RRepoGEO

REPOGEO REPORT · LITE

NovaSky-AI/SkyRL

Default branch main · commit 72c7834c · scanned 5/12/2026, 1:32:23 AM

GitHub: 1,820 stars · 317 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 NovaSky-AI/SkyRL, 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
  • hightopics#1
    Add comprehensive topics to improve categorization

    Why:

    COPY-PASTE FIX
    reinforcement-learning, rl, large-language-models, llms, fine-tuning, multi-agent-rl, deep-learning, machine-learning, python, full-stack
  • mediumreadme#2
    Add a concise, benefit-oriented sentence after the H1 in the README

    Why:

    CURRENT
    The README currently goes from H1 directly to a navigation bar and then the "Overview" section which starts with an `IMPORTANT` note.
    COPY-PASTE FIX
    After the H1 and navigation links, add: "SkyRL empowers researchers and practitioners to build, train, and deploy advanced RL agents for large language models, offering a unified framework for both single and multi-agent scenarios."
  • lowcomparison#3
    Add a 'Why SkyRL?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why SkyRL?
Unlike frameworks that specialize in single-agent RL (e.g., Stable Baselines3) or focus solely on LLM fine-tuning (e.g., Hugging Face TRL), SkyRL provides a unified, full-stack solution for both single-agent and multi-agent reinforcement learning, specifically optimized for large language models. Our modular design integrates training, inference, and agent layers, streamlining the entire RL development lifecycle.

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 NovaSky-AI/SkyRL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 3×
  2. huggingface/trl · recommended 2×
  3. ray-project/ray · recommended 2×
  4. huggingface/peft · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How can I apply reinforcement learning to fine-tune large language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT (huggingface/peft)
    2. TRL (huggingface/trl)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. OpenAI
    5. Colossal-AI (hpcaitech/ColossalAI)
    6. Accelerate (huggingface/accelerate)
    7. Ray RLlib (ray-project/ray)
    8. PyTorch FSDP

    AI recommended 8 alternatives but never named NovaSky-AI/SkyRL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some modular full-stack reinforcement learning frameworks for large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL (huggingface/trl)
    3. Acme (deepmind/acme)
    4. JAX (google/jax)
    5. Flax (google/flax)
    6. Ray RLlib (ray-project/ray)
    7. Hugging Face Transformers (huggingface/transformers)
    8. OpenAI's Spinning Up (openai/spinningup)
    9. PyTorch (pytorch/pytorch)
    10. TensorFlow (tensorflow/tensorflow)
    11. Stable Baselines3 (DLR-RM/stable-baselines3)
    12. Hugging Face Transformers (huggingface/transformers)

    AI recommended 12 alternatives but never named NovaSky-AI/SkyRL. 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 NovaSky-AI/SkyRL?
    pass
    AI named NovaSky-AI/SkyRL explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite