Skill.md
- SKILL.md
--- name: dsl-vm-reverse description: Reverse JavaScript-based custom DSL/VM interpreters and risk-control engines: identify IIFE/switch-based opcode dispatch, extract opcode tables, and capture runtime semantics. ---
Skill.md
Professional code review with auto CHANGELOG generation, integrated with Codex AI. Use when you want professional code review before commits, you need automatic CHANGELOG generation, or reviewing large-scale refactoring.
SKILL.md
--- name: codex-review description: "Professional code review with auto CHANGELOG generation, integrated with Codex AI. Use when you want professional code review before commits, you need automatic CHANGELOG generation, or reviewing large-scale refactoring." risk: critical source: community date_added: "2026-02-27" --- # codex-review ## Overview Professional code review with auto CHANGELOG generation, integrated with Codex AI ## When to Use - When you want professional code review before commits - When you need automatic CHANGELOG generation - When reviewing large-scale refactoring ## Installation ```bash npx skills add -g BenedictKing/codex-review ``` ## Step-by-Step Guide 1. Install the skill using the command above 2. Ensure Codex CLI is installed 3. Use `/codex-review` or natural language triggers ## Examples See [GitHub Repository](https://github.com/BenedictKing/codex-review) for examples. ## Best Practices - Keep CHANGELOG.md in your project root - Use conventional commit messages ## Troubleshooting See the GitHub repository for troubleshooting guides. ## Related Skills - context7-auto-research, tavily-web, exa-search, firecrawl-scraper ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Use when you want professional code review before commits, you need automatic CHANGELOG generation, or reviewing large-scale refactoring.