RRepoGEO

REPOGEO REPORT · LITE

NovaSky-AI/SkyThought

Default branch main · commit 0d190f11 · scanned 6/21/2026, 3:41:52 PM

GitHub: 3,390 stars · 344 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/SkyThought, 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
    llm, large-language-models, model-training, ai-agents, code-generation, reinforcement-learning, distillation, cost-effective-ai, self-reflective-ai, modular-ai, python
  • highreadme#2
    Clarify the project's core purpose and audience in the README's opening

    Why:

    CURRENT
    # SkyThought
    COPY-PASTE FIX
    # SkyThought: Cost-Effective LLM Training & AI Agent Development

SkyThought enables you to train your own O1 preview models within $450, leveraging advanced techniques like reinforcement learning and distillation to enhance large language model performance, especially for code generation. Its modular and extensible architecture is designed for building robust, self-reflective AI agents.
  • mediumabout#3
    Expand the repository description to highlight key capabilities

    Why:

    CURRENT
    Sky-T1: Train your own O1 preview model within $450
    COPY-PASTE FIX
    SkyThought: Train cost-effective O1 preview models (within $450) and enhance LLM performance for tasks like code generation using RL and distillation, built on a modular architecture for AI agents.

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/SkyThought
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LoRA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LoRA · recommended 2×
  2. QLoRA · recommended 2×
  3. huggingface/transformers · recommended 1×
  4. huggingface/peft · recommended 1×
  5. facebookresearch/llama · recommended 1×
  • CATEGORY QUERY
    What are cost-effective options for training a custom large language model?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. LoRA
    4. Llama 2 (facebookresearch/llama)
    5. Mistral 7B (mistralai/mistral-src)
    6. Falcon 7B (tiiuae/falcon)
    7. Phi-2 (microsoft/phi-2)
    8. Axolotl (OpenAccess-AI-Collective/axolotl)
    9. QLoRA
    10. FSDP
    11. GPT-2 (openai/gpt-2)
    12. EleutherAI's Pythia (EleutherAI/pythia)
    13. BLOOMZ (bigscience/bloomz)
    14. Google Cloud Vertex AI
    15. AWS SageMaker
    16. Azure Machine Learning
    17. Google's PaLM 2
    18. RunPod
    19. Vast.ai
    20. Paperspace Gradient

    AI recommended 20 alternatives but never named NovaSky-AI/SkyThought. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I enhance large language model performance for code generation using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. trl
    3. LoRA
    4. QLoRA
    5. DeepSpeed
    6. OpenAI API
    7. GPT-3.5
    8. GPT-4
    9. Ray RLlib
    10. Dopamine
    11. TF-Agents

    AI recommended 11 alternatives but never named NovaSky-AI/SkyThought. 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/SkyThought?
    pass
    AI named NovaSky-AI/SkyThought 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/SkyThought in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NovaSky-AI/SkyThought 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/SkyThought solve, and who is the primary audience?
    pass
    AI named NovaSky-AI/SkyThought 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/SkyThought — 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