swaylq / master-skill

What is master-skill?

Master-skill is a framework that takes an industry name and automatically generates a "cognitive operating system" for that field, ready to plug into AI coding agents like Claude Code, OpenClaw, or Codex. You tell it what you do -- say, "cross-border e-commerce" or "iOS app publishing" -- and 30-60 minutes later it produces a skill containing mental models, decision trees, workflows, and executable bash commands that let the agent think and act like a senior practitioner in that space. It builds on two related projects (colleague-skill and nuwa-skill) and generates skills with embedded CLI tools for decision-making and workflow execution.

Why is it gaining traction?

The hook is that AI agents sound generic on specialized topics, and this makes them domain-experts on demand. Instead of installing a dozen fragmented skills, you install one that auto-distills the right tools and knowledge for your specific industry. The generated skills come with bash tooling out of the box, not just conversation prompts. A 16-item quality gate validates every generated skill against source credibility, and the system handles disagreement across schools of thought (like five camps giving different answers) by preserving the divergence rather than averaging it away.

Who should use this?

This targets developers and technical leads working with AI coding agents who need reliable domain expertise in fields like legal practice, architecture, insurance brokering, or cross-border ecommerce. If you've been frustrated that your AI assistant gives generic answers on specialized topics, this is the missing layer. However, it's currently a research prototype with only 10 stars -- the tooling is raw bash scripts, the documentation is primarily in Chinese, and it requires manual oversight at quality gates before deployment.

Verdict

The concept is solid and the 12 working prototypes across very different industries show the framework is real. With a credibility score of 0.699999988079071% and only 10 stars, this is early-stage and best evaluated by running the included prototypes rather than installing it fresh. Treat it as a methodology demonstration and testing bed, not production-ready tooling. The bash CLI generation and quality validation pipeline are worth watching.