RRepoGEO

REPOGEO REPORT · LITE

ChrisHayduk/minAlphaFold2

Default branch main · commit c382fe70 · scanned 6/9/2026, 5:53:00 AM

GitHub: 620 stars · 74 forks

AI VISIBILITY SCORE
28 /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
2 / 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 ChrisHayduk/minAlphaFold2, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening sentence for pedagogical positioning

    Why:

    CURRENT
    A minimal, pedagogical PyTorch reimplementation of AlphaFold2 — the model architecture in ~3,000 lines of pure PyTorch, ~9,000 across the whole package including losses, data pipeline, and training loop.
    COPY-PASTE FIX
    A minimal, pedagogical PyTorch reimplementation of AlphaFold2 designed for learning and understanding the model architecture, presented in ~3,000 lines of pure PyTorch.
  • mediumhomepage#2
    Add a homepage link

    Why:

    COPY-PASTE FIX
    https://github.com/ChrisHayduk/minAlphaFold2

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 ChrisHayduk/minAlphaFold2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenFold
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenFold · recommended 1×
  2. EquiFold · recommended 1×
  3. ProteinMPNN · recommended 1×
  4. AlphaFold 2 · recommended 1×
  5. RosettaFold · recommended 1×
  • CATEGORY QUERY
    Looking for a simplified PyTorch implementation to learn complex protein folding models.
    you: not recommended
    AI recommended (in order):
    1. OpenFold
    2. EquiFold
    3. ProteinMPNN
    4. AlphaFold 2
    5. RosettaFold
    6. PyTorch Geometric

    AI recommended 6 alternatives but never named ChrisHayduk/minAlphaFold2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a minimal deep learning model for understanding protein structure prediction?
    you: not recommended
    AI recommended (in order):
    1. OpenFold (openfold/openfold)
    2. RoseTTAFold
    3. AlphaFold-Colab / AlphaFold-Notebooks
    4. DeepMind's AlphaFold2 GitHub Repository (deepmind/alphafold)
    5. PyTorch Geometric (PyG)
    6. Keras
    7. TensorFlow

    AI recommended 7 alternatives but never named ChrisHayduk/minAlphaFold2. 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 ChrisHayduk/minAlphaFold2?
    pass
    AI named ChrisHayduk/minAlphaFold2 explicitly

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

  • If a team adopts ChrisHayduk/minAlphaFold2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name ChrisHayduk/minAlphaFold2 — 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?

  • In one sentence, what problem does the repo ChrisHayduk/minAlphaFold2 solve, and who is the primary audience?
    pass
    AI named ChrisHayduk/minAlphaFold2 explicitly

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

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ChrisHayduk/minAlphaFold2 — 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