RRepoGEO

REPOGEO REPORT · LITE

nashsu/llm_wiki

Default branch main · commit cddc1b7c · scanned 5/8/2026, 5:32:26 AM

GitHub: 6,309 stars · 779 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 nashsu/llm_wiki, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, knowledge-base, desktop-app, personal-knowledge-management, pkm, rag, knowledge-graph, cross-platform
  • highreadme#2
    Emphasize 'desktop application' and 'personal knowledge base' in the README's opening

    Why:

    CURRENT
    A personal knowledge base that builds itself. LLM reads your documents, builds a structured wiki, and keeps it current.
    COPY-PASTE FIX
    LLM Wiki is a cross-platform desktop application that turns your documents into an organized, interlinked knowledge base — automatically. Instead of traditional RAG (retrieve-and-answer from scratch every time), the LLM incrementally builds and maintains a persistent wiki from your sources.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the official project website or a relevant landing page URL (e.g., a GitHub Pages site, a dedicated project site) to the repository's 'About' section.

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 nashsu/llm_wiki
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stardog
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stardog · recommended 2×
  2. Amazon Neptune · recommended 2×
  3. Google Cloud Natural Language API · recommended 2×
  4. Google Cloud BigQuery · recommended 2×
  5. deepset-ai/haystack · recommended 1×
  • CATEGORY QUERY
    How to automatically build a persistent, interlinked knowledge base from my document collection?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset-ai/haystack)
    2. Neo4j
    3. OpenAI API
    4. LangChain
    5. Weaviate
    6. spaCy
    7. Stardog
    8. Amazon Kendra
    9. Amazon Neptune
    10. Google Cloud Natural Language API
    11. Google Cloud BigQuery

    AI recommended 11 alternatives but never named nashsu/llm_wiki. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    AI tool to incrementally build and maintain a knowledge graph from diverse document sources?
    you: not recommended
    AI recommended (in order):
    1. TypeDB (vaticle/typedb)
    2. Stardog
    3. Ontotext GraphDB
    4. Amazon Neptune
    5. Neo4j (neo4j/neo4j)
    6. Kensho S&P Link
    7. Google Cloud Natural Language API
    8. Google Cloud BigQuery
    9. Google Cloud Storage
    10. Google Cloud AI Platform Notebooks

    AI recommended 10 alternatives but never named nashsu/llm_wiki. 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 nashsu/llm_wiki?
    pass
    AI named nashsu/llm_wiki explicitly

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

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

    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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