yaojingang

yaojingang / yao-meta-skill

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YAO = Yielding AI Outcomes. A lightweight but rigorous system for creating, evaluating, packaging, and governing reusable agent skills.

github.comyaojingangyao-meta-skill#readme agent-skills ai-agents meta-skill prompt-engineering skill-engineering
73
13
100% credibility
Found Apr 01, 2026 at 73 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A lightweight system to transform workflows, prompts, and notes into reusable, evaluable, and portable AI agent skills with governance and quality checks.

How It Works

1
🔍 Discover Yao Meta Skill

You hear about a simple way to turn your repeated tasks or notes into reusable AI helpers that teams can share safely.

2
💡 Describe Your Workflow

You jot down the everyday task or process you do often, like turning notes into a smart guide.

3
✨ Create Your Skill Package

With one easy command, it builds a neat package with clear instructions, tests, and safety checks just for your idea.

4
✅ Test and Fine-Tune

You run quick checks to make sure it triggers right and handles edge cases perfectly.

5
🔒 Review Quality and Safety

It scans for ownership, limits, and team readiness, giving you a confidence score.

6
📦 Package for Sharing

You export ready-to-use versions that work across different AI tools with one click.

🎉 Reusable Skill Ready!

Now your workflow lives as a governed, portable AI asset anyone can use reliably.

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AI-Generated Review

What is yao-meta-skill?

Yao-meta-skill is a Python toolkit for turning rough workflows, prompts, and notes into reusable agent skills, emphasizing Yielding AI Outcomes over raw text generation. It handles creating, evaluating, packaging, and governing these skills with clear triggers, lean descriptions, and portable exports for platforms like OpenAI and Claude. Developers get a lightweight system that packages skills as neutral bundles ready for team libraries, complete with evals and governance checks via a unified CLI.

Why is it gaining traction?

Its rigorous eval suites—covering train/dev/holdout, blind tests, adversarial cases, and route confusion—set it apart from casual prompt tools, ensuring skills trigger reliably without regressions. Built-in governance scores, context budgeting, and cross-platform packaging make skills maintainable assets, not one-offs. The hook is the quick workflow: init a skill, run tests with make test, and export zips, delivering 100% precision/recall on its own benchmarks.

Who should use this?

Agent builders crafting reusable capabilities for production agents. Prompt engineers transitioning to structured skills for teams. Internal tooling leads packaging workflows into governed libraries, especially those targeting multi-platform agents like OpenAI or Claude setups.

Verdict

Worth trying for serious agent work—its evals hit perfect scores, docs span multiple languages, and CI is solid—but at 73 stars and 1.0% credibility, it's early-stage with room for broader adoption. Pairs well with yao ecosystem searches like shunyu yao github or github yao pkg for lightweight, rigorous agent outcomes.

(198 words)

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