RRepoGEO

REPOGEO REPORT · LITE

Toni-SM/skrl

Default branch develop · commit 3cdc7f3b · scanned 5/19/2026, 1:52:09 AM

GitHub: 1,052 stars · 142 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 Toni-SM/skrl, 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 H1/H2 to highlight multi-framework and robotics simulation support

    Why:

    CURRENT
    <h2 align="center" style="border-bottom: 0 !important;">SKRL - Reinforcement Learning library</h2>
    COPY-PASTE FIX
    <h2 align="center" style="border-bottom: 0 !important;">SKRL - Modular Reinforcement Learning for PyTorch, JAX, Warp & Robotics Simulation</h2>
  • mediumabout#2
    Refine the repository's "About" description for conciseness and impact

    Why:

    CURRENT
    Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments
    COPY-PASTE FIX
    A unified, modular Reinforcement Learning (RL) library supporting PyTorch, JAX, and NVIDIA Warp, designed for diverse environments including Gymnasium/Gym, NVIDIA Isaac Lab, and MuJoCo Playground.
  • lowtopics#3
    Add "deep-reinforcement-learning" topic for specificity

    Why:

    CURRENT
    brax, deep-learning, flax, gym, gymnasium, isaaclab, isaacsim, jax, machine-learning, multi-agent, python, reinforcement-learning, robotics, torch, warp
    COPY-PASTE FIX
    brax, deep-learning, deep-reinforcement-learning, flax, gym, gymnasium, isaaclab, isaacsim, jax, machine-learning, multi-agent, python, reinforcement-learning, robotics, torch, warp

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 Toni-SM/skrl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CleanRL
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CleanRL · recommended 2×
  2. Tianshou · recommended 2×
  3. RLlib · recommended 1×
  4. Acme · recommended 1×
  5. Catalyst.RL · recommended 1×
  • CATEGORY QUERY
    Looking for a flexible reinforcement learning framework supporting PyTorch, JAX, and NVIDIA Warp.
    you: not recommended
    AI recommended (in order):
    1. RLlib
    2. CleanRL
    3. Tianshou
    4. Acme
    5. Catalyst.RL

    AI recommended 5 alternatives but never named Toni-SM/skrl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement deep reinforcement learning for robotics simulation environments like Isaac Lab?
    you: not recommended
    AI recommended (in order):
    1. RLlib (Ray)
    2. Stable Baselines3 (SB3)
    3. Tianshou
    4. Acme (DeepMind)
    5. CleanRL
    6. PyTorch
    7. TensorFlow (with Keras)

    AI recommended 7 alternatives but never named Toni-SM/skrl. 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 Toni-SM/skrl?
    pass
    AI named Toni-SM/skrl explicitly

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

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

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

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Toni-SM/skrl — 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