RRepoGEO

REPOGEO REPORT · LITE

attentionmech/mav

Default branch main · commit e518c8ee · scanned 6/15/2026, 5:43:04 PM

GitHub: 523 stars · 42 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 attentionmech/mav, 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
  • highabout#1
    Clarify the 'About' description to specify LLM focus

    Why:

    CURRENT
    Model Activity Visualiser
    COPY-PASTE FIX
    Visualize and inspect the internal workings of Large Language Models (LLMs) during text generation.
  • highreadme#2
    Update README H1 to explicitly mention LLMs

    Why:

    CURRENT
    # MAV - Model Activity Visualiser
    COPY-PASTE FIX
    # MAV - Large Language Model Activity Visualiser
  • mediumreadme#3
    Integrate common LLM inspection keywords into the README

    Why:

    COPY-PASTE FIX
    MAV allows you to **inspect activations**, **visualize attention patterns**, and **interpret the internal thought process** of LLMs as they generate text.

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 attentionmech/mav
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Captum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Captum · recommended 2×
  2. Ecco · recommended 2×
  3. LIT · recommended 1×
  4. TransformerViz · recommended 1×
  5. BertViz · recommended 1×
  • CATEGORY QUERY
    How can I visualize the internal thought process of large language models during text generation?
    you: not recommended
    AI recommended (in order):
    1. LIT
    2. Captum
    3. Ecco
    4. TransformerViz
    5. BertViz
    6. OpenAI's Activation Atlas
    7. Lucid
    8. scikit-learn
    9. PyTorch
    10. TensorFlow

    AI recommended 10 alternatives but never named attentionmech/mav. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for inspecting activations and attention patterns in generative AI models?
    you: not recommended
    AI recommended (in order):
    1. TransformerLens
    2. Neuroscope
    3. Captum
    4. Ecco
    5. LIME
    6. SHAP
    7. TensorBoard

    AI recommended 7 alternatives but never named attentionmech/mav. 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 attentionmech/mav?
    pass
    AI named attentionmech/mav explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of attentionmech/mav. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/attentionmech/mav.svg)](https://repogeo.com/en/r/attentionmech/mav)
HTML
<a href="https://repogeo.com/en/r/attentionmech/mav"><img src="https://repogeo.com/badge/attentionmech/mav.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

attentionmech/mav — 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