RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/every-embodied

Default branch main · commit d7753b9a · scanned 5/24/2026, 6:13:05 PM

GitHub: 1,974 stars · 213 forks

AI VISIBILITY SCORE
28 /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
2 / 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 datawhalechina/every-embodied, 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
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    The README starts with an ASCII art logo, title, and navigation links, without an immediate prose introduction.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title/links section: "This repository provides a comprehensive, hands-on learning roadmap to build embodied AI robots from scratch using Python, guiding you through VLA/OpenVLA/SmolVLA/Pi0."
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://datawhalechina.github.io/every-embodied/zh-cn/
  • lowreadme#3
    Clarify the project's license in the README

    Why:

    CURRENT
    The README does not explicitly state the project's license in its prose.
    COPY-PASTE FIX
    Add a sentence to the README, for example: "This project is licensed under the terms specified in the [LICENSE file](LICENSE)."

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 datawhalechina/every-embodied
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ROS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ROS · recommended 2×
  2. OpenCV · recommended 2×
  3. PyTorch · recommended 2×
  4. TensorFlow · recommended 2×
  5. ros_py · recommended 1×
  • CATEGORY QUERY
    How can I learn to build embodied AI robots from scratch using Python?
    you: not recommended
    AI recommended (in order):
    1. ROS
    2. ros_py
    3. OpenCV
    4. opencv-python
    5. PyTorch
    6. TensorFlow
    7. Keras
    8. Gymnasium
    9. OpenAI Gym
    10. Gazebo
    11. ros_gz
    12. NumPy
    13. SciPy
    14. Matplotlib
    15. Seaborn

    AI recommended 15 alternatives but never named datawhalechina/every-embodied. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for developing vision-language-action models for robotics?
    you: not recommended
    AI recommended (in order):
    1. OpenVLA
    2. RoboCat
    3. ROS
    4. MoveIt
    5. PCL - Point Cloud Library
    6. OpenCV
    7. OpenAI GPT-4V
    8. Google Gemini
    9. Meta Llama 3
    10. Hugging Face Transformers
    11. Diffusers
    12. CLIP
    13. BLIP-2
    14. LLaVA
    15. InstructBLIP
    16. RLlib
    17. PyTorch
    18. TensorFlow
    19. Franka Emika Panda

    AI recommended 19 alternatives but never named datawhalechina/every-embodied. 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 datawhalechina/every-embodied?
    pass
    AI named datawhalechina/every-embodied explicitly

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

  • If a team adopts datawhalechina/every-embodied in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/every-embodied 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 datawhalechina/every-embodied solve, and who is the primary audience?
    pass
    AI did not name datawhalechina/every-embodied — likely talking about a different project

    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
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