RRepoGEO

REPOGEO REPORT · LITE

chrishayuk/larql

Default branch main · commit ab7a08c1 · scanned 6/27/2026, 9:47:47 AM

GitHub: 1,066 stars · 186 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a clear "About" description

    Why:

    COPY-PASTE FIX
    A standalone tool to query, inspect, and modify transformer neural network weights as a graph database, using LQL (Lazarus Query Language). Not related to Laravel or GraphQL.
  • mediumreadme#2
    Clarify README's opening statement to counter miscategorization

    Why:

    CURRENT
    # LARQL

The model IS the database. Query neural network weights like a graph database. No GPU required.
    COPY-PASTE FIX
    # LARQL

A standalone tool to query, inspect, and modify transformer neural network weights as a graph database, using LQL (Lazarus Query Language). **LARQL is not related to Laravel or GraphQL.** No GPU required.

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ROME · recommended 1×
  2. MEND · recommended 1×
  3. SERAC · recommended 1×
  4. Knowledge Neurons · recommended 1×
  5. Concept Eraser · recommended 1×
  • CATEGORY QUERY
    How to programmatically inspect and modify factual knowledge stored within a transformer model?
    you: not recommended
    AI recommended (in order):
    1. ROME
    2. MEND
    3. SERAC
    4. Knowledge Neurons
    5. Concept Eraser
    6. Hugging Face Transformers
    7. Pinecone
    8. Weaviate
    9. PostgreSQL

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

    Show full AI answer
  • CATEGORY QUERY
    Tool to query neural network weights like a graph database for model introspection.
    you: not recommended
    AI recommended (in order):
    1. GNNExplainer
    2. Neo4j
    3. TigerGraph
    4. GraphDB
    5. NetworkX

    AI recommended 5 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.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite