Methodology Bootstrapping
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.
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2025年10月26日 15:19
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CI/CD Optimization
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.
Retrospective Validation
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).
Technical Debt Management
yaleh
Systematic technical debt quantification and management using SQALE methodology with value-effort prioritization, phased paydown roadmaps, and prevention strategies. Use when technical debt unmeasured or subjective, need objective prioritization, planning refactoring work, establishing debt prevention practices, or tracking debt trends over time. Provides 6 methodology components (measurement with SQALE index, categorization with code smell taxonomy, prioritization with value-effort matrix, phased paydown roadmap, trend tracking system, prevention guidelines), 3 patterns (SQALE-based quantification, code smell taxonomy mapping, value-effort prioritization), 3 principles (high-value low-effort first, SQALE provides objective baseline, complexity drives maintainability debt). Validated with 4.5x speedup vs manual approach, 85% transferability across languages (Go, Python, JavaScript, Java, Rust), SQALE industry-standard methodology.
Testing Strategy
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.
Code Refactoring
yaleh
BAIME-aligned refactoring protocol for Go hotspots (CLIs, services, MCP tooling) with automated metrics (e.g., metrics-cli, metrics-mcp) and documentation.
raffle-winner-picker
ComposioHQ
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
webapp-testing
ComposioHQ
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
ios-simulator-skill
conorluddy
This Claude Skill can be used to build, test, and automate iOS apps. 13 production-ready scripts including ultra token-efficient xcode build automation, log monitoring, intelligent simulator selection, and accessibility-driven UI simulator navigation.
cloudflare-browser-rendering
mrgoonie
Guide for implementing Cloudflare Browser Rendering - a headless browser automation API for screenshots, PDFs, web scraping, and testing. Use when automating browsers, taking screenshots, generating PDFs, scraping dynamic content, extracting structured data, or testing web applications. Supports REST API, Workers Bindings (Puppeteer/Playwright), MCP servers, and AI-powered automation. (project)
Systematic Debugging
mrgoonie
Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions.
Root Cause Tracing
mrgoonie
Systematically trace bugs backward through call stack to find original trigger
Verification Before Completion
mrgoonie
Run verification commands and confirm output before claiming success