RRepoGEO

REPOGEO REPORT · LITE

castorini/daam

Default branch main · commit c30493ed · scanned 6/15/2026, 4:21:55 PM

GitHub: 798 stars · 70 forks

AI VISIBILITY SCORE
35 /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
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 castorini/daam, 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 the README's opening statement to emphasize specialization

    Why:

    CURRENT
    In our paper, we propose diffusion attentive attribution maps (DAAM), a cross attention-based approach for interpreting Stable Diffusion.
    COPY-PASTE FIX
    DAAM is a specialized library for generating **diffusion attentive attribution maps**, offering a unique cross-attention based approach to interpret *generative image diffusion models* like Stable Diffusion. It provides insights specifically tailored for diffusion architectures, differentiating it from general XAI methods and general ML libraries.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/tetrisd/Diffusion-Attentive-Attribution-Maps
  • lowreadme#3
    Explicitly state the problem DAAM solves and its primary audience in the README

    Why:

    COPY-PASTE FIX
    DAAM addresses the critical need for understanding the internal decision-making processes of complex generative image diffusion models. It is designed for AI researchers and practitioners who require detailed, model-specific explanations for their diffusion model outputs.

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 castorini/daam
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/diffusers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/diffusers · recommended 1×
  2. matplotlib/matplotlib · recommended 1×
  3. mwaskom/seaborn · recommended 1×
  4. tensorflow/lucid · recommended 1×
  5. Concept Bottleneck Models (CBMs) · recommended 1×
  • CATEGORY QUERY
    How to interpret the decision-making process of generative image diffusion models?
    you: not recommended
    AI recommended (in order):
    1. Diffusers Library (Hugging Face) (huggingface/diffusers)
    2. matplotlib (matplotlib/matplotlib)
    3. seaborn (mwaskom/seaborn)
    4. Lucid (Google) (tensorflow/lucid)
    5. Concept Bottleneck Models (CBMs)
    6. TESA (Towards Explainable Stable Diffusion) (ExplainableAI-Lab/TESA)
    7. CLIP Embeddings (openai/CLIP)
    8. Captum (PyTorch) (pytorch/captum)
    9. TensorBoard (tensorflow/tensorboard)
    10. Weights & Biases (wandb/wandb)

    AI recommended 10 alternatives but never named castorini/daam. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for explainable AI to understand outputs from image generation models.
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. Grad-CAM
    4. Captum
    5. InterpretML
    6. Lucid

    AI recommended 6 alternatives but never named castorini/daam. 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 castorini/daam?
    pass
    AI named castorini/daam explicitly

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

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

    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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
castorini/daam — RepoGEO report