---
name: codex-review
description: "Professional code review with auto CHANGELOG generation, integrated with Codex AI"
risk: unknown
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

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

```bash
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Codex Review"
```

### Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags codex-review ai-agents
```

### Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>
```

### Control Tower Integration

Register agents and tasks with the Control Tower (`execution/control_tower.py`) for centralized orchestration across machines and LLM providers.

### Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

<!-- AGI-INTEGRATION-END -->
