RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/minihack

Default branch main · commit 09eaa7ee · scanned 6/2/2026, 11:42:06 AM

GitHub: 518 stars · 67 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 facebookresearch/minihack, 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, nethack, rl-environments, game-ai, procedural-generation, deep-learning, ai-research
  • highhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/samvelyan/minihack
  • mediumreadme#3
    Strengthen NetHack-specific positioning in README opening

    Why:

    CURRENT
    MiniHack is a sandbox framework for easily designing rich and diverse environments for Reinforcement Learning (RL). Based on the game of NetHack, MiniHack uses the NetHack Learning Environment (NLE) to communicate with the game and to provide a convenient interface for customly created RL training and test environments of varying complexity.
    COPY-PASTE FIX
    MiniHack is a unique sandbox framework for easily designing rich and diverse **NetHack-based** environments for Reinforcement Learning (RL). Leveraging the NetHack Learning Environment (NLE), MiniHack provides a convenient interface for custom RL training and test environments of varying complexity, specifically tailored for the NetHack game world.

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 facebookresearch/minihack
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Farama-Foundation/Gymnasium
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Farama-Foundation/Gymnasium · recommended 1×
  2. bulletphysics/bullet3 · recommended 1×
  3. deepmind/mujoco · recommended 1×
  4. Unity-Technologies/ml-agents · recommended 1×
  5. deepmind/lab · recommended 1×
  • CATEGORY QUERY
    How can I easily create custom, complex reinforcement learning environments for research?
    you: not recommended
    AI recommended (in order):
    1. Gymnasium (Farama-Foundation/Gymnasium)
    2. PyBullet (bulletphysics/bullet3)
    3. MuJoCo (deepmind/mujoco)
    4. Unity ML-Agents (Unity-Technologies/ml-agents)
    5. DeepMind Lab (deepmind/lab)
    6. Isaac Sim

    AI recommended 6 alternatives but never named facebookresearch/minihack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help build diverse, procedurally generated game environments for deep reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Unity ML-Agents Toolkit
    2. Unreal Engine
    3. UnrealCV
    4. Gymnasium
    5. OpenAI Gym
    6. Pygame
    7. Pyglet
    8. ProcGen Benchmark
    9. Minigrid
    10. DeepMind Lab
    11. Godot Engine

    AI recommended 11 alternatives but never named facebookresearch/minihack. 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 facebookresearch/minihack?
    pass
    AI named facebookresearch/minihack explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of facebookresearch/minihack. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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facebookresearch/minihack — 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