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CI/CD pipeline patterns for GitHub Actions including test, build, deploy stages, caching, matrix testing, and release automation. Use when setting up or modifying CI/CD pipelines.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | ci-cd |
| description | CI/CD pipeline patterns for GitHub Actions including test, build, deploy stages, caching, matrix testing, and release automation. Use when setting up or modifying CI/CD pipelines. |
| compatibility | Requires GitHub repository |
| metadata | {"version":"1.0"} |
Need CI/CD → What stack?
├─ Node/TypeScript → Use assets/ci-node.yml.template
├─ Python → Use assets/ci-python.yml.template
└─ Multi-stack → Combine relevant templates
Push/PR → Lint → Test → Build → Deploy (main only)
- uses: actions/cache@v4
with:
path: ~/.npm
key: npm-${{ hashFiles('**/package-lock.json') }}
restore-keys: npm-
| Anti-Pattern | Fix |
|---|---|
| No caching | Cache dependencies |
npm install in CI | Use npm ci (deterministic) |
| Skip tests for speed | Parallelize instead |
| Deploy on every push | Use tags/releases |
| Manual version bumps | Automate with semantic-release |
Web accessibility checklist covering WCAG guidelines, ARIA patterns, keyboard navigation, color contrast, screen reader support, and form accessibility. Use when building UI components, reviewing frontend code, or when accessibility is mentioned.
When to refactor vs leave alone, code smell detection, safe refactoring steps, and common transformation patterns. Use when asked to refactor code, clean up technical debt, or when reviewing code that shows structural problems.
Authentication and authorization patterns including JWT vs sessions, OAuth flows, token refresh, RBAC, and password handling. Use when implementing login, signup, token management, role-based access, or reviewing auth security.
Structured research methodology with scoping, multi-source collection, triangulation, source credibility scoring, and anti-hallucination guards. Use when investigating technologies, comparing tools, evaluating libraries, exploring unfamiliar codebases, or producing research reports.
Build MCP (Model Context Protocol) servers in TypeScript or Python. Use when creating custom tools, resources, or prompts that extend AI assistant capabilities via MCP.
Structured logging patterns, log levels, trace IDs, and what to log vs what never to log. Use when setting up logging, choosing log levels, implementing request tracing, or reviewing log output quality.