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.
Installs into .claude/skills of the current project.
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---
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. This entrypoint does not bundle the upstream integration. Inspect a selected upstream revision and authorize installation scope before using the installation option above.
2. Ensure Codex CLI is installed
3. Use `/codex-review` only when the installed host exposes it. Pin repository/base/head and inspect the net diff directly; return findings with exact trigger, location and evidence. A generated CHANGELOG must match the actual diff and must not rewrite unrelated release history.
## 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
- An automated reviewer is advisory, not merge approval. Confirm provider/data/cost scope before sending private source externally; no implicit commit, push or publication.
- 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.