Total 50,401 skills, Code Quality has 2287 skills
Showing 12 of 2287 skills
Use when working with performance testing review multi agent review
Technical solution evaluation and code review in the style of Linus Torvalds. Only use this when the user explicitly requests a Linus-style review or explicitly asks for a rigorous evaluation of code changes/technical solutions (e.g., "review changes/code", "evaluate if the solution is appropriate", "check submission standards", "linus-tech-review").
Code review practices with technical rigor and verification gates. Use for receiving feedback, requesting code-reviewer subagent reviews, or preventing false completion claims in pull requests.
Systematically trace bugs backward through call stack to find original trigger. Use when errors occur deep in execution and you need to trace back to find the original trigger.
Review code architecture for maintainability, catch structural issues before they become debtUse when "Reviewing pull requests with structural changes, Planning refactoring work, Evaluating new feature architecture, Assessing technical debt, Before major releases, When code feels "hard to change", architecture, code-review, refactoring, design-patterns, technical-debt, dependencies, maintainability" mentioned.
This skill should be used when performing a code review on local changes on the current branch compared to the main branch. It uses multiple parallel agents to check for bugs, CLAUDE.md compliance, git history context, previous PR comments, and code comment adherence, then scores and filters findings by confidence level.
Cross-Layer Check
General Bugfix Workflow Knowledge Base, including TDD process, output format specifications, confidence scoring standards, and general best practices. Applicable to all tech stacks (backend/frontend/e2e).
Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.
Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.
Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.
Before adding abstraction, asks "do we need this now?" Activates when proposing factories, abstract classes, config-driven behavior, or "for future extensibility." Resists over-engineering. Three similar lines are better than a premature abstraction.