RRepoGEO

REPOGEO REPORT · LITE

NovaSky-AI/SkyRL

Default branch main · commit 7f453704 · scanned 6/22/2026, 7:37:07 AM

GitHub: 2,015 stars · 358 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
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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ['reinforcement-learning', 'llm', 'large-language-models', 'rlhf', 'deep-learning', 'machine-learning', 'python', 'ai', 'framework', 'full-stack']
  • mediumreadme#2
    Strengthen the README's opening statement for competitive positioning

    Why:

    CURRENT
    SkyRL is a full-stack RL library that provides the following components:
    COPY-PASTE FIX
    SkyRL is a leading full-stack reinforcement learning library, offering a modular and performant framework for LLMs that competes with solutions like Hugging Face TRL and Ray RLlib.
  • lowreadme#3
    Add a 'Why SkyRL?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why SkyRL?

Unlike general-purpose RL frameworks, SkyRL is built from the ground up for LLM alignment, integrating advanced algorithms like PPO, DPO, RPO, KTO, and ORPO within a unified, full-stack environment.

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/trl
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/trl · recommended 1×
  2. pytorch/pytorch · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. AlphaCode · recommended 1×
  • CATEGORY QUERY
    How can I apply reinforcement learning techniques to improve large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (TRL library) (huggingface/trl)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. Hugging Face Transformers (huggingface/transformers)
    5. AlphaCode
    6. Anthropic's Constitutional AI
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)

    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 frameworks for training reinforcement learning models efficiently?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib
    2. Stable Baselines3 (SB3)
    3. Gymnasium
    4. PyTorch Lightning
    5. Acme
    6. Tianshou
    7. CleanRL

    AI recommended 7 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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NovaSky-AI/SkyRL — 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