RRepoGEO

REPOGEO REPORT · LITE

OpenMOSS/Awesome-WAM

Default branch main · commit 988a0321 · scanned 6/15/2026, 2:42:52 AM

GitHub: 782 stars · 19 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 OpenMOSS/Awesome-WAM, 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 explicit disambiguation for 'WAM' in the README's opening paragraph

    Why:

    COPY-PASTE FIX
    This repository is a curated, continuously updated reading list and resource hub for **World Action Models (WAMs)** in embodied AI. To be clear, 'WAM' in this context refers exclusively to World Action Models, distinct from WebAssembly Micro Runtime (WAMR).
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    embodied-ai, world-models, action-models, reading-list, research, ai, machine-learning, robotics
  • mediumhomepage#3
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://openmoss.github.io/Awesome-WAM/

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 OpenMOSS/Awesome-WAM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DreamerV3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DreamerV3 · recommended 2×
  2. MuZero · recommended 2×
  3. World Models · recommended 1×
  4. DreamerV1 · recommended 1×
  5. DreamerV2 · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive reading list for world action models in embodied AI?
    you: not recommended
    AI recommended (in order):
    1. World Models
    2. DreamerV3
    3. DreamerV1
    4. DreamerV2
    5. Model-Based Reinforcement Learning: A Survey
    6. Deep Reinforcement Learning: An Overview
    7. MuZero

    AI recommended 7 alternatives but never named OpenMOSS/Awesome-WAM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key research challenges and solutions for embodied AI world models?
    you: not recommended
    AI recommended (in order):
    1. Meta-World
    2. POET
    3. Go-Explore
    4. DreamerV3
    5. PlaNet
    6. MuZero
    7. Slot Attention
    8. SAVi
    9. Spacecraft
    10. GNNs
    11. DoWhy
    12. CausalML
    13. RLBench
    14. Isaac Sim
    15. Albumentations
    16. Kornia

    AI recommended 16 alternatives but never named OpenMOSS/Awesome-WAM. 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 OpenMOSS/Awesome-WAM?
    pass
    AI named OpenMOSS/Awesome-WAM explicitly

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

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

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

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OpenMOSS/Awesome-WAM — 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