Total 50,306 skills, Code Quality has 2284 skills
Showing 12 of 2284 skills
Assesses and responds to incoming code review feedback on PRs (reviewer comments, requested changes), especially when suggestions are unclear, technically questionable, or scope-expanding. Use before implementing review suggestions to align on intent and keep changes minimal.
Review PowerShell code for language and runtime conventions: advanced functions, parameter design, error handling, object pipeline behavior, compatibility, and testability. Language-only atomic skill; output is a findings list.
[Code Quality] Run quality gate checklist. Use for pre-release, pre-dev, or pre-QA quality verification.
Enterprise systematic code review orchestrator with TRUST 5 principles, multi-language support, Context7 integration, AI-powered quality checks, SOLID principle validation, security vulnerability detection, and maintainability analysis across 25+ programming languages; activates for code reviews, quality standard validation, TRUST 5 enforcement, architectural audits, and automated review automation
Clean Code practices for TypeScript/JavaScript. Identify code smells, apply refactoring patterns, and write maintainable code. Use when reviewing code quality, refactoring messy code, or ensuring code follows best practices. Triggers on requests like "clean up this code", "refactor for readability", "identify code smells", or "make this more maintainable".
Retrieve code review results from DeepSource — issues, vulnerabilities, report cards, and analysis runs. Use when asked about code quality, security findings, dependency CVEs, coverage metrics, or analysis status.
Skill for creating custom lint rules by leveraging the existing linter ecosystems of various programming languages. This is a linter designed for AI Agents rather than humans, and its error messages function as correction instruction prompts for AI. Create custom rules in the `lints/` directory using standard methods for each language, including Rust (dylint), TypeScript/JavaScript (ESLint), Python (pylint), Go (golangci-lint), etc. Use this skill in the following scenarios: (1) When you want AI to enforce project-specific coding rules; (2) When you want to create lint rules that output AI-readable correction instructions when violations occur; (3) When you want to enforce naming conventions, structural patterns, and consistency rules through AI-driven linting. Triggers: "Create a linter rule", "Add a lint rule", "Enforce this pattern", "AI linter", "Custom lint", "Code rules", "Naming rules", "Structural rules", "create a linter rule", "add a lint rule", "enforce this pattern", "AI linter".
Systematic debugging workflow — reproduce, investigate, hypothesize, fix, and prevent. Covers root cause analysis, bug category strategies, evidence-based diagnosis, and post-mortem documentation.
Automated brownfield codebase analysis. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile. Essential for Conductor integration and onboarding existing projects.
Review PRs, MRs, and Gerrit changes with focus on security, maintainability, and architectural fit. Leverages github, gitlab, or gerrit skills based on repository context. Use when asked to review my code, check this PR, review a pull request, look at a merge request, review a patchset, or provide code review feedback.
Review the current Pull Request that has been checked out locally with structured feedback on code quality, issues, testing, and suggestions. Use when you need a comprehensive code review of a PR branch.
Interactively fix any type checking issues in Python code