Review Viewer

# Yao Meta Skill

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.

maturity: governed archetype: governed format: agent-skills updated: 2026-03-31 intent confidence: 100 / 100

## Architecture at a glance

Inputs
workflow, prompt, transcript, docs, or notes
Boundary
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.
Logic
Decide whether the request should become a skill and choose the lightest fit.; Capture job, output, exclusions, constraints, and standards.; Run reference scan: external benchmarks first, user references second, local fit third; surface only uncertainty or conflict.
Usage
Use $yao-meta-skill to turn my workflow or notes into a reusable skill with lean structure, clear triggering, and the right evals.; Use this skill when the request matches: 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.
Next
Review the top iteration directions before growing the package.

## Core logic

- Decide whether the request should become a skill and choose the lightest fit.
- Capture job, output, exclusions, constraints, and standards.
- Run reference scan: external benchmarks first, user references second, local fit third; surface only uncertainty or conflict.
- Write the `description` early and test route quality before expanding the package.
- Add output-risk, artifact-design, prompt-quality, and system-model reports only when they matter.

## How to use it

- Use $yao-meta-skill to turn my workflow or notes into a reusable skill with lean structure, clear triggering, and the right evals.
- Use this skill when the request matches: 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.

## Intent questions

- If this skill worked beautifully, what recurring job would it reliably handle for the user every time?
  This reveals the real job-to-be-done and gives the package a humane center instead of a guessed prompt shape.
- When someone reaches for this skill in the real world, what materials will they actually hand to it?
  Input shape decides whether references, scripts, or templates are needed.
- What finished output should it hand back so the user can immediately keep moving?
  Outputs should drive the package structure before extra guidance is added.
- Which nearby requests should this skill politely refuse so the boundary stays clean?
  The exclusion list is the fastest route to better trigger quality.
- What matters most here: speed, consistency, auditability, portability, governance, or tone/style fit?
  Constraints decide how much structure, packaging, and review the skill actually needs.

## Why this package is strong

- 触发面保持精简，并锚定在 frontmatter description。
- 已生成 Skill IR，核心语义可先于平台打包被审查和迁移。
- 已生成目标编译报告，可审查 IR 到 OpenAI、Claude、generic 等目标契约的映射。
- 已生成 Output Eval Lab scorecard，可比较 with-skill 与 baseline 输出质量。
- 已生成 Output Execution Runs，可区分记录样本、命令执行和模型执行证据。
- 已生成 Output Review Adjudication，可记录盲评决策、一致率和待评审项。

## Borrow plan

- No external benchmark objects recorded yet. Add 2 to 5 references before deepening the package.

## Compare view

Winner: Current

{'priority': ['fewest false positives', 'fewest false negatives', 'highest near-neighbor pass rate', 'highest negative pass rate', 'highest precision', 'highest recall', 'shortest description']}

Variant | Tokens | Dev Errors | Holdout Errors | Strategy
--- | --- | --- | --- | ---
Baseline | 8 | 0 | 0 | existing
Current | 65 | 0 | 0 | current
Current | 65 | 0 | 0 | current

## Variant diff studio

### Baseline

existing

Create and improve agent skills.

tokens 8 (0)dev 0 (0)holdout 0 (0)

Adds relative to baseline

- Create and improve agent skills.

Drops from baseline

No dropped cues.

### Current

current

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.

tokens 65 (+57)dev 0 (0)holdout 0 (0)

Adds relative to baseline

- 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.

Drops from baseline

- Create and improve agent skills.

### Winner — Current

current

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.

tokens 65 (+57)dev 0 (0)holdout 0 (0)

Adds relative to baseline

- 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.

Drops from baseline

- Create and improve agent skills.

## Evidence readiness

Readiness score: 85/100

- Intent clarity · ready
  100/100 intent confidence.
- Benchmark coverage · ready
  3 GitHub benchmark repositories attached.
- Pattern gate · ready
  5 accepted, 1 deferred using threshold 4/4.
- Conflict handling · decision needed
  The stated preference leans lightweight or speed-first, while the benchmark mix leans toward governance, review, or heavier evaluation structure.
- Output risk profile · ready
  5 output risk families attached.
- Artifact design profile · ready
  Execution-focused technical artifact with environment assumptions, copyable commands, expected outputs, and side effects made explicit.
- Prompt quality profile · ready
  89.0/100 prompt-facing quality score.

## Honest boundary check

- Are the known limits visible before the package deepens?
- Does the evidence support the borrowed patterns?
- Should uncertainty become a clarification question instead of more structure?

## Output risk profile

- Markdown readability
  Tables can render as dense grids with weak hierarchy or poor mobile readability.; Long bullets can make the output look complete while hiding the actual decision logic.
- Citation and footnote clutter
  Footnote markers or dense citation notes can interrupt the reading flow.; Evidence can be over-attached to obvious statements and under-attached to risky claims.
- Screenshot and visual capture
  Screenshots can be captured from the wrong state, wrong viewport, or wrong crop.; Missing screenshots can cause the skill to invent visual references instead of declaring the gap.

## Self-repair checks

- Preview whether each table still reads well when columns are narrow.
- Convert any table with paragraph-length cells into bullets or cards.
- Remove decorative citations that do not support a material claim.
- Move repeated source explanations into one compact source note.
- Check that every screenshot reference points to a real provided or generated asset.

## Artifact design profile

Design system: metric editorial

- Code, CLI, or implementation guide
  Execution-focused technical artifact with environment assumptions, copyable commands, expected outputs, and side effects made explicit.
- Report or brief
  High-trust editorial report with a clear first-screen thesis, compact evidence blocks, and decisions separated from supporting detail.
- Review viewer
  Side-by-side reviewer studio with explicit tradeoffs, evidence readiness, and fast paths for approving, blocking, or requesting one focused fix.

## Visual quality gates

- Name the working directory and required inputs for commands.
- Mark destructive, networked, or external side-effect operations.
- Prefer the smallest runnable snippet over broad framework scaffolding.
- Keep the first screen useful without requiring the reader to parse every detail.
- Use tables only for comparisons; move explanations below the table.

## Prompt quality profile

Relevance: prompt-heavy · score 89.0 / 100 · complexity expert

- Completeness · 100 / 100
  Name missing inputs, outputs, constraints, or success standards before deepening the package.
- Clarity · 85 / 100
  Replace broad verbs with observable actions and define what done means.
- Consistency · 90 / 100
  Check that role, task, format, exclusions, and examples do not contradict each other.
- Practicality · 90 / 100
  Add runnable steps, examples, or verification cues instead of abstract advice.
- Specificity · 80 / 100
  Anchor wording in the user's audience, domain nouns, and target outcome.

## RTF to skill mapping

- Role
  Use an operator role with explicit boundaries, inputs, outputs, and failure handling.
- Task
  Convert the job into ordered steps with validation checks and stop conditions.
- Format
  Return a runbook-like handoff with commands, checks, owners, and next actions when relevant.

## Reference coach

### obra/superpowers

Borrow now

- Borrow the way it turns a messy workflow into a repeatable operating path.
- Borrow the clear execution entrypoints and command structure.

Avoid

- Do not import process overhead that only exists for that project's scale.

### affaan-m/ECC

Borrow now

- Borrow the way it turns a messy workflow into a repeatable operating path.
- Borrow the clear execution entrypoints and command structure.

Avoid

- Do not import process overhead that only exists for that project's scale.

### multica-ai/andrej-karpathy-skills

Borrow now

- Borrow explicit validation and quality gates that make iteration safer.
- Borrow the way it separates explanation, examples, and reusable structure.

Avoid

- Do not clone heavyweight evaluation scaffolding if a lighter gate is enough here.

## Decide before you deepen

- Choose one pattern to borrow on purpose, not three at once.
- State one thing this skill will not inherit from the benchmark objects.
- Only deepen the package after that choice is visible in the boundary or execution flow.

## Reference synthesis

### Official skill anatomy and context discipline

Borrow now

- Borrow progressive disclosure: keep the entrypoint lean and move depth into references or scripts.

Avoid

- Do not let packaging or platform concerns swallow the core job boundary.

### Human-in-the-loop verification

Borrow now

- Borrow a review checkpoint wherever trust matters more than raw speed.

Avoid

- Do not force every skill through heavyweight review when the risk is low.

### Boundary-first design

Borrow now

- Borrow the discipline of defining what the skill should not own before growing the package.

Avoid

- Do not expand execution assets until route boundaries stay clean.

## Borrow now

- Borrow progressive disclosure: keep the entrypoint lean and move depth into references or scripts.
- Borrow a review checkpoint wherever trust matters more than raw speed.
- Borrow the discipline of defining what the skill should not own before growing the package.
- Borrow the way it turns a messy workflow into a repeatable operating path.

Use the recommendation by default. Only surface the underlying benchmark tradeoffs when intent is uncertain or a real design conflict needs a deliberate call.

## Top three next moves

### Borrow one proven pattern on purpose

You already have public benchmark objects. The next gain is to choose one pattern intentionally instead of absorbing everything loosely.

- Read the strongest pattern from obra/superpowers.
- Decide whether to borrow method, structure, execution, or portability, but only one of them first.
- Record what you will not borrow so the package stays light.

Unlocks: A cleaner package shape with less accidental over-design.

### Harden portability semantics

The skill already signals reuse across environments, so contract clarity matters early.

- Confirm activation mode, execution context, and trust assumptions.
- Add or review degradation strategy for non-native targets.
- Package the skill once to verify adapter expectations.

Unlocks: Safer cross-environment reuse with less target drift.

### Create an iteration evidence loop

The package should show what changed and why after the first draft.

- Generate the HTML skill report and keep it aligned with the package.
- Record reference scan choices and non-goals.
- Capture the next iteration choice explicitly before adding more files.

Unlocks: A clearer path for the next author or reviewer.

## Recent feedback

- No lightweight feedback captured yet. Use `yao.py feedback` to record quick review notes.

## Promotion status

Promote: 0

Keep current: 3

Blocked: 0

## Package map

- SKILL.md — Skill entrypoint
- README.md — Human-readable usage guide
- agents/interface.yaml — Neutral interface metadata
- manifest.json — Lifecycle and portability metadata
- references — Extended guidance and reusable notes
- scripts — Deterministic helpers or local tooling
- evals — Trigger and quality checks
- reports — Generated evidence and overview artifacts

## First-pass review frame

- Does the trigger stay narrow enough for the intended job?
- Does the archetype match the real reuse level?
- Are we adding structure faster than we are adding reliability?
- Should the next step be trigger tightening, execution assets, or portability hardening?

## Authoring discipline

- Name unresolved assumptions before deepening the package.
- Keep the package no larger than the recurring job requires.
- Touch only files that directly support the requested change.
- Tie every meaningful new artifact to a check or reviewer note.

## Reviewer guardrails

- Block speculative features that are not backed by real workflow variation.
- Move unverifiable ideas into next-step candidates instead of baseline structure.
- Reject decorative folders, reports, or governance that do not reduce risk.
- Ask for one high-leverage clarification when job, output, or exclusion is still fuzzy.