Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Mechanical enforcement layer for AI agent project standards using git hooks, secret detection, deployment verification, and import registries. Born from real production incidents including server crashes, token leaks, and code rewrites. Enforces rules that markdown and prompt instructions cannot reliably maintain.
Git hooks and code-level checks enforce standards at 95-100% reliability compared to 40-70% for prompt rules and markdown instructions.
When to use it
- Blocking AI agents from reimplementing existing functions instead of importing them
- Catching hardcoded API keys before they reach version control
- Verifying deployed code matches written code after agent tasks
- Preventing deployment gaps where features are coded but never wired into production
- Propagating enforcement improvements across projects via skill update feedback loops
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: set up guardrails on my Python project
- 1Runs install.sh to install git pre-commit hook and scaffolding into the project
- 2Copies registry-template.py to create a module import registry
- 3Adapts agents-md-template.md into project AGENTS.md with mechanical enforcement rules
- 4Runs create-deployment-check.sh to generate .deployment-check.sh and DEPLOYMENT-CHECKLIST.md
- 5Installs the pre-commit-deployment git hook template
Project now blocks secret commits, detects duplicate implementations, and verifies deployment wiring before any commit is accepted