RRepoGEO

REPOGEO REPORT · LITE

NovaSky-AI/SkyThought

Default branch main · commit 0d190f11 · scanned 5/11/2026, 11:07:07 AM

GitHub: 3,378 stars · 344 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/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
    ai, machine-learning, code-generation, model-training, large-language-models, llm, affordable-ai, distillation, reinforcement-learning
  • highreadme#2
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    # SkyThought

[](https://github.com/NovaSky-AI/SkyThought)
    COPY-PASTE FIX
    # SkyThought

SkyThought offers an affordable framework for training and fine-tuning advanced AI models, including specialized large language models for code generation, leveraging techniques like distillation and reinforcement learning.

[](https://github.com/NovaSky-AI/SkyThought)
  • mediumreadme#3
    Add an 'Overview' section to the README

    Why:

    COPY-PASTE FIX
    ## Overview

SkyThought is dedicated to making advanced AI model development accessible and cost-effective. Our project provides tools and pre-trained models, such as the Sky-T1 series, enabling researchers and developers to train high-performance models for tasks like code generation and beyond, often within a budget of $450. We actively explore innovative techniques like distillation and reinforcement learning to enhance model capabilities.

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
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Google Colab Pro/Pro+ · recommended 1×
  3. RunPod · recommended 1×
  4. Vast.ai · recommended 1×
  5. OpenAI API · recommended 1×
  • CATEGORY QUERY
    How can I train a custom code generation model affordably?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Google Colab Pro/Pro+
    3. RunPod
    4. Vast.ai
    5. OpenAI API
    6. Replicate
    7. Modal
    8. NVIDIA RTX 3090
    9. NVIDIA RTX 4090

    AI recommended 9 alternatives but never named NovaSky-AI/SkyThought. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are alternative approaches to improve model performance beyond distillation?
    you: not recommended
    AI recommended (in order):
    1. Optuna
    2. Ray Tune
    3. AutoKeras
    4. FLAML
    5. XGBoost
    6. LightGBM
    7. CatBoost
    8. Scikit-learn
    9. Albumentations
    10. imgaug
    11. Keras ImageDataGenerator
    12. Augly
    13. PyTorch
    14. Keras
    15. Hugging Face Transformers
    16. PyTorch Hub
    17. torchvision.models
    18. TensorFlow Hub
    19. Keras Applications
    20. AdamW
    21. pytorch_optimizer

    AI recommended 21 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?

Embed your GEO score

Drop this badge into the README of NovaSky-AI/SkyThought. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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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