Measurement-driven code refactoring — profile before changing, measure after, keep only if metrics improve. Covers complexity reduction, extraction patterns, and bulk refactoring for mechanical changes across many files.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Measure before. Refactor. Measure after. Keep only if it improved.
Read skills/refactoring-process.md for the measure-refactor-measure workflow, extraction patterns, and bulk refactoring mode.
Read guidelines.md for measurement requirements, revert criteria, and scope limits.
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User asks for design, planning, or approach exploration before implementation. Covers new features, components, refactors, or architecture decisions. Creates design docs and proposes approaches with trade-offs.
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Use when starting work in a repository under repositories/ that may lack CI configuration. Detects missing CI workflows (GitHub Actions, GitLab CI, CircleCI) and alerts the user to add one. Skips repos marked as research-only.
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Use when the user asks to measure command execution time or optimize a feedback loop. Records explicit measurements and recommends faster alternatives such as unit tests versus integration tests. It is opt-in; it does not run on every command.
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Team conventions for Python development — credentials, API clients, LLM response parsing, testing patterns, and data pipeline structure. Covers dotenv loading, retry logic, secret validation, and pipeline anti-patterns.
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Scan projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Especially useful for data and API integrations, regardless of implementation language.
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Use when committing or pushing changes and the repository needs a version bump. Analyzes commits using conventional commit prefixes to determine whether the next release is a major, minor, or patch increment. Also use when the user asks about versioning, release planning, or changelog generation.
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