Total 53,176 skills, Code Quality has 2413 skills
Showing 12 of 2413 skills
Review AI-generated code changes before committing using GitHuman. Use when reviewing code changes, creating code reviews, checking what the AI agent wrote, preparing to commit, or when user mentions "review", "GitHuman", or "before commit".
Conditional code-review persona, selected when the diff touches TypeScript code. Reviews changes with Kieran's strict bar for type safety, clarity, and maintainability.
Use when building features with **Codex** (OpenAI Codex CLI) in any codebase and the work should go through a disciplined build → review → test → fix loop. Triggers on "run the build loop", "build the next task", "continue the plan", "build this feature properly", or any request to implement work from a plan file or a direct feature prompt. Builds from the plan (or the prompt if no plan exists), runs Codex's `/review` on uncommitted changes and fixes every issue found, tests and verifies the feature end to end, fixes anything testing surfaces, and reports back once complete. Repeats until all plan tasks are checked off.
Automated AI-powered code review that runs on git hooks with progressive disclosure design. Use when setting up automated code review for a project, installing git hooks for code review, creating or modifying review rules, or configuring review behavior. Triggers on requests like "set up AI code review", "install review hooks", "create review rules", or "configure code reviewer".
Refactor code for readability using DRY, meaningful names, and modularization.
Use this skill when working with CodeRabbit, such as running CodeRabbit reviews, generating and processing automated CodeRabbit comments, or evaluating CodeRabbit suggestions.
MANDATORY for code review - must use Codex CLI for all code reviews, then apply fixes based on Codex feedback. Also use for cross-verification, debugging, and getting alternative implementations.
Review code for logging patterns and suggest evlog adoption. Detects console.log spam, unstructured errors, and missing context. Guides wide event design, structured error handling, request-scoped logging, and log draining with adapters (Axiom, OTLP).
Devil's Advocate stress-testing for code, architecture, PRs, and decisions. Surfaces hidden flaws through structured adversarial analysis with metacognitive depth. Use for high-stakes review, stress-testing choices, or when the user wants problems found deliberately. NOT for routine code review (use engineering:code-review). Triggers on "스트레스 테스트", "stress test", "devil's advocate", "반론", "이거 괜찮아", "문제 없을까", "깊은 리뷰", "critical review", "adversarial".
Review the C#/.NET code for design pattern implementation and suggest improvements.
Remove AI-generated code slop from the current branch. Use after writing code to clean up unnecessary comments, defensive checks, and inconsistent style.
Analyze candidate algorithms for time/space complexity, scalability limits, and resource-budget fit (CPU, memory, I/O, concurrency). Use when feasibility depends on input growth or latency/memory constraints and quantitative bounds are required before implementation; do not use for persistence schema or deployment topology decisions.