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Yao Meta Skill

Turn chaos into packaged skills, knowledge into leverage

Teams waste time recreating processes, lose institutional knowledge when people leave, and struggle to scale workflows across projects.

Convert scattered workflows into documented, evaluated, reusable skill packages that any team member can apply consistently.

  • Extract skills from workflows, prompts, transcripts, and docs
  • Evaluate skill quality with automated and manual evals
  • Refactor skills for clarity, determinism, and reusability
  • Package skills with routing logic and structured references
  • Version and distribute skills across teams operationally

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mfkvault install yaojingang-yao-meta-skill

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  • Full skill code ready to install
  • Works with 4 AI agents
  • Lifetime updates included
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Description

--- name: yao-meta-skill description: Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use when asked to create a skill, turn a repeated process into a reusable skill, improve an existing skill, add evals, or package a skill for team reuse. metadata: author: Yao Team philosophy: "structured design, evaluation loop, template ergonomics, operational packaging" --- # Yao Meta Skill Build reusable skill packages, not long prompts. ## Router Rules - Route by frontmatter `description` first. - Keep `SKILL.md` to routing plus a minimal execution skeleton. - Put long guidance in `references/`, deterministic logic in `scripts/`, and evidence in `reports/`. - Use the lightest process that still makes the skill reliable. ## Modes - `Scaffold`: exploratory or personal use. - `Production`: team reuse with focused gates. - `Library`: shared infrastructure or meta skill. Mode rules: [Operating Modes](references/operating-modes.md), [QA Ladder](references/qa-ladder.md), [Resource Boundary Spec](references/resource-boundaries.md), [Method](references/skill-engineering-method.md). ## Compact Workflow 1. Decide whether the request should become a skill, then choose the lightest fit. 2. Run a short intent dialogue to capture the real job, outputs, exclusions, constraints, and standards. 3. Run a reference scan: external benchmarks first, user references second, local fit checks third. 4. Write the `description` early and test route quality before expanding the package. 5. Add only the folders and gates that earn their keep. 6. After the first package exists, surface the top three next iteration directions. Core playbooks: [Method](references/skill-engineering-method.md), [Intent Dialogue](references/intent-dialogue.md), [Reference Scan](references/reference-scan.md), [Archetypes](references/skill-archetypes.md), [Gate Selection](references/gate-selection.md), [Iteration Philosophy](references/iteration-philosophy.md), [Non-Skill Decision Tree](references/non-skill-decision-tree.md). ## First-Turn Style When the skill first activates: - open warmly, like a thoughtful teacher or design partner - start from the user's work and desired outcome before asking for structure - ask only `2-3` high-leverage questions unless the user already gave enough detail - let the user answer naturally first; offer a tiny scaffold only as an optional shortcut - do not default to cold field lists such as `Name / Capability / Inputs / Outputs` Chinese conversations should sound soft and companion-like rather than procedural. For concrete opening patterns, see [Intent Dialogue](references/intent-dialogue.md). ## Output Contract Unless the user asks otherwise, produce: 1. a working skill directory 2. a `SKILL.md` 3. aligned `agents/interface.yaml` 4. optional `references/`, `scripts/`, `evals/`, `reports/`, and `manifest.json` only when justified 5. a short summary of boundary, exclusions, references, gates, and next steps ## Reference Map Primary references: [Method](references/skill-engineering-method.md), [Reference Scan](references/reference-scan.md), [Intent Dialogue](references/intent-dialogue.md), [Governance](references/governance.md), [Resource Boundaries](references/resource-boundaries.md).

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