Total 52,846 skills, Code Quality has 2393 skills
Showing 12 of 2393 skills
Executable documentation governance with compound engineering and abductive learning. Enforces the Seven Laws through type compilation, schema validation, and hookify-based enforcement. Implements programmatic compound engineering where K' = K ∪ crystallize(assess(τ)) for monotonic knowledge growth. Integrates abstracted abductive learning (OHPT protocol) for systematic debugging and pattern extraction. Trigger when writing code, debugging, establishing governance, or when mentioned vibecode, compound, abductive, or executable documentation. Self-validating and homoiconic.
Code review of current git changes, compare to related plan if exists, identify bad engineering, over-engineering, or suboptimal solutions. Use when user asks to review changes, check git diff, validate implementation quality, or assess code changes.
Use when writing code, documentation, or comments - always use accessible and respectful terminology
Perform automated code reviews with best practices, security checks, and refactoring suggestions. Use when reviewing code, checking for vulnerabilities, or analyzing code quality.
Comprehensive code review assistant that analyzes code for security vulnerabilities, performance issues, and code quality. Use when reviewing pull requests, conducting code audits, or analyzing code changes. Supports Python, JavaScript/TypeScript, and general code patterns. Includes automated analysis scripts and structured checklists.
Review code changes from multiple specialist perspectives in parallel. Use when you want a thorough review of a PR, branch, or set of changes covering security, performance, correctness, edge cases, and ripple effects. Spawns parallel reviewer agents that each focus on a different lens, then synthesizes into a unified review.
Automatically scan and execute all skills ending with best-practice to check if the project complies with best practices. Ensure all relevant best practices are checked through the auto-discovery mechanism, and enforce standardized validation instructions to improve output stability.
Use when designing solutions, adding features, or refactoring by applying KISS, YAGNI, and Principle of Least Astonishment to write simple, predictable code.
Structured debug mode focused on root-cause confirmation and controlled fixes.
TDD enforcement during implementation. Reads `tdd:` setting from CLAUDE.md. Modes - strict (human approval for escape), soft (warnings), off (disabled). Auto-invoked by /implement.
Use when developing a new feature, fixing a bug, or making significant code changes - guides the full cycle from planning through verified commit with expert review
Guide for using ruff, the extremely fast Python linter and formatter. Use this when linting, formatting, or fixing Python code to maintain code quality and consistency.