YAO (Yielding AI Outcomes) is a system designed to transform raw operational inputs like workflows, prompts, and transcripts into reusable, governed agent skills. It targets agent builders, internal tooling teams, and prompt engineers looking to move beyond ad-hoc prompts towards structured, maintainable AI assets. The primary benefit is converting scattered knowledge into discoverable, repeatable, and portable skill packages with built-in governance and rigorous evaluation.

How It Works

The system employs a "Hero view" architecture, processing messy operational inputs through a compact flow to produce a governed, reusable skill package. This involves routing inputs to a lean SKILL.md definition, designing the skill with archetypes and gates, running a unified CLI for creation, validation, evaluation, and promotion, and finally outputting a skill package with reports and client-specific adapters. Its approach is characterized by being method-first, trigger-aware, lightweight at the entrypoint, toolchain-backed, governed as an asset, portable, and evidence-rich, emphasizing strict evaluation loops and first-class governance.

Quick Start & Requirements

• Primary install/run command: Use the unified authoring CLI, e.g., python3 scripts/yao.py init my-skill --description "Describe what the skill does." or make test.
• Prerequisites: Python 3.x (implied by python3 commands).
• Links:
  • Examples: examples/README.md
  • Evals: evals/README.md
  • Documentation: Available in English (README.md), Chinese (docs/README.zh-CN.md), Japanese (docs/README.ja-JP.md), French (docs/README.fr-FR.md), and Russian (docs/README.ru-RU.md).

Highlighted Details

• Method Completeness (9.8/10): Features a formal doctrine for skill engineering, gate selection, lifecycle governance, and resource boundaries.
• Engineering Toolchain (9.8/10): Provides a unified CLI and CI path for authoring, validation, packaging, reporting, and promotion checks.
• Governance, Maintenance, and Safety (9.8/10): Skills can carry lifecycle state, review cadence, maturity scores, trust boundaries, and promotion evidence.
• Rigorous Evaluation Loop (9.7/10): Incorporates train/dev/holdout suites, blind holdout, adversarial holdout, judge-backed blind evaluation, drift history, and promotion gates.
• Portability and Packaging (9.6/10): Maintains a neutral source while using adapters and degradation rules for cross-environment compatibility.
• Context Efficiency (9.4/10): Focuses on a compact entrypoint, tiered context budgets, and tracking quality density.

Maintenance & Community

No specific details on contributors, sponsorships, or community channels (like Discord/Slack) are provided in the README. Continuous integration is configured via .github/workflows/test.yml.

Licensing & Compatibility

• License Type: MIT.
• Compatibility: The MIT license generally permits commercial use and linking with closed-source projects without significant restrictions.

Limitations & Caveats

While the system emphasizes rigorous evaluation and governance, some aspects are still under active refinement, such as tracking adversarial calibration and family drift separately. The README does not explicitly state an alpha status or list known bugs, but the detailed evaluation and refinement processes suggest an actively developed, mature system focused on identifying and mitigating weaknesses through its extensive testing and validation suites.