Total 53,711 skills, Code Quality has 2422 skills
Showing 12 of 2422 skills
Dependency audit and cleanup workflow for maintaining healthy project dependencies. Use for regular maintenance, security updates, and removing unused packages.
Identifies and fixes component hierarchy issues by detecting orphaned classes in root namespaces and ensuring components exist only as leaf nodes. Use when analyzing component structure, finding orphaned classes, flattening component hierarchies, removing component nesting, or when the user asks about component flattening, orphaned classes, or component structure cleanup.
Clean and format SQL migrations for Supabase - idempotency, RLS policies, formatting, schema fixes. Use when: fix this SQL, clean migration, RLS policy, Supabase schema, format postgres, prepare for SQL Editor, idempotent migration.
Systematically analyze codebase structure, complexity, dependencies, and architectural patterns to understand project organization
Pre-commit code quality review workflow. Use before committing changes to verify code quality, security, testing coverage, and adherence to project conventions. Supports both automated checking and interactive review modes.
Automated tech debt cleanup worker (L3). Reads codebase audit findings, applies safe auto-fixes for low-risk issues (unused imports, dead code, commented-out code, deprecated aliases). Confidence >=90% only. Creates single commit with summary.
Remove AI-generated code slop from branches. Use after AI-assisted coding sessions to clean up defensive bloat, unnecessary comments, type casts, and style inconsistencies. Focuses on identifying and removing AI artifacts that degrade code quality.
Agent Review workflows and best practices for catching bugs before merging. Use for reviewing agent-generated code and local changes.
Guide for implementing parsers with error recovery for new languages in Biome. Use when creating parsers for JavaScript, CSS, JSON, HTML, GraphQL, or adding new language support. Examples:<example>User needs to add parsing support for a new language</example><example>User wants to implement error recovery in parser</example><example>User is writing grammar definitions in .ungram format</example>
Comprehensive multi-stage code review using specialized subagents. Use when reviewing PRs with complex architectural impact, security concerns, or when thorough multi-perspective analysis is needed.
Analyze gaps between implementation plans and actual codebase implementation for the Rust self-learning memory project
Analyses and optimises performance across frontend, backend and database interactions. Identifies bottlenecks and implements solutions to enhance speed and efficiency.