README
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.
- SkillOps loop: metadata-only adoption drift, telemetry hooks, adaptive proposals, daily and weekly curator reports, and portfolio-level drift signals.
Current posture: the repository is ready for beta and external testing, while stronger public "world-class" claims remain evidence-gated. Provider-backed production evidence, human blind-review evidence, native permission execution, and real-client telemetry are tracked as separate evidence tasks instead of being treated as completed work.
See the companion artifacts: