Total 55,321 skills, Code Quality has 2489 skills
Showing 12 of 2489 skills
Orchestrate parallel debugging agents with root-cause tracing for multi-failure scenarios
Game development expert including DragonRuby, Unity, and game mechanics
Design systems using Martin Fowler's principles of refactoring, continuous integration, and patterns of enterprise application architecture. Emphasizes clean code, evolution over revolution, and writing code for humans first. Use when designing enterprise systems, planning refactors, or establishing engineering culture.
Use when working with fundamental software development knowledge — patterns, algorithms, architecture, and craftsmanship principles drawn from canonical published works. USE FOR: development fundamentals, pattern selection, architecture decisions, algorithm choice, code quality principles, choosing between architectural styles DO NOT USE FOR: specific pattern implementations (use sub-skills: design-patterns, integration-patterns, algorithms, etc.), testing strategy (use testing), infrastructure (use iac)
Pre/post-operation validation to detect missing components and prevent future issues
Audit, compare, and document Opik integrations across Python SDK, TypeScript SDK, and OTEL/API backend. Use when adding a new integration, reviewing an existing one, or generating first-pass integration docs and gap analysis.
Guides technical evaluation of code review feedback before implementation. Use when receiving PR comments, review suggestions, GitHub feedback, or when asked to address reviewer feedback. Emphasizes verification and reasoned pushback over blind agreement.
Hypothesis-driven autonomous debugging with real command validation
Simplify and refine code for clarity, consistency, and maintainability. Use after writing or modifying code to clean it up while preserving all functionality.
Pre-PR review pipeline — runs security, API audit, and scope check agents in parallel. Read-only, no changes. Use before creating PRs or after completing a phase of work.
This skill should be used when analyzing technical debt in a codebase, documenting code quality issues, creating technical debt registers, or assessing code maintainability. Use this for identifying code smells, architectural issues, dependency problems, missing documentation, security vulnerabilities, and creating comprehensive technical debt documentation.
Remove LLM-generated code patterns that add noise without value. Use when reviewing diffs, PRs, or branches to clean up AI-generated code. Triggers include requests to "remove slop", "clean up AI code", "review for AI patterns", or checking diffs against main for unnecessary verbosity, redundant checks, or over-engineering introduced by LLMs. Language-agnostic.