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Found 58 Skills
Use when a repository needs cleanup of dead code, build artifacts, unused dependencies, outdated docs, or stale tests - provides safe cleanup workflows, validation steps, and reporting templates for code, deps, docs, tests, and sprint archives.
Detect code smells including long methods, large classes, duplicated code, and deep nesting. Use when identifying code quality issues or planning refactoring.
[Fix & Debug] ⚡⚡ Fix a GitHub issue with systematic debugging
This skill should be used when the user asks to "review code", "review PR", "code review", "audit code", "check for bugs", "security review", "review my changes", "find issues in this code", "review the diff", or asks for pull request review or code audit.
Plans and executes safe refactoring with tests as a safety net. Use when restructuring code, extracting functions, renaming across files, or simplifying complex logic without changing behavior.
The practice of restructuring and simplifying code continuously – reducing complexity, improving design, and keeping codebases clean.
Use when committing code - ensures atomic, descriptive commits that leave the codebase in a merge-ready state at every point
Brownfield Upgrade - Upgrade all dependencies and modernize the application while maintaining spec-driven control. Runs after Gear 6 for brownfield projects with modernize flag enabled. Updates deps, fixes breaking changes, improves test coverage, updates specs to match changes.
Systematic code refactoring skill that transforms complex, hard-to-understand code into clear, well-documented, maintainable code while preserving correctness. Use when users request "readable", "maintainable", or "clean" code, during code reviews flagging comprehension issues, for legacy code modernization, or in educational/onboarding contexts. Applies structured refactoring patterns with validation.
Finds unused dependencies, files, and exports in JS/TS projects. Use when cleaning up dead code, removing stale packages from package.json, or identifying unreferenced exports.
Corrective cleanup of AI-generated code — removes LLM-specific patterns while preserving behavior. Use when the user says "clean up", "deslop", "slop", "clean AI code", or when you spot LLM-generated code smells after any generation session.
Reliable end-to-end engineering workflow for debugging, root-cause analysis, minimal patching, and verification in production codebases. Use when Codex needs to investigate a failure systematically, trace execution, test hypotheses, implement a correct fix, validate the resolution, and check for regressions before declaring the task complete.