RRepoGEO

REPOGEO REPORT · LITE

yaojingang/yao-meta-skill

Default branch main · commit 5fbed1d2 · scanned 6/20/2026, 6:18:06 PM

GitHub: 1,395 stars · 134 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface yaojingang/yao-meta-skill, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to clarify unique focus on agent skill systems

    Why:

    CURRENT
    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.
    COPY-PASTE FIX
    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. Unlike general workflow orchestrators or MLOps platforms, `yao-meta-skill` is purpose-built as a rigorous engineering, evaluation, governance, and portability system specifically for reusable AI agent skills. It creates, evaluates, packages, and governs these reusable agent skills.
  • mediumreadme#2
    Add a 'Comparison' section to explicitly differentiate from common alternatives

    Why:

    COPY-PASTE FIX
    ## Comparison to Other Tools

While `yao-meta-skill` touches on aspects of AI development, it differs significantly from general LLM frameworks (like LangChain), MLOps platforms (like MLflow), or workflow orchestrators (like Apache Airflow or Prefect). `yao-meta-skill` focuses exclusively on the full lifecycle of *reusable AI agent skills*: from their engineering and evaluation to their governance, packaging, and portability across different AI environments. It provides a structured 'Skill OS' for managing these specific assets, rather than general-purpose model training, deployment, or task orchestration.
  • mediumtopics#3
    Remove the broad topic 'workflow-automation'

    Why:

    CURRENT
    agent-skills, ai-agents, evaluation, governance, meta-skill, portability, skill-engineering, workflow-automation
    COPY-PASTE FIX
    agent-skills, ai-agents, evaluation, governance, meta-skill, portability, skill-engineering

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface yaojingang/yao-meta-skill
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LangSmith · recommended 1×
  3. MLflow · recommended 1×
  4. Weights & Biases · recommended 1×
  5. OpenAI Evals · recommended 1×
  • CATEGORY QUERY
    How to rigorously engineer, evaluate, and govern reusable AI agent skills for consistent outcomes?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. MLflow
    4. Weights & Biases
    5. OpenAI Evals
    6. Pydantic
    7. Docker
    8. Kubernetes

    AI recommended 8 alternatives but never named yaojingang/yao-meta-skill. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help create portable, cross-platform agent skill packages from repetitive AI workflows?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines (kubeflow/pipelines)
    2. MLflow (mlflow/mlflow)
    3. Apache Airflow (apache/airflow)
    4. Metaflow (Netflix/metaflow)
    5. Prefect (PrefectHQ/prefect)
    6. Docker (moby/moby)
    7. Podman (containers/podman)
    8. Docker Compose (docker/compose)

    AI recommended 8 alternatives but never named yaojingang/yao-meta-skill. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of yaojingang/yao-meta-skill?
    pass
    AI named yaojingang/yao-meta-skill explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts yaojingang/yao-meta-skill in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yaojingang/yao-meta-skill explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo yaojingang/yao-meta-skill solve, and who is the primary audience?
    pass
    AI named yaojingang/yao-meta-skill explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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yaojingang/yao-meta-skill — RepoGEO report