Initial commit

skills/methodology-bootstrapping/reference/overview.md

@@ -0,0 +1,149 @@
+# Methodology Bootstrapping - Overview
+
+**Unified framework for developing software engineering methodologies through systematic observation, empirical validation, and automated enforcement.**
+
+## Philosophy
+
+> The best methodologies are not **designed** but **evolved** through systematic observation, codification, and automation of successful practices.
+
+Traditional methodologies are:
+- Theory-driven (based on principles, not data)
+- Static (created once, rarely updated)
+- Prescriptive (one-size-fits-all)
+- Manual (require discipline, no automated validation)
+
+**Methodology Bootstrapping** enables methodologies that are:
+- Data-driven (based on empirical observation)
+- Dynamic (continuously evolving)
+- Adaptive (project-specific)
+- Automated (enforced by CI/CD)
+
+## Three-Layer Architecture
+
+The framework integrates three complementary layers:
+
+### Layer 1: Core Framework (OCA Cycle)
+- **Observe**: Instrument and collect data
+- **Codify**: Extract patterns and document
+- **Automate**: Convert to automated checks
+- **Evolve**: Apply methodology to itself
+
+**Output**: Three-tuple (O, Aₙ, Mₙ)
+- O = Task output (code, docs, system)
+- Aₙ = Converged agent set (reusable)
+- Mₙ = Converged meta-agent (transferable)
+
+### Layer 2: Scientific Foundation
+- Hypothesis formation
+- Experimental validation
+- Statistical analysis
+- Pattern recognition
+- Empirical evidence
+
+### Layer 3: Quantitative Evaluation
+- **V_instance(s)**: Domain-specific task quality
+- **V_meta(s)**: Methodology transferability quality
+- Convergence criteria
+- Optimization mathematics
+
+## Key Insights
+
+### Insight 1: Dual-Layer Value Functions
+
+Optimizing only task quality (V_instance) produces good code but no reusable methodology.
+Optimizing both layers creates **compound value**: good code + transferable methodology.
+
+### Insight 2: Self-Referential Feedback Loop
+
+The methodology can improve itself:
+1. Use tools to observe methodology development
+2. Extract meta-patterns from methodology creation
+3. Codify patterns as methodology improvements
+4. Automate methodology validation
+
+This creates **closed loop**: methodologies optimize methodologies.
+
+### Insight 3: Convergence is Mathematical
+
+Methodology is complete when:
+- System stable (no agent evolution)
+- Dual threshold met (V_instance ≥ 0.80, V_meta ≥ 0.80)
+- Diminishing returns (ΔV < epsilon)
+
+No guesswork - the math tells you when done.
+
+### Insight 4: Agent Specialization Emerges
+
+Don't predetermine agents. Let specialization emerge:
+- Start with generic agents (coder, tester, doc-writer)
+- Identify gaps during execution
+- Create specialized agents only when needed
+- 8 experiments: 0-5 specialized agents per experiment
+
+### Insight 5: Meta-Agent M₀ is Sufficient
+
+Across all 8 experiments, the base Meta-Agent (M₀) never needed evolution:
+- M₀ capabilities: observe, plan, execute, reflect, evolve
+- Sufficient for all domains tested
+- Agent specialization handles domain gaps
+- Meta-Agent handles coordination
+
+## Validated Outcomes
+
+**From 8 experiments** (testing, error recovery, CI/CD, observability, dependency health, knowledge transfer, technical debt, cross-cutting concerns):
+
+- **Success rate**: 100% (8/8 converged)
+- **Efficiency**: 4.9 avg iterations, 9.1 avg hours
+- **Quality**: V_instance 0.784, V_meta 0.840
+- **Transferability**: 70-95%
+- **Speedup**: 3-46x vs ad-hoc
+
+## When to Use
+
+**Ideal conditions**:
+- Recurring problem requiring systematic approach
+- Methodology needs to be transferable
+- Empirical data available for observation
+- Automation infrastructure exists (CI/CD)
+- Team values data-driven decisions
+
+**Sub-optimal conditions**:
+- One-time ad-hoc task
+- Established industry standard fully applies
+- No data available (greenfield)
+- No automation infrastructure
+- Team prefers intuition over data
+
+## Prerequisites
+
+**Tools**:
+- Session analysis (meta-cc MCP server or equivalent)
+- Git repository access
+- Code metrics tools (coverage, linters)
+- CI/CD platform (GitHub Actions, GitLab CI)
+- Markdown editor
+
+**Skills**:
+- Basic data analysis (statistics, patterns)
+- Software development experience
+- Scientific method understanding
+- Documentation writing
+
+**Time investment**:
+- Learning framework: 4-8 hours
+- First experiment: 6-15 hours
+- Subsequent experiments: 4-10 hours (with acceleration)
+
+## Success Criteria
+
+| Criterion | Target | Validation |
+|-----------|--------|------------|
+| Framework understanding | Can explain OCA cycle | Self-test |
+| Dual-layer evaluation | Can calculate V_instance, V_meta | Practice |
+| Convergence recognition | Can identify completion | Apply criteria |
+| Methodology documentation | Complete docs | Peer review |
+| Transferability | ≥85% reusability | Cross-project test |
+
+---
+
+**Next**: Read [observe-codify-automate.md](observe-codify-automate.md) for detailed OCA cycle explanation.