RRepoGEO

REPOGEO REPORT · LITE

opendilab/DI-sheep

Default branch master · commit 623e3bfa · scanned 6/10/2026, 12:58:00 PM

GitHub: 508 stars · 31 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 opendilab/DI-sheep, 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
    Reposition the README's opening to clearly state the project's purpose

    Why:

    CURRENT
    # DI-sheep：深度强化学习 + 羊了个羊
当 AI 技术的明珠——深度强化学习，遇到号称“通关率只有0.01%”的游戏“羊了个羊”，会碰撞出哪些奇思妙想呢？
    COPY-PASTE FIX
    # DI-sheep：深度强化学习 + 羊了个羊 (Deep Reinforcement Learning for 'Sheep-a-Sheep' Tile-Matching Game)
This project applies Deep Reinforcement Learning (DRL) to the challenging 'Sheep-a-Sheep' tile-matching puzzle game, offering a DRL environment and AI agents for research and play.
  • mediumtopics#2
    Add more specific topics related to game AI and puzzle games

    Why:

    CURRENT
    artificial-intelligence, deep-reinforcement-learning, di-engine, javascript, python, react, reinforcement-learning, typescript
    COPY-PASTE FIX
    artificial-intelligence, deep-reinforcement-learning, di-engine, javascript, python, react, reinforcement-learning, typescript, game-ai, puzzle-game, tile-matching, game-environment
  • lowabout#3
    Refine the 'About' description for clarity on project output

    Why:

    CURRENT
    羊了个羊 + 深度强化学习（Deep Reinforcement Learning + 3 Tiles Game)
    COPY-PASTE FIX
    A Deep Reinforcement Learning (DRL) project for the 'Sheep-a-Sheep' tile-matching puzzle game, providing a DRL environment and trained AI 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 opendilab/DI-sheep
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Baselines3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Baselines3 · recommended 1×
  2. Ray RLLib · recommended 1×
  3. CleanRL · recommended 1×
  4. OpenAI Gym/Gymnasium · recommended 1×
  5. TensorFlow Agents · recommended 1×
  • CATEGORY QUERY
    How can I build an AI agent to play complex tile-matching puzzle games using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. Ray RLLib
    3. CleanRL
    4. OpenAI Gym/Gymnasium
    5. TensorFlow Agents

    AI recommended 5 alternatives but never named opendilab/DI-sheep. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries help create deep reinforcement learning environments for custom game simulations?
    you: not recommended
    AI recommended (in order):
    1. Gymnasium
    2. PettingZoo
    3. Unity ML-Agents
    4. PyGame
    5. Arcade

    AI recommended 5 alternatives but never named opendilab/DI-sheep. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 opendilab/DI-sheep?
    pass
    AI named opendilab/DI-sheep explicitly

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

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

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

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opendilab/DI-sheep — 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