RRepoGEO

REPOGEO REPORT · LITE

nashsu/llm_wiki

Default branch main · commit d0437e84 · scanned 6/18/2026, 12:51:52 AM

GitHub: 11,795 stars · 1,434 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)

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

AI VISIBILITY SCORE
28 /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
2 / 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 specific, descriptive topics to the repository

    Why:

    COPY-PASTE FIX
    knowledge-base, llm, ai, desktop-app, pkm, document-management, rag, knowledge-graph, cross-platform
  • highreadme#2
    Reposition the README's opening to clearly state it's a desktop application and highlight its advanced AI capabilities

    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.
  • mediumlicense#3
    Clarify the existing license(s) directly in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under a custom or compound license. Please refer to the `LICENSE` file for specific terms and conditions.

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
Obsidian
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Obsidian · recommended 2×
  2. Notion · recommended 2×
  3. DEVONthink · recommended 2×
  4. logseq/logseq · recommended 1×
  5. Roam Research · recommended 1×
  • CATEGORY QUERY
    How can I automatically organize my documents into an intelligent, interlinked knowledge base?
    you: not recommended
    AI recommended (in order):
    1. Obsidian
    2. Notion
    3. Logseq (logseq/logseq)
    4. Roam Research
    5. Mem
    6. TiddlyWiki (TiddlyWiki/TiddlyWiki5)
    7. DEVONthink

    AI recommended 7 alternatives but never named nashsu/llm_wiki. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What desktop application builds an incrementally updated knowledge wiki from my diverse document collection?
    you: not recommended
    AI recommended (in order):
    1. Obsidian
    2. Logseq
    3. TiddlyWiki
    4. Notion
    5. DEVONthink
    6. Joplin

    AI recommended 6 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 did not name nashsu/llm_wiki — likely talking about a different project

    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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nashsu/llm_wiki — 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