RRepoGEO

REPOGEO REPORT · LITE

aiming-lab/SkillRL

Default branch main · commit 8e66726e · scanned 6/16/2026, 1:28:12 PM

GitHub: 839 stars · 65 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 aiming-lab/SkillRL, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    reinforcement-learning, skill-discovery, llm-agents, hierarchical-rl, game-ai, deep-learning, machine-learning
  • mediumreadme#2
    Refine the README's opening statement to emphasize RL skill discovery

    Why:

    CURRENT
    SkillRL is a framework that enables LLM agents to learn high-level, reusable behavioral patterns from past experiences.
    COPY-PASTE FIX
    SkillRL is a novel reinforcement learning framework for automatic skill discovery and distillation, enabling agents (including LLM agents) to learn high-level, reusable behavioral patterns from past experiences.
  • lowreadme#3
    Add a dedicated 'Why SkillRL?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## 💡 Why SkillRL? What makes it different?

Unlike general reinforcement learning libraries, SkillRL focuses on hierarchical skill discovery and distillation from experience, particularly for complex tasks like those found in games. It provides a unique approach to model and enhance skill acquisition, including for human-like 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 aiming-lab/SkillRL
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. AutoGPT · recommended 1×
  3. BabyAGI · recommended 1×
  4. LlamaIndex · recommended 1×
  5. OpenAI Function Calling · recommended 1×
  • CATEGORY QUERY
    How can I enable LLM agents to learn reusable high-level behavioral patterns from experience?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGPT
    3. BabyAGI
    4. LlamaIndex
    5. OpenAI Function Calling
    6. RLHF
    7. DPO

    AI recommended 7 alternatives but never named aiming-lab/SkillRL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to automatically discover skills and distill strategic patterns for reinforcement learning.
    you: not recommended
    AI recommended (in order):
    1. Meta-World
    2. RLlib
    3. Stable Baselines3

    AI recommended 3 alternatives but never named aiming-lab/SkillRL. 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 aiming-lab/SkillRL?
    pass
    AI named aiming-lab/SkillRL explicitly

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

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

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

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aiming-lab/SkillRL — 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