RRepoGEO

REPOGEO REPORT · LITE

beyretb/AnimalAI-Olympics

Default branch master · commit 871a2f1a · scanned 6/1/2026, 3:07:15 AM

GitHub: 578 stars · 83 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 beyretb/AnimalAI-Olympics, 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 README's opening statement about repo status and purpose

    Why:

    CURRENT
    **UPDATE: Animal-AI v3 is now up on a separate repo.** The below repository contains the codebase for the competition that was ran in 2019. It is not under active maintenance but issues are still monitored to some extent.
    COPY-PASTE FIX
    This repository hosts the original codebase for the Animal AI Olympics competition (2019), providing a historical snapshot of the environment and tasks used to evaluate AI agents on animal-inspired cognitive challenges. While Animal-AI v3 is now available in a separate, actively maintained repository, this archive remains valuable for researchers interested in the original competition's setup and results. Issues are still monitored to some extent.
  • mediumabout#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the official website URL for the Animal AI Olympics competition (e.g., `https://animalaiolympics.com` or the relevant archive link) to the repository's 'About' section.
  • lowreadme#3
    Enhance README's 'Overview' section with the core differentiator

    Why:

    CURRENT
    The Animal-AI Testbed introduces the study of animal cognition to the world of AI. It provides an environment for testing agents on tasks taken from, or inspired by, the animal cognition literature. Decades of research in this field allow us to train and test for cognitive skills in Artificial Intelligence agents.
    COPY-PASTE FIX
    The Animal-AI Testbed introduces the study of animal cognition to the world of AI, providing a unique environment for testing agents on tasks taken from, or inspired by, the animal cognition literature and comparative psychology. This explicit focus allows us to train and test for cognitive skills in Artificial Intelligence agents, differentiating it from more general AI evaluation platforms.

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 beyretb/AnimalAI-Olympics
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepMind Lab
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepMind Lab · recommended 1×
  2. Unity ML-Agents Toolkit · recommended 1×
  3. OpenAI Gym · recommended 1×
  4. Minigrid · recommended 1×
  5. Animal-AI Olympics (AAI) · recommended 1×
  • CATEGORY QUERY
    How can I evaluate AI agents on tasks inspired by animal cognition and intelligence?
    you: not recommended
    AI recommended (in order):
    1. DeepMind Lab
    2. Unity ML-Agents Toolkit
    3. OpenAI Gym
    4. Minigrid
    5. Animal-AI Olympics (AAI)
    6. PlaNet (Planning Network)
    7. DreamerV3 (DeepMind)
    8. Neuro-Symbolic Concept Learner - NSCL

    AI recommended 8 alternatives but never named beyretb/AnimalAI-Olympics. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What platforms are available for deep reinforcement learning agent training and competition environments?
    you: not recommended
    AI recommended (in order):
    1. Gymnasium (Farama-Foundation/Gymnasium)
    2. PettingZoo (Farama-Foundation/PettingZoo)
    3. Unity ML-Agents Toolkit (Unity-Technologies/ml-agents)
    4. DeepMind Lab (deepmind/lab)
    5. MetaWorld (rlworkgroup/metaworld)
    6. RoboSumo (robosumo/robosumo)
    7. OpenSpiel (deepmind/open_spiel)

    AI recommended 7 alternatives but never named beyretb/AnimalAI-Olympics. 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 beyretb/AnimalAI-Olympics?
    pass
    AI named beyretb/AnimalAI-Olympics explicitly

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

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

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

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MARKDOWN (README)
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beyretb/AnimalAI-Olympics — RepoGEO report