Codex Review

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

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 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

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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

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

# 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:

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.

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.

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