RRepoGEO

REPOGEO REPORT · LITE

sdyckjq-lab/llm-wiki-skill

Default branch main · commit add8225e · scanned 5/23/2026, 12:48:27 AM

GitHub: 1,616 stars · 210 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
17 /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
1 / 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 sdyckjq-lab/llm-wiki-skill, 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 topics to improve categorization

    Why:

    COPY-PASTE FIX
    personal-knowledge-management, knowledge-base, llm-skill, ai-assistant, knowledge-graph, offline-first, karpathy-llm-wiki, pkm
  • highlicense#2
    Add a LICENSE file and declare it in README

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0). Then, add a line to the README like: `License: [Your Chosen License, e.g., MIT]`.
  • highreadme#3
    Reposition README's opening to clarify core purpose

    Why:

    CURRENT
    基于 Andrej Karpathy 的 llm-wiki 方法论
**更适合国内宝宝体质的 K 神知识库**
把碎片化的信息变成持续积累、互相链接的知识库
    COPY-PASTE FIX
    llm-wiki 是一个基于 Andrej Karpathy 方法论的 **AI 驱动个人知识库构建工具**，旨在将碎片化信息转化为持续积累、互相链接的知识库，并支持多平台 Agent 集成。
**更适合国内宝宝体质的 K 神知识库**

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 sdyckjq-lab/llm-wiki-skill
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Obsidian
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Obsidian · recommended 1×
  2. Dataview · recommended 1×
  3. Excalidraw · recommended 1×
  4. Smart Connections · recommended 1×
  5. Text Generator · recommended 1×
  • CATEGORY QUERY
    How can I build a structured personal knowledge base using AI for information digestion?
    you: not recommended
    AI recommended (in order):
    1. Obsidian
    2. Dataview
    3. Excalidraw
    4. Smart Connections
    5. Text Generator
    6. Mem.ai
    7. Logseq
    8. Logseq AI
    9. Notion
    10. Notion AI
    11. Readwise Reader
    12. TiddlyWiki

    AI recommended 12 alternatives but never named sdyckjq-lab/llm-wiki-skill. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools create an offline, interactive knowledge graph from processed unstructured data?
    you: not recommended
    AI recommended (in order):
    1. Neo4j Desktop (neo4j/neo4j-desktop)
    2. Neo4j AuraDB Free Tier
    3. GraphDB Free
    4. Gephi (gephi/gephi)
    5. NetworkX (networkx/networkx)
    6. Dash (plotly/dash)
    7. Plotly (plotly/plotly.py)
    8. yFiles for HTML
    9. SQLite
    10. DuckDB (duckdb/duckdb)
    11. Graphviz (graphviz/graphviz)
    12. D3.js (d3/d3)

    AI recommended 12 alternatives but never named sdyckjq-lab/llm-wiki-skill. 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 sdyckjq-lab/llm-wiki-skill?
    pass
    AI did not name sdyckjq-lab/llm-wiki-skill — 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 sdyckjq-lab/llm-wiki-skill in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sdyckjq-lab/llm-wiki-skill 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 sdyckjq-lab/llm-wiki-skill solve, and who is the primary audience?
    pass
    AI did not name sdyckjq-lab/llm-wiki-skill — 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?

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sdyckjq-lab/llm-wiki-skill — 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