RRepoGEO

REPOGEO REPORT · LITE

openai/mlsh

Default branch master · commit 2ae2393d · scanned 6/4/2026, 1:08:45 PM

GitHub: 619 stars · 162 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 openai/mlsh, 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 README to clarify its purpose in meta-learning RL

    Why:

    CURRENT
    Code for Meta-Learning Shared Hierarchies.
    COPY-PASTE FIX
    This repository provides the official code for the paper 'Meta-Learning Shared Hierarchies', focusing on applying meta-learning techniques to train hierarchical policies in reinforcement learning environments, particularly within OpenAI Gym.
  • hightopics#2
    Add specific topics for meta-learning and reinforcement learning

    Why:

    CURRENT
    ["paper"]
    COPY-PASTE FIX
    ["meta-learning", "reinforcement-learning", "hierarchical-policies", "openai-gym", "machine-learning", "research-code"]
  • mediumreadme#3
    Clarify the project's licensing status in the README

    Why:

    COPY-PASTE FIX
    ## License
This repository is provided as-is for research purposes, accompanying the paper 'Meta-Learning Shared Hierarchies'. No formal open-source license is provided, and no updates are expected. Users should consider this code for academic exploration only.

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 openai/mlsh
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 1×
  2. Farama-Foundation/Meta-World · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. openai/baselines · recommended 1×
  • CATEGORY QUERY
    How to implement meta-learning for hierarchical policies in reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Meta-World (Farama-Foundation/Meta-World)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. OpenAI Baselines (openai/baselines)
    6. Tianshou (thu-ml/tianshou)
    7. Acme (deepmind/acme)
    8. TF-Agents (tensorflow/agents)

    AI recommended 8 alternatives but never named openai/mlsh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for training agents across multiple related Gym environments efficiently.
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib
    2. Stable Baselines3 (SB3)
    3. CleanRL
    4. OpenAI Gym
    5. TensorFlow Agents (TF-Agents)

    AI recommended 5 alternatives but never named openai/mlsh. 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 openai/mlsh?
    pass
    AI named openai/mlsh explicitly

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

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

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

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openai/mlsh — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite