RRepoGEO

REPOGEO REPORT · LITE

minerllabs/minerl

Default branch dev · commit cdeae668 · scanned 6/7/2026, 1:41:52 AM

GitHub: 948 stars · 172 forks

AI VISIBILITY SCORE
69 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 minerllabs/minerl, 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

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

OVERALL DIRECTION
  • mediumreadme#1
    Emphasize 'sample-efficient reinforcement learning' in the README's opening

    Why:

    CURRENT
    Python package providing easy to use Gym environments and data access for training agents in Minecraft.
    COPY-PASTE FIX
    The MineRL Python Package is designed to accelerate research in **sample-efficient reinforcement learning** by providing easy-to-use OpenAI Gym environments and extensive human demonstration data for training AI agents in Minecraft.
  • mediumreadme#2
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [describe the license(s) here, e.g., a custom license, or a combination of licenses]. Please refer to the `LICENSE` file for full details.

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
1 / 2
50% of queries surface minerllabs/minerl
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
RLlib
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. RLlib · recommended 2×
  2. Stable Baselines3 · recommended 2×
  3. Malmo · recommended 1×
  4. Gym-Minecraft · recommended 1×
  5. DreamerV3 · recommended 1×
  • CATEGORY QUERY
    How to train AI agents in a Minecraft environment using Python?
    you: #1
    AI recommended (in order):
    1. MineRL ← you
    2. Malmo
    3. Gym-Minecraft
    4. DreamerV3
    5. RLlib
    6. Stable Baselines3
    Show full AI answer
  • CATEGORY QUERY
    What are good Python libraries for sample-efficient reinforcement learning in game environments?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. RLlib
    3. CleanRL
    4. Tianshou
    5. ACME
    6. Surreal

    AI recommended 6 alternatives but never named minerllabs/minerl. 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 minerllabs/minerl?
    pass
    AI named minerllabs/minerl explicitly

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

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