RRepoGEO

REPOGEO REPORT · LITE

uncbiag/Awesome-Foundation-Models

Default branch main · commit 1a1aacd7 · scanned 5/15/2026, 12:27:34 AM

GitHub: 1,156 stars · 60 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 uncbiag/Awesome-Foundation-Models, 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
  • highlicense#1
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of a widely recognized open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#2
    Reposition the README's opening sentence to clarify the repo's nature

    Why:

    CURRENT
    A foundation model is a large-scale pretrained model (e.g., BERT, DALL-E, GPT-3) that can be adapted to a wide range of downstream applications. This term was first popularized by the Stanford Institute for Human-Centered Artificial Intelligence. This repository maintains a curated list of foundation models for vision and language tasks. Research papers without code are not included.
    COPY-PASTE FIX
    This repository maintains a curated list of foundation models for vision and language tasks. A foundation model is a large-scale pretrained model (e.g., BERT, DALL-E, GPT-3) that can be adapted to a wide range of downstream applications. This term was first popularized by the Stanford Institute for Human-Centered Artificial Intelligence. Research papers without code are not included.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project page, a related survey, or the Stanford HAI page mentioned in the README) to the 'Homepage' field in the repository settings.

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 uncbiag/Awesome-Foundation-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Hub
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Hub · recommended 1×
  2. Papers With Code · recommended 1×
  3. TensorFlow Hub · recommended 1×
  4. PyTorch Hub · recommended 1×
  5. Kaggle Models · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of pre-trained models for vision and language AI?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Papers With Code
    3. TensorFlow Hub
    4. PyTorch Hub
    5. Kaggle Models
    6. OpenMMLab

    AI recommended 6 alternatives but never named uncbiag/Awesome-Foundation-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the leading large-scale models available for various multimodal AI applications?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini
    3. Claude 3
    4. Llama 3
    5. Stable Diffusion
    6. DALL-E 3

    AI recommended 6 alternatives but never named uncbiag/Awesome-Foundation-Models. 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 uncbiag/Awesome-Foundation-Models?
    pass
    AI named uncbiag/Awesome-Foundation-Models explicitly

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

  • If a team adopts uncbiag/Awesome-Foundation-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named uncbiag/Awesome-Foundation-Models 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 uncbiag/Awesome-Foundation-Models solve, and who is the primary audience?
    pass
    AI did not name uncbiag/Awesome-Foundation-Models — 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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uncbiag/Awesome-Foundation-Models — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite