Baseline Quality Assessment

16

yaleh

Achieve comprehensive baseline (V_meta ≥0.40) in iteration 0 to enable rapid convergence. Use when planning iteration 0 time allocation, domain has established practices to reference, rich historical data exists for immediate quantification, or targeting 3-4 iteration convergence. Provides 4 quality levels (minimal/basic/comprehensive/exceptional), component-by-component V_meta calculation guide, and 3 strategies for comprehensive baseline (leverage prior art, quantify baseline, domain universality analysis). 40-50% iteration reduction when V_meta(s₀) ≥0.40 vs <0.20. Spend 3-4 extra hours in iteration 0, save 3-6 hours overall.

Dependency Health

16

yaleh

Security-first dependency management methodology with batch remediation, policy-driven compliance, and automated enforcement. Use when security vulnerabilities exist in dependencies, dependency freshness low (outdated packages), license compliance needed, or systematic dependency management lacking. Provides security-first prioritization (critical vulnerabilities immediately, high within week, medium within month), batch remediation strategy (group compatible updates, test together, single PR), policy-driven compliance framework (security policies, freshness policies, license policies), and automation tools for vulnerability scanning, update detection, and compliance checking. Validated in meta-cc with 6x speedup (9 hours manual to 1.5 hours systematic), 3 iterations, 88% transferability across package managers (concepts universal, tools vary by ecosystem).

CI/CD Optimization

16

yaleh

Comprehensive CI/CD pipeline methodology with quality gates, release automation, smoke testing, observability, and performance tracking. Use when setting up CI/CD from scratch, build time over 5 minutes, no automated quality gates, manual release process, lack of pipeline observability, or broken releases reaching production. Provides 5 quality gate categories (coverage threshold 75-80%, lint blocking, CHANGELOG validation, build verification, test pass rate), release automation with conventional commits and automatic CHANGELOG generation, 25 smoke tests across execution/consistency/structure categories, CI observability with metrics tracking and regression detection, performance optimization including native-only testing for Go cross-compilation. Validated in meta-cc with 91.7% pattern validation rate (11/12 patterns), 2.5-3.5x estimated speedup, GitHub Actions native with 70-80% transferability to GitLab CI and Jenkins.

API Design

16

yaleh

Systematic API design methodology with 6 validated patterns covering parameter categorization, safe refactoring, audit-first approach, automated validation, quality gates, and example-driven documentation. Use when designing new APIs, improving API consistency, implementing breaking change policies, or building API quality enforcement. Provides deterministic decision trees (5-tier parameter system), validation tool architecture, pre-commit hook patterns. Validated with 82.5% cross-domain transferability, 37.5% efficiency gains through audit-first refactoring.

Code Refactoring

16

yaleh

BAIME-aligned refactoring protocol for Go hotspots (CLIs, services, MCP tooling), including automated metrics (e.g., metrics-cli, metrics-mcp) and documentation.

Rapid Convergence

16

yaleh

Achieve 3-4 iteration methodology convergence (vs standard 5-7) when clear baseline metrics exist, domain scope is focused, and direct validation is possible. Use when you have V_meta baseline ≥0.40, quantifiable success criteria, retrospective validation data, and generic agents are sufficient. Enables 40-60% time reduction (10-15 hours vs 20-30 hours) without sacrificing quality. Prediction model helps estimate iteration count during experiment planning. Validated in error recovery (3 iterations, 10 hours, V_instance=0.83, V_meta=0.85).

Observability Instrumentation

16

yaleh

Comprehensive observability methodology implementing three pillars (logs, metrics, traces) with structured logging using Go slog, Prometheus-style metrics, and distributed tracing patterns. Use when adding observability from scratch, logs unstructured or inadequate, no metrics collection, debugging production issues difficult, or need performance monitoring. Provides structured logging patterns (contextual logging, log levels DEBUG/INFO/WARN/ERROR, request ID propagation), metrics instrumentation (counter/gauge/histogram patterns, Prometheus exposition), tracing setup (span creation, context propagation, sampling strategies), and Go slog best practices (JSON formatting, attribute management, handler configuration). Validated in meta-cc with 23-46x speedup vs ad-hoc logging, 90-95% transferability across languages (slog specific to Go but patterns universal).

Methodology Bootstrapping

16

yaleh

Apply Bootstrapped AI Methodology Engineering (BAIME) to develop project-specific methodologies through systematic Observe-Codify-Automate cycles with dual-layer value functions (instance quality + methodology quality). Use when creating testing strategies, CI/CD pipelines, error handling patterns, observability systems, or any reusable development methodology. Provides structured framework with convergence criteria, agent coordination, and empirical validation. Validated in 8 experiments with 100% success rate, 4.9 avg iterations, 10-50x speedup vs ad-hoc. Works for testing, CI/CD, error recovery, dependency management, documentation systems, knowledge transfer, technical debt, cross-cutting concerns.

Agent Prompt Evolution

16

yaleh

Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.

Testing Strategy

16

yaleh

Systematic testing methodology for Go projects using TDD, coverage-driven gap closure, fixture patterns, and CLI testing. Use when establishing test strategy from scratch, improving test coverage from 60-75% to 80%+, creating test infrastructure with mocks and fixtures, building CLI test suites, or systematizing ad-hoc testing. Provides 8 documented patterns (table-driven, golden file, fixture, mocking, CLI testing, integration, helper utilities, coverage-driven gap closure), 3 automation tools (coverage analyzer 186x speedup, test generator 200x speedup, methodology guide 7.5x speedup). Validated across 3 project archetypes with 3.1x average speedup, 5.8% adaptation effort, 89% transferability to Python/Rust/TypeScript.

Retrospective Validation

16

yaleh

Validate methodology effectiveness using historical data without live deployment. Use when rich historical data exists (100+ instances), methodology targets observable patterns (error prevention, test strategy, performance optimization), pattern matching is feasible with clear detection rules, and live deployment has high friction (CI/CD integration effort, user study time, deployment risk). Enables 40-60% time reduction vs prospective validation, 60-80% cost reduction. Confidence calculation model provides statistical rigor. Validated in error recovery (1,336 errors, 23.7% prevention, 0.79 confidence).

Error Recovery

16

yaleh

Comprehensive error handling methodology with 13-category taxonomy, diagnostic workflows, recovery patterns, and prevention guidelines. Use when error rate >5%, MTTD/MTTR too high, errors recurring, need systematic error prevention, or building error handling infrastructure. Provides error taxonomy (file operations, API calls, data validation, resource management, concurrency, configuration, dependency, network, parsing, state management, authentication, timeout, edge cases - 95.4% coverage), 8 diagnostic workflows, 5 recovery patterns, 8 prevention guidelines, 3 automation tools (file path validation, read-before-write check, file size validation - 23.7% error prevention). Validated with 1,336 historical errors, 85-90% transferability across languages/platforms, 0.79 confidence retrospective validation.