RRepoGEO

REPOGEO REPORT · LITE

skydiscover-ai/skydiscover

Default branch main · commit c4c9e275 · scanned 6/4/2026, 4:57:52 PM

GitHub: 533 stars · 73 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 skydiscover-ai/skydiscover, 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 specific GitHub topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-driven-discovery, optimization-algorithms, evolutionary-computation, llm-agents, scientific-discovery, benchmarking, meta-learning, hyperparameter-optimization, machine-learning-framework
  • highreadme#2
    Integrate LLM-driven optimization into the README's opening paragraph

    Why:

    CURRENT
    SkyDiscover is a modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks.
    COPY-PASTE FIX
    SkyDiscover is a modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks. It uniquely features **EvoX**, an adaptive optimization algorithm that dynamically evolves optimization strategies using Large Language Models (LLMs) on the fly.
  • mediumabout#3
    Refine the About description for greater specificity

    Why:

    CURRENT
    AI-Driven Scientific and Algorithmic Discovery
    COPY-PASTE FIX
    A flexible framework for AI-driven scientific and algorithmic discovery, focused on implementing, comparing, and evolving optimization algorithms, including LLM-powered strategies.

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 skydiscover-ai/skydiscover
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mlflow/mlflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. mlflow/mlflow · recommended 1×
  2. Weights & Biases (W&B) · recommended 1×
  3. ray-project/ray · recommended 1×
  4. Netflix/metaflow · recommended 1×
  5. IDSIA/sacred · recommended 1×
  • CATEGORY QUERY
    What are good frameworks for comparing different AI-driven discovery algorithms?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Weights & Biases (W&B)
    3. Ray Tune (ray-project/ray)
    4. Metaflow (Netflix/metaflow)
    5. Sacred (IDSIA/sacred)
    6. Neptune.ai
    7. TensorBoard (tensorflow/tensorboard)

    AI recommended 7 alternatives but never named skydiscover-ai/skydiscover. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an AI framework to dynamically evolve optimization strategies using large language models.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGPT
    5. BabyAGI
    6. AgentGPT
    7. OpenAI API
    8. Anthropic's Claude
    9. Google's Gemini
    10. RLlib

    AI recommended 10 alternatives but never named skydiscover-ai/skydiscover. 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 skydiscover-ai/skydiscover?
    pass
    AI named skydiscover-ai/skydiscover explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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