RRepoGEO

REPOGEO REPORT · LITE

ai-boost/awesome-ai-for-science

Default branch master · commit e6f5742b · scanned 5/22/2026, 7:13:14 AM

GitHub: 1,572 stars · 169 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
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 ai-boost/awesome-ai-for-science, 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
  • highreadme#1
    Reposition README opening to emphasize its role as a discovery hub

    Why:

    CURRENT
    A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery across all disciplines.
    COPY-PASTE FIX
    This repository serves as your essential guide to discovering and leveraging the best AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery across all disciplines.
  • mediumreadme#2
    Add a 'How to Use' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled '🚀 How to Use This List' or '💡 Getting Started' that provides clear instructions on navigating the curated resources to find relevant tools, papers, and projects for scientific research.
  • lowabout#3
    Strengthen the repository description with a unique identifier

    Why:

    CURRENT
    A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery — from physics and chemistry to biology, materials, and beyond.
    COPY-PASTE FIX
    The definitive curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery — from physics and chemistry to biology, materials, and beyond.

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 ai-boost/awesome-ai-for-science
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AlphaFold2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AlphaFold2 · recommended 1×
  2. OpenFold · recommended 1×
  3. ChemAxon MarvinSketch/MarvinView · recommended 1×
  4. GraphCast · recommended 1×
  5. IBM Watson Discovery · recommended 1×
  • CATEGORY QUERY
    What AI tools can help accelerate scientific discovery across various research disciplines?
    you: not recommended
    AI recommended (in order):
    1. AlphaFold2
    2. OpenFold
    3. ChemAxon MarvinSketch/MarvinView
    4. GraphCast
    5. IBM Watson Discovery
    6. Insilico Medicine's Pharma.AI platform
    7. ESMFold

    AI recommended 7 alternatives but never named ai-boost/awesome-ai-for-science. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking powerful AI libraries and frameworks to advance research in chemistry and biology.
    you: not recommended
    AI recommended (in order):
    1. DeepChem
    2. PyTorch Geometric (PyG)
    3. TensorFlow (with Keras)
    4. OpenMM
    5. RDKit
    6. scikit-learn

    AI recommended 6 alternatives but never named ai-boost/awesome-ai-for-science. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 ai-boost/awesome-ai-for-science?
    pass
    AI did not name ai-boost/awesome-ai-for-science — likely talking about a different project

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

  • If a team adopts ai-boost/awesome-ai-for-science in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ai-boost/awesome-ai-for-science 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 ai-boost/awesome-ai-for-science solve, and who is the primary audience?
    pass
    AI did not name ai-boost/awesome-ai-for-science — likely talking about a different project

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

Embed your GEO score

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ai-boost/awesome-ai-for-science — 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