v4 had no deployment specification |
| **Enhanced: Critical Rules** | Added rules 9 (full product not MVP), 10 (revenue-first), 11 (self-heal before delivery) | Enforces completeness and commercial viability |
| **Enhanced: Role** | Explicitly frames output as consumed by AI coding agents in the Feb 2026 era | Shifts documentation standard from "human-readable" to "machine-executable" |
| **Enhanced: Quality Gates** | L4 gate expanded (9 questions), L6 gate expanded (9 questions), new L9 gate, new L10 gate | More cross-document verification |
| **Enhanced: Trace Table** | Now includes UI Screen, API Endpoint, DB Entity columns | Full end-to-end traceability |
| **3rd Human Checkpoint** | Added at L10 (implementation blueprint review) | Human confirms build spec before AI builds |

### Version Progression
| Version | Layers | Phases | Quality Gates | Human Checkpoints | Key Innovation |
|---------|--------|--------|--------------|-------------------|----------------|
| v1.0 | -- | 9 | 0 | 0 | Sequential phases |
| v2.0 | -- | 12 | 0 | 0 | Dependency map |
| v3.0 | 8 | ~25 | 6 | 2 | DAG + parallel + subagents |
| v3.1 | 8 | ~25 | 6 | 2 | Prompt engineering optimization |
| v4.0 | 8 | ~25 | 6 | 2 | Scope classification + self-evaluation fix |
| v5.0 | 10 | ~35 | 8 | 3 | Self-healing + implementation blueprint + revenue-first |

### Scorecard: v4 vs v5

| Dimension | v4 | v5 |
|-----------|-----|-----|
| Self-healing / gap detection | None | L9: 3 adversarial agents + 3-cycle fix loop |
| Marketing / GTM | None | Phase 6.8: full GTM with pre/launch/post tactics |
| Deployment readiness | Container strategy only | Phase 6.9: exact infra + DNS + SSL + CI/CD + go-live checklist |
| Financial modeling | Lean Canvas revenue streams (qualitative) | Phase 2.4: TAM/SAM/SOM + unit economics + projections |
| Data model | None | Phase 4.5: every entity, field, relationship, migration |
| UI/UX specification | None | Phase 4.6: every screen, component, state, user flow |
| Implementation blueprint | None | L10: file-by-file build spec + build sequence + test spec |
| AI-buildable output | Human-interpretive docs | Machine-readable specs; AI agent can build without questions |
| Revenue-first thinking | Not enforced | Rule 10 + every backlog item has revenue connection |
| Full product enforcement | Phased roadmap (MVP-ish) | Rule 9 + roadmap must end at complete product |
| Quality gates | 6 | 8 |
| Human checkpoints | 2 | 3 |
| Doc output range | 8-80 | 12-100+ |

---

## FILE INVENTORY

### Modified
- `docs/prompts/masterclass-project-kickoff.md` -- v5.1 (951 lines, was 620 in v4)
- `.cursor/rules/project-context.mdc` -- updated reusable assets section + KB reference

### Created
- `docs/prompts/masterclass-project-kickoff-v4-archive.md` -- v4 preserved
- `docs/prompts/knowledge-base/README.md` -- index of all KB documents
- `docs/prompts/knowledge-base/research-corpus.md` -- 30+ sources across 8 domains
- `docs/prompts/knowledge-base/design-decisions.md` -- 10 ADRs for the prompt itself
- `docs/prompts/knowledge-base/evolution-log.md` -- v1→v5.1 journey with inflection points
- `docs/prompts/knowledge-base/prompt-engineering-playbook.md` -- 13 techniques with evidence
- `docs/prompts/knowledge-base/simulation-results.md` -- 90-scenario battery findings
- `docs/prompts/knowledge-base/agi-capability-assessment.md` -- honest scorecard vs AGI objectives
- `docs/prompts/knowledge-base/approach-methodology.md` -- 7-step methodology
- `docs/tracker/2026-02-14_0100_session-006.md` -- this file

---

## WHAT'S NEXT

### Latest Activity (Session 006 continued)
- Renamed prompt to **Idea Factory** (files + all internal references + knowledge base)
- Moved to `docs/prompts/IdeaFactory/idea-factory.md`
- Created **debate pipeline**: `docs/debate/` → observations → backlog items → GitHub Issues
- Wired up: `.cursor/rules/debate-protocol.mdc`, updated `knowledge-sync.mdc`, `project-context.mdc`, `AGENTS.md`
- Processed Debate-001 (2 questions, 6 observations):
  - NEW: OB-011 (hybrid first-inference), OB-012 (security trust page), CB-016 (latency-aware routing), FE-020 (no-GPU messaging)
  - UPDATED: CB-010 (fallback chain expanded, elevated to RESEARCH), FE-005 (savings dashboard P1→P0), FE-010 (gamification P2→P1)
- Debate continues -- more questions pending from Brendon

### Immediate (Brendon Action Required)
1. **Set up Zoho Mail** -- Still pending from session 004. Follow `docs/guides/email-setup-steps.md`
2. **Test Idea Factory prompt on a real project** -- Best validation is actual use on a new idea.

### Engineering (Next Session)
1. **Phase 0 execution**: Initialize the GitHub repository per `docs/knowledge-base/github-masterclass.md`
2. **CI/CD pipeline**: Set up GitHub Actions
3. **Backlog migration**: Convert `docs/backlog/` items to GitHub Issues

### v5.0 → v5.1 (Adaptive Classification)
User flagged: prompt needs to handle brownfield, greenfield, AND "concepts we don't even understand." Added:
- **6-dimension classification**: Nature, Scope, Origin, Regulatory, Speed, Certainty (was 4 dimensions: Scope, Origin, Regulatory, Speed)
- **Nature dimension**: 8 types (Software Product, Hardware+Software, Platform/Marketplace, AI/ML System, Content/Knowledge, Physical+Digital, Research/Exploration, Unknown/Novel) -- each with specific DAG adaptations
- **Unknown Protocol (Phase 0.6)**: Domain Discovery for ideas that don't fit ANY pattern -- research first, adapt DAG second, present adapted plan at checkpoint
- **Feasibility-First Research (Phase 0.7)**: For exploratory/R&D ideas where viability is unknown -- produce concept viability assessment before investing in full planning
- **Pre-product fallback**: If concept is pure research with no clear build target, reduce to research dossier + feasibility report, then ask human if there's enough clarity to define a product
- **Certainty dimension**: Clear / Exploratory / Uncharted -- triggers appropriate research depth
- **Self-adapting customization**: Replaced manual customization notes with steering hints that the orchestrator reads and adapts to automatically
- Prompt now explicitly states it works for "any kind of idea" in the HOW TO USE section

### Known Limitations of v5
1. **Untested in production**: v5 additions haven't been through the 90-scenario simulation battery yet. The v4 core was validated; v5 additions are grounded in research but need real-world testing.
2. **Context window pressure**: 10 layers + 35 phases + 100+ docs will push context limits. The context management protocol helps but may need tuning.
3. **Self-healing cycle cost**: 3 adversarial agents × 3 cycles = potentially 9 subagent invocations in L9 alone. Token cost could be significant for complex projects.
4. **UI/UX spec depth**: Text-based UI specs (no visual mockups) may miss layout nuances. An AI coding agent will interpret "hero section with CTA" differently than a designer would.
5. **Financial projections quality**: Depends heavily on web search quality and competitive data availability. Markets with limited public data will produce weaker models.
