RRepoGEO

REPOGEO REPORT · LITE

cbfinn/maml_rl

Default branch master · commit 9c8e2ebd · scanned 5/31/2026, 9:42:28 PM

GitHub: 669 stars · 187 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 cbfinn/maml_rl, 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

1 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumlicense#1
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## License

The code in this repository is released under the terms specified in the [LICENSE](LICENSE) file. Please refer to that file for full details on usage and distribution.

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 cbfinn/maml_rl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Model-Agnostic Meta-Learning (MAML)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Model-Agnostic Meta-Learning (MAML) · recommended 1×
  2. PyTorch · recommended 1×
  3. TensorFlow · recommended 1×
  4. Proximal Policy Optimization (PPO) · recommended 1×
  5. Stable Baselines3 · recommended 1×
  • CATEGORY QUERY
    How can I quickly adapt a deep reinforcement learning agent to new, unseen tasks?
    you: not recommended
    AI recommended (in order):
    1. Model-Agnostic Meta-Learning (MAML)
    2. PyTorch
    3. TensorFlow
    4. Proximal Policy Optimization (PPO)
    5. Stable Baselines3
    6. RLlib
    7. Soft Actor-Critic (SAC)
    8. DreamerV3
    9. Multi-Task Learning (MTL)
    10. Decision Transformer

    AI recommended 10 alternatives but never named cbfinn/maml_rl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable few-shot learning for policy adaptation in reinforcement learning environments?
    you: not recommended
    AI recommended (in order):
    1. Meta-World (Farama-Foundation/Meta-World)
    2. RLlib (ray-project/ray)
    3. TorchRL (pytorch/rl)
    4. OpenSpiel (deepmind/open_spiel)
    5. Acme (deepmind/acme)
    6. Gymnasium (Farama-Foundation/Gymnasium)

    AI recommended 6 alternatives but never named cbfinn/maml_rl. 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 cbfinn/maml_rl?
    pass
    AI named cbfinn/maml_rl explicitly

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

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

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

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cbfinn/maml_rl — 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