RRepoGEO

REPOGEO REPORT · LITE

mahmoodlab/TRIDENT

Default branch main · commit 091fe0c7 · scanned 6/12/2026, 8:47:13 PM

GitHub: 572 stars · 125 forks

AI VISIBILITY SCORE
40 /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
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 mahmoodlab/TRIDENT, 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's opening sentence to highlight "deep learning foundation models"

    Why:

    CURRENT
    Trident is a toolkit for large-scale whole-slide image processing.
    COPY-PASTE FIX
    Trident is a comprehensive toolkit for large-scale whole-slide image processing, specifically designed for deep learning foundation models in computational pathology.
  • mediumlicense#2
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    Add a line near the top of the README, e.g., after the initial description: "This project is licensed under the terms specified in the [LICENSE](LICENSE) file."
  • lowreadme#3
    Explicitly label patch/slide encoders as "foundation models" in the Key Features

    Why:

    CURRENT
    22+ patch encoders**: UNI, CONCHv1.5, Virchow, Prov-GigaPath, H-Optimus-0, etc. Slide encoders**: Titan, GigaPath, PRISM, CHIEF, Madeleine, Feather.
    COPY-PASTE FIX
    22+ patch encoders**: Includes leading deep learning foundation models like UNI, CONCHv1.5, Virchow, Prov-GigaPath, H-Optimus-0, etc. **Slide encoders**: Features powerful foundation models such as Titan, GigaPath, PRISM, CHIEF, Madeleine, Feather.

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 mahmoodlab/TRIDENT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
HistoGPT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. HistoGPT · recommended 1×
  2. UniPath · recommended 1×
  3. CTransPath · recommended 1×
  4. OpenAI CLIP · recommended 1×
  5. DINOv2 · recommended 1×
  • CATEGORY QUERY
    How to process whole-slide pathology images efficiently using deep learning foundation models?
    you: not recommended
    AI recommended (in order):
    1. HistoGPT
    2. UniPath
    3. CTransPath
    4. OpenAI CLIP
    5. DINOv2
    6. MAE
    7. Google's Vision Transformer (ViT)
    8. Swin Transformer
    9. MONAI

    AI recommended 9 alternatives but never named mahmoodlab/TRIDENT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for large-scale tissue segmentation and embedding in histology slides?
    you: not recommended
    AI recommended (in order):
    1. QuPath
    2. HALO
    3. Aperio ImageScope
    4. Visiopharm
    5. ASAP
    6. ImageJ/Fiji
    7. PathML

    AI recommended 7 alternatives but never named mahmoodlab/TRIDENT. 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 mahmoodlab/TRIDENT?
    pass
    AI named mahmoodlab/TRIDENT explicitly

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

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

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

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mahmoodlab/TRIDENT — 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