RRepoGEO

REPOGEO REPORT · LITE

ECNU-ICALK/AutoSkill

Default branch main · commit 94c47ca4 · scanned 7/1/2026, 3:38:03 PM

GitHub: 500 stars · 49 forks

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 ECNU-ICALK/AutoSkill, 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
    Clarify AutoSkill's unique LLM-driven approach in the README's opening

    Why:

    CURRENT
    AutoSkill is a practical implementation of **Experience-driven Lifelong Learning (ELL)**. It learns from real interaction experience (dialogue + agents), automatically creates reusable Skills, and continuously evolves existing Skills through merge + version updates.
    COPY-PASTE FIX
    AutoSkill is a practical implementation of **Experience-driven Lifelong Learning (ELL)**, specifically designed for **LLM-powered agents**. It learns from real interaction experience (dialogue + agents), automatically creates reusable Skills through automated skill discovery and definition using Large Language Models, and continuously evolves existing Skills through merge + version updates.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the text of the MIT License, as hinted by the README's link to `https://opensource.org/licenses/MIT`.
  • mediumtopics#3
    Add 'large-language-models' and 'llm-agents' to repository topics

    Why:

    CURRENT
    agent-skills, continual-learning, experience-driven-lifelong-learning, self-evolving
    COPY-PASTE FIX
    agent-skills, continual-learning, experience-driven-lifelong-learning, self-evolving, large-language-models, llm-agents

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 ECNU-ICALK/AutoSkill
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Gym
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Gym · recommended 2×
  2. Stable Baselines3 · recommended 2×
  3. Avalanche · recommended 2×
  4. Learn2Learn · recommended 2×
  5. PyTorch · recommended 2×
  • CATEGORY QUERY
    How can AI agents continuously learn and improve skills from real-world interactions?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. Stable Baselines3
    3. Ray RLlib
    4. scikit-learn
    5. modAL
    6. Avalanche
    7. Learn2Learn
    8. TensorFlow Federated
    9. PySyft
    10. PyTorch
    11. TensorFlow
    12. Amazon Mechanical Turk
    13. Scale AI
    14. Labelbox

    AI recommended 14 alternatives but never named ECNU-ICALK/AutoSkill. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks support self-evolving agent skills for lifelong learning applications?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. Farama Gymnasium
    3. RLlib
    4. Stable Baselines3
    5. PyTorch
    6. TensorFlow
    7. Meta-World
    8. Avalanche
    9. Learn2Learn
    10. Acme
    11. OpenCog Hyperon
    12. Unity ML-Agents

    AI recommended 12 alternatives but never named ECNU-ICALK/AutoSkill. 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 ECNU-ICALK/AutoSkill?
    pass
    AI named ECNU-ICALK/AutoSkill explicitly

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

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

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

Embed your GEO score

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ECNU-ICALK/AutoSkill — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite