RRepoGEO

REPOGEO REPORT · LITE

SkalskiP/awesome-foundation-and-multimodal-models

Default branch master · commit 80b5ae1c · scanned 6/2/2026, 11:02:45 PM

GitHub: 639 stars · 46 forks

AI VISIBILITY SCORE
22 /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
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 SkalskiP/awesome-foundation-and-multimodal-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
  • highreadme#1
    Reposition README opening to clarify it's an 'awesome list'

    Why:

    CURRENT
    <h1 align="center">awesome foundation and multimodal models</h1>
    
    ## 👁️ + 💬 + 🎧 = 🤖
    
    **foundation modela pre-trained machine learning model that serves as a base for a wide range of downstream tasks. It captures general knowledge from a large dataset and can be fine-tuned to perform specific tasks more effectively.
    
    **multimodal modela model that can process multiple modalities (e.g. text, image,
    video, audio, etc.) at the same time.
    COPY-PASTE FIX
    <h1 align="center">awesome foundation and multimodal models</h1>
    
    ## 👁️ + 💬 + 🎧 = 🤖
    
    A curated list of top foundation and multimodal models, including papers, code, examples, and tutorials. This repository serves as a comprehensive resource for AI/ML practitioners and researchers exploring the cutting edge of multimodal AI.
    
    **foundation modela pre-trained machine learning model that serves as a base for a wide range of downstream tasks. It captures general knowledge from a large dataset and can be fine-tuned to perform specific tasks more effectively.
    
    **multimodal modela model that can process multiple modalities (e.g. text, image,
    video, audio, etc.) at the same time.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Create a LICENSE file in the root of the repository, choosing an appropriate open-source license like MIT or Apache-2.0, and ensure its content is standard.)
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    (Add a relevant URL to the repository's homepage field in GitHub settings, e.g., a related project, organization, or a hosted version of the list if applicable.)

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 SkalskiP/awesome-foundation-and-multimodal-models
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Gemini
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Gemini · recommended 1×
  2. OpenAI GPT-4o · recommended 1×
  3. Meta Llama 3 · recommended 1×
  4. Microsoft Copilot · recommended 1×
  5. Google PaLM 2 · recommended 1×
  • CATEGORY QUERY
    Which multimodal AI models process and understand data across vision, text, and audio modalities?
    you: not recommended
    AI recommended (in order):
    1. Google Gemini
    2. OpenAI GPT-4o
    3. Meta Llama 3
    4. Microsoft Copilot
    5. Google PaLM 2

    AI recommended 5 alternatives but never named SkalskiP/awesome-foundation-and-multimodal-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need powerful foundation models for advanced computer vision tasks like zero-shot detection and image captioning.
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. DALL-E 3
    3. OWL-ViT
    4. Grounding DINO
    5. BLIP-2
    6. Florence-2
    7. ViT

    AI recommended 7 alternatives but never named SkalskiP/awesome-foundation-and-multimodal-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 SkalskiP/awesome-foundation-and-multimodal-models?
    pass
    AI did not name SkalskiP/awesome-foundation-and-multimodal-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?

  • If a team adopts SkalskiP/awesome-foundation-and-multimodal-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SkalskiP/awesome-foundation-and-multimodal-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 SkalskiP/awesome-foundation-and-multimodal-models solve, and who is the primary audience?
    pass
    AI did not name SkalskiP/awesome-foundation-and-multimodal-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?

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  • Brand-free category queries5 vs 2 in Lite
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