YAO 代表 Yielding AI Outcomes：目标不是生成更多提示文本，而是生成可重复使用的 AI 资产和真实的运营成果。

yao-meta-skill 创建、评估、打包和管理可重用的代理技能。1.0 系列专注于将重复的工作流转化为可安装、可读、跨平台的技能包。2.0 系列将这一工厂扩展为技能操作系统：一个治理系统，用于一次性建模技能，为多个目标编译，测试其行为，审查发布证据，并跟踪下一次迭代。

快速入门 ·Skill OS 2.0 ·1.0 vs 2.0 ·操作员用户体验 ·基准测试 ·示例 ·评估 ·故障库 ·方法原则

Skill OS 2.0升级

Skill OS 2.0 保留了 yao-meta-skill 的原始承诺，但使软件包生命周期更加明确。它不再止步于 SKILL.md，而是增加了语义契约、目标编译器、评估证据、发布门以及围绕技能的操作报告。

• 技能IR：一种平台中立的中间表示，用于表示意图、触发、输入、输出、边界、引用和预期产物。

Yao Meta Skill

YAO stands for Yielding AI Outcomes: the goal is not to generate more prompt text, but to produce reusable AI assets and real operational outcomes.

yao-meta-skill creates, evaluates, packages, and governs reusable agent skills. The 1.0 line focused on turning repeated workflows into installable, readable, cross-platform skill packages. The 2.0 line expands that factory into a Skill OS: a governed system for modeling a skill once, compiling it for multiple targets, testing its behavior, reviewing its release evidence, and tracking the next iteration.

Quick Start · Skill OS 2.0 · 1.0 vs 2.0 · Operator UX · Benchmark · Examples · Evals · Failure Library · Method Doctrine

Skill OS 2.0 Upgrade

Skill OS 2.0 keeps the original promise of yao-meta-skill, but makes the package lifecycle more explicit. Instead of stopping at SKILL.md, it adds a semantic contract, target compilers, evaluation evidence, release gates, and operation reports around the skill.

• Skill IR: a platform-neutral intermediate representation for intent, triggers, inputs, outputs, boundaries, references, and expected artifacts.
• Target compilers and adapters: generated surfaces for OpenAI, Claude, generic agent skills, Agent Skills compatible packages, and VS Code-oriented workflows.
• Output Eval Lab: trigger checks, output assertions, execution evidence, timing and token evidence, benchmark reproducibility, blind-review packs, answer keys, and adjudication reports.
• Review Studio 2.0: a single HTML gate page for intent, triggers, output eval, context cost, runtime checks, trust, Skill Atlas signals, adoption drift, waivers, annotations, release evidence, warnings, blockers, and fix actions.
• Evidence and release governance: evidence consistency checks, package verification, install simulation, runtime permission probes, world-class evidence intake, world-class ledger, operator runbook, and public claim guard.