# Get-er-DONE Methodology

## META
- created: 2026-02-14
- type: methodology reference
- companion-to: IdeaFactory (ideation/specification)
- references: orchestration-strategy.md, test-strategy.md, github-strategy.md

## OVERVIEW
Get-er-DONE is a build orchestration protocol for AI coding agents. It takes fully-specified project documentation (output from IdeaFactory or equivalent) and transforms it into working, tested, deployed software using coordinated AI agent swarms. Version: v1.3 (combined lifecycle simulation-hardened).

## CORE TENETS
1. **Spec-Driven Development (SDD)**: Specifications are the source of truth. Tests are derived from specs. Code satisfies tests. If spec changes, tests change, code follows. PRD is ultimate truth.
2. **Context-Focused Work Units**: Every agent task fits within a single context window (<80K tokens). Only relevant files included. Quality is inversely proportional to context noise.
3. **CIV Pattern**: Coordinator-Implementor-Verifier. Three-role separation ensures focused implementation and independent verification. Inspired by AOrchestra and AgentOrchestra research.
4. **TDD-First**: RED-GREEN-REFACTOR is non-negotiable. Tests written before code. Spec tests are living specifications that double as ongoing health checks.
5. **GitHub-Native**: GitHub is the single source of truth. Issues drive work. PRs encapsulate changes. Actions enforce quality. Projects track progress.
6. **Reusable Test Ecosystem**: Test battery serves three audiences: Developer (during build), CI (on every PR), Operator (post-deploy). Same tests, different execution contexts.
7. **Nature-Aware Build**: Adapts build strategy based on IdeaFactory's classification (AI/ML, Hardware, Platform, Content, Research). Not all projects are standard web apps.
8. **Brownfield-Ready**: Extends existing code rather than replacing it. Preserves existing tests. Supports migration and pivot origins.
9. **Traceability**: Count-based cross-referencing ensures every spec item has a corresponding build task and test. Nothing falls through cracks.

## RELATIONSHIP TO IDEAFACTORY
IdeaFactory produces the WHAT: PRD, architecture, design system, wireframes, component inventory, OpenAPI spec, implementation blueprint, test strategy, financial model, threat model, AGENTS.md, CONTEXT.md.
Get-er-DONE produces the HOW and DOES IT: takes those docs and builds the actual software, tests, CI/CD, GitHub integration, and deployment.

**File Ownership**: IdeaFactory PRODUCES initial AGENTS.md, CONTEXT.md, .cursor/rules/. Get-er-DONE EVOLVES them (appends, never deletes).
**Test Ownership**: IdeaFactory's test-strategy.md specifies WHAT to test. Get-er-DONE REALIZES it (writes actual test code).
**Classification Flow**: IdeaFactory's 6-dimension classification (Nature/Scope/Origin/Regulatory/Speed/Certainty) in CONTEXT.md drives Get-er-DONE's build adaptation.

Sequence: IdeaFactory -> Meta-Orchestrator Handoff -> Get-er-DONE -> Working Product -> Evolution

## RELATIONSHIP TO META-ORCHESTRATOR
Meta-Orchestrator (`/home/dev/projects/IdeaFactory.DEV/prompts/Meta-Orchestrator/meta-orchestrator.md`) coordinates the full lifecycle. Key protocols:
- **Handoff Protocol**: Staleness detection (>30 days), traceability verification (count alignment), classification flow, build-readiness assessment
- **Feedback Routing Protocol**: When build reveals spec gaps, routes to specific IdeaFactory layer with cascade check. Max 3 round-trips per issue.
- **Evolution Routing**: Post-ship changes routed to right protocol (small feature -> Get-er-DONE, large feature -> IdeaFactory + Get-er-DONE, deprecation -> reverse flow)
- **File Ownership Protocol**: Prevents AGENTS.md/CONTEXT.md overwrites at handoff

## KEY PATTERNS

### Task Sizing (fits context window)
- XS (<1h, <10K context): Single file, simple change
- S (1-4h, 10-30K): Single component with tests
- M (4-8h, 30-60K): Multi-file feature
- L (1-2d): MUST decompose into S/M tasks
- XL (2-5d): ALWAYS decompose

### Context Budget (COMPASS-inspired)
- System: 2K | Spec: 3K | Code: 20-50K | Tests: 5K | Progress: 2K
- Total: 32-62K, max 80K, leaves 50%+ for reasoning
- Anti-pattern: Never dump entire codebase into any subagent

### Sprint Structure
- 2-week sprints aligned with implementation blueprint phases
- Each sprint produces deployable increment
- Sprint review: demo, metrics, retrospective

### Quality Gates
- Task-level: tests pass, linter clean, typed, no regressions, acceptance criteria met
- Sprint-level: spec tests pass, contract tests pass, demo works, docs updated
- Release-level: full suite, performance benchmarks, security scan, changelog

## RESEARCH GROUNDING (Feb 2026)

Full research corpus with detailed findings, application mappings, and limitations: `/home/dev/projects/IdeaFactory.DEV/prompts/Get-er-DONE/knowledge-base/research-corpus.md`

Summary (12 sources):

### Primary (8)
1. **AOrchestra** (arxiv 2602.03786, Feb 2026): Agent tuple model (Instruction, Context, Tools, Model). 16.28% improvement over baselines on GAIA/SWE-Bench. Inspired CIV pattern and Task Specification Format.
2. **COMPASS** (arxiv 2510.08790, Oct 2025): Context management for long-horizon tasks. 20% accuracy improvement. Inspired Context Budget Protocol (80K ceiling, structured allocation, anti-patterns).
3. **AgentOrchestra** (arxiv 2506.12508, Jun 2025): Hierarchical multi-agent with sub-goal formulation. Reinforced Coordinator -> Implementor/Verifier hierarchy.
4. **TestForge** (arxiv 2503.14713, Mar 2025): Iterative test generation. 84.3% pass rate, $0.63/file. Validates TDD feedback loop and max-2-retry policy.
5. **Nexus** (arxiv 2510.26423, Oct 2025): Multi-agent test oracle synthesis. Validates Verifier role checking test meaningfulness, not just pass/fail.
6. **GitHub Agentic Workflows** (github.blog, Feb 2026 tech preview): Markdown-defined automation with Copilot/Claude/Codex. Shaped L2 GitHub Strategy and issue-driven development.
7. **GitHub Copilot Coding Agent** (docs.github.com, Aug 2025+): AGENTS.md + copilot-setup-steps.yml + issue-to-PR automation. Primary execution path for agent-ready tasks.
8. **Continuous AI** (github.blog, Feb 2026): Background agents for judgment-heavy CI. Validates separation of deterministic CI checks vs agent-assisted verification.

### Supplementary (4)
9. **Empirical Study of AI Agent Testing** (arxiv 2509.19185): 70%+ testing effort on deterministic components, <5% on planning, ~1% on prompts. Validates spec test emphasis.
10. **Agent-Testing Agent (ATA)** (arxiv 2508.17393): Meta-agent testing in 20-30 min vs days. Informed Verifier agent design.
11. **SWE-Gym** (arxiv 2412.21139): 2,438 Python tasks with executable tests. Validates spec-test-as-specification pattern.
12. **Intent** (Augment Code): Git worktree isolation + CIV pattern. Inspired CIV naming and one-task-one-PR principle.

## v1.1 ADDITIONS (Research Operationalization)
All 12 research sources now have corresponding protocols in the prompt:
- **Model Selection Protocol** (AOrchestra): Match model tier to task complexity. Frontier for Coordinator, fast for XS/S Implementor tasks.
- **Goal Drift Prevention** (COMPASS): Meta-Thinker check every 3 tasks. Compare recent outputs against PRD objectives.
- **Adaptive Task Difficulty** (SWE-Gym): Expected success rates by task type (config 90%, single-file 70%, multi-file 50%, AI component 30%).
- **Intelligent Component Testing** (Empirical Study): Property-based + statistical + boundary tests for non-deterministic components.
- **Git Worktree Isolation** (Intent): Parallel agents in isolated worktrees for conflict-free execution.
- **Sprint Retrospective** (feedback loop): Structured metrics review + learnings fed into next sprint.
- **Verifier Deliberation** (Nexus): Structured reasoning with counter-specifications, not just pass/fail checklist.
- **Deterministic vs Judgment Separation** (Continuous AI): Run all automated checks first, AI judgment only after deterministic gates pass.
- **Self-Healing Reflexion Loop** (L7.5): 3 adversarial attackers (spec compliance, integration/edge, security/reliability) + heal cycle, max 2 iterations.

## v1.2 ADDITIONS (Individual Simulation Hardening)
100-scenario battery across 5 attack vectors (tech stack diversity, project scale, input spec quality, platform/environment, adversarial/failure modes):
- Platform Capability Check with graceful degradation (6 capabilities)
- Scope Classification (Micro/Small/Medium/Complex with layer skip rules)
- Stack Adaptation Protocol (6+ language-tool mapping tables)
- Spec Validation Phase (L1: consistency, contradiction, completeness)
- Merge Conflict, Flaky Test, Dependency Verification, Architecture Correction protocols
- Semantic Verification in Verifier (intent-matching, not just assertion-passing)

## v1.3 ADDITIONS (Combined Lifecycle Simulation)
100-scenario battery testing IdeaFactory + Get-er-DONE SYMBIOSIS (handoff integrity, feedback loops, evolution cycles, context continuity, adversarial edge cases). Score: 3.6/10 -> 8.4/10:
- **Nature Awareness Protocol**: Reads IdeaFactory 6-dimension classification, adapts build for AI/ML, Hardware, Platform, Content, Research
- **Brownfield Build Protocol**: Reads existing-system-audit.md, extends rather than replaces, preserves existing tests
- **Traceability Verification** (L1.1.5): Count-based cross-reference of components/endpoints/pages/features/criteria before build
- **File Ownership Protocol**: IdeaFactory PRODUCES, Get-er-DONE EVOLVES additively for AGENTS.md/CONTEXT.md
- **Expanded Required Docs**: Financial model and threat model promoted from optional. Knowledge base and debate findings as context sources.
- **Container-First Alignment**: "Container-first with appropriate deploy strategy" (not "everything in Docker") -- matches IdeaFactory
- **Scope Alignment**: Micro/Small/Medium/Complex names identical to IdeaFactory

## ANTI-PATTERNS
- Passing entire codebase to subagents (context pollution -- COMPASS)
- Skipping TDD for speed (technical debt compounds faster with AI -- TestForge)
- Single-agent monolithic builds (no verification, drift accumulates -- AOrchestra, AgentOrchestra)
- Manual testing only (not reproducible, not CI-compatible -- Continuous AI)
- External tools replacing GitHub (sync overhead, split brain)
- Using frontier models for XS tasks (wasteful -- AOrchestra model selection)
- Treating AI components same as deterministic ones for testing (Empirical Study: <5% coverage)
- Skipping retrospectives (same mistakes repeat across sprints)
- Verifier as passive checker instead of active deliberator (Nexus)
- **Both prompts producing same file without ownership rules** (AGENTS.md/CONTEXT.md collision -- Combined Simulation F2)
- **Vague feedback routing** ("go back to IdeaFactory" instead of "go to IdeaFactory L4.3, cascade check L4.2" -- Combined Simulation F5)
- **Losing classification at handoff** (IdeaFactory classifies as AI/ML but Get-er-DONE builds as standard web app -- Combined Simulation F4)
- **No traceability check** (IdeaFactory specs 25 components but Get-er-DONE only builds 15 -- Combined Simulation F7)
- **Building on stale specs** (6 months between spec and build, APIs deprecated -- Combined Simulation F12)
- **Contradicting rules between prompts** (one says "Docker always", other says "container-first with escape" -- Combined Simulation F3)
