RRepoGEO

REPOGEO REPORT · LITE

AMAP-ML/SkillClaw

Default branch main · commit 03f7bb43 · scanned 5/18/2026, 5:23:07 PM

GitHub: 1,343 stars · 124 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 AMAP-ML/SkillClaw, 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
  • highabout#1
    Clarify the "About" description to prevent miscategorization

    Why:

    CURRENT
    Let Skills Evolve Collectively with Agentic Evolver
    COPY-PASTE FIX
    A framework for AI agents to collectively learn and evolve skills from real-world interactions.
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2604.08377
  • mediumreadme#3
    Add an explicit "What is SkillClaw?" statement to the README's opening

    Why:

    COPY-PASTE FIX
    Add this paragraph immediately after the H3: SkillClaw is a novel framework designed for **AI agents** to autonomously and **collectively evolve their skills** through continuous interaction. Unlike systems for extracting human skills from text, SkillClaw focuses on enabling agents to learn, adapt, and share operational capabilities in dynamic environments.

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 AMAP-ML/SkillClaw
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ray RLlib
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray RLlib · recommended 1×
  2. OpenAI Gym/Gymnasium · recommended 1×
  3. Stable Baselines3 · recommended 1×
  4. TensorFlow Agents (TF-Agents) · recommended 1×
  5. PyTorch Lightning · recommended 1×
  • CATEGORY QUERY
    How can I enable my AI agents to continually learn and evolve skills from interactions?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib
    2. OpenAI Gym/Gymnasium
    3. Stable Baselines3
    4. TensorFlow Agents (TF-Agents)
    5. PyTorch Lightning
    6. Tianshou
    7. DeepMind's Acme
    8. TensorFlow Federated
    9. PySyft
    10. Avalanche
    11. LearnPy

    AI recommended 11 alternatives but never named AMAP-ML/SkillClaw. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks support collective skill evolution and knowledge sharing among multiple AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. RLlib (ray-project/ray)
    3. PettingZoo (Farama-Foundation/PettingZoo)
    4. MASON
    5. NetLogo (NetLogo/NetLogo)
    6. Unity ML-Agents (Unity-Technologies/ml-agents)

    AI recommended 6 alternatives but never named AMAP-ML/SkillClaw. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 AMAP-ML/SkillClaw?
    pass
    AI named AMAP-ML/SkillClaw explicitly

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

  • If a team adopts AMAP-ML/SkillClaw in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AMAP-ML/SkillClaw 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 AMAP-ML/SkillClaw solve, and who is the primary audience?
    pass
    AI named AMAP-ML/SkillClaw explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
AMAP-ML/SkillClaw — RepoGEO report