RRepoGEO

REPOGEO REPORT · LITE

chrishayuk/larql

Default branch main · commit b6d5e8d5 · scanned 5/28/2026, 9:43:13 AM

GitHub: 1,020 stars · 173 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 chrishayuk/larql, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Decompiles transformer models into a queryable format (vindex) and provides LQL to browse, edit, and recompile their knowledge, treating neural network weights like a graph database.
  • hightopics#2
    Add relevant repository topics

    Why:

    COPY-PASTE FIX
    llm-interpretability, model-editing, transformer-models, neural-networks, graph-database, ai-research, rust
  • mediumreadme#3
    Enhance README H1 for clarity and scope

    Why:

    CURRENT
    # LARQL
    COPY-PASTE FIX
    # LARQL: Query Neural Network Weights as a Graph Database

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 chrishayuk/larql
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ROME
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ROME · recommended 2×
  2. TransformerLens · recommended 1×
  3. LIME · recommended 1×
  4. SHAP · recommended 1×
  5. ConceptBottleneckModel · recommended 1×
  • CATEGORY QUERY
    How to inspect and query the internal knowledge stored within large language models?
    you: not recommended
    AI recommended (in order):
    1. TransformerLens
    2. LIME
    3. SHAP
    4. ConceptBottleneckModel
    5. OpenAI's Microscope
    6. Google's Lucid
    7. OpenIE
    8. Stanford CoreNLP
    9. ROME

    AI recommended 9 alternatives but never named chrishayuk/larql. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to modify or add facts to a transformer model without requiring full retraining?
    you: not recommended
    AI recommended (in order):
    1. ROME
    2. MEND
    3. SERAC
    4. MEMIT
    5. LoRA
    6. Knowledge Neurons

    AI recommended 6 alternatives but never named chrishayuk/larql. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 chrishayuk/larql?
    pass
    AI named chrishayuk/larql explicitly

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

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

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

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chrishayuk/larql — 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