Skills matching the name, description or GitHub author.
Perform comprehensive code reviews using OpenAI Codex CLI. This skill should be used when users request code reviews, want to analyze diffs/PRs, need security audits, performance analysis, or want automated cod...
Code review with Codex CLI. Use for PR review, diff review, code analysis, or when user says "codexでレビュー", "コードレビュー".
Request code review by dispatching reviewer subagent with proper context and git SHAs. WHEN: MUST use after completing major features or before merge. Invoke with "/requesting-code-review" or "request code revi...
Handle code review feedback with technical rigor, verification, and appropriate pushback. WHEN: MUST use when received code review feedback. Invoke with "/receiving-code-review" or "handle this review feedback"...
Professional code review for anti-patterns, extensibility, architectural soundness, and code quality. Use when users request code review, ask to check for anti-patterns, need architectural validation, want to e...
Reviews code changes for correctness, security, and maintainability with actionable feedback. Triggers on: review this code, code review, check this PR, review my changes.
Performs thorough code reviews with actionable feedback. Analyzes code for quality, bugs, security vulnerabilities, performance issues, and best practices violations. Use when the user wants a code review or as...
Fetch PR review comments from AI reviewers, verify accuracy, and apply valid fixes. Use when the user wants to process AI code review comments, apply AI reviewer suggestions, or verify and implement automated c...
Comprehensive code review tool for Clean Code principles and project architecture. Supports single-file review (/review-code) and project-wide analysis (/review-code ). Checks variable naming, function length,...
Scan entire Python project directories and review all .py files for Clean Code principles. Use when the user requests project-wide code review with /review-project command, wants to analyze multiple files, or n...
Review code for Clean Code principles focusing on meaningful variable names, function length, and redundant comments. Use when the user requests code review with /review-code command, asks to check code quality...
Code Reviewer Agent. Frontend/Backend 코드 리뷰를 담당합니다. 코드 품질, 테스트 커버리지, 보안, 성능을 검토합니다.
Comprehensive code review checking quality, security, and best practices. Triggers: CR, review, 審查, 檢查, check, 看一下, PR, code review, 品質, inspect, 檢視, 看看, 幫看, lint, quality check, 品質檢查, pull r...
Complete code review workflow for PR/MR with multiple reviewers and automated checks. Triggers: PRW, 審查流程, review workflow, PR review, MR review, pull request, merge request, 程式碼審查流程, full review, 完...
Expert code reviewer that enforces best practices, clean code principles, strong typing (TypeScript), architecture guidelines, and security standards. Reviews PRs and code snippets for bugs, code smells, anti-p...
Create AI-assisted code reviews on GitHub, GitLab, or Forgejo. Use when asked to review a PR/MR, analyze code changes, or provide review feedback.
Technical code review (architecture, type safety, patterns). Use for pre-merge review or general code quality audit.
Use when receiving code review feedback, processing PR comments, or needing to evaluate suggestions before implementing - requires technical verification not blind agreement
Code review workflow for evaluating changes against standards. Use when reviewing pull requests, commits, or diffs.
Performs strict code review with comprehensive validation
Code review and audit agent for pull requests and code quality
Reviews code for quality, security, and maintainability. Use after writing/modifying code, before commits, or when asked to review changes. Triggers on "review code", "check my changes", "code review".
This skill should be used when the user asks to "review code with codex", "run codex review", "check code quality", "codex レビュー", mentions "レビューゲート", or before git commit/PR/merge/release. Performs i...
Technical evaluation of code review feedback. Use when receiving review feedback - requires verification before implementing, no performative agreement, push back when technically wrong.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.