techwavedev 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
- Install the skill using the command above
- Ensure Codex CLI is installed
- Use
/codex-reviewor 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
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.