RRepoGEO

REPOGEO REPORT · LITE

Astro-Han/karpathy-llm-wiki

Default branch main · commit 9e8c4f44 · scanned 6/7/2026, 7:58:13 PM

GitHub: 1,013 stars · 139 forks

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 Astro-Han/karpathy-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
  • highreadme#1
    Clarify README opening to emphasize 'tool for building' over 'content about'

    Why:

    CURRENT
    `karpathy-llm-wiki` packages Karpathy's LLM Wiki idea into one installable Agent Skills skill.
    COPY-PASTE FIX
    `karpathy-llm-wiki` is a **framework and installable Agent Skills skill** that packages Andrej Karpathy's LLM Wiki *idea* into a practical system. This project is *not* a wiki *about* Karpathy's content, but a tool for your coding agent to ingest sources into `raw/`, compile durable knowledge pages into `wiki/`, answer questions with citations, and lint the wiki for consistency.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://agentskills.io
  • mediumreadme#3
    Enhance the 'LLM Wiki vs RAG' comparison with a clear summary

    Why:

    COPY-PASTE FIX
    (After the comparison table in the 'LLM Wiki vs RAG' section) In summary, while RAG systems perform reactive retrieval from raw documents, `karpathy-llm-wiki` empowers your LLM agent to proactively synthesize and maintain a structured, citable knowledge base, ensuring durable, high-quality information for complex queries.

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 Astro-Han/karpathy-llm-wiki
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 3×
  2. OpenAI API · recommended 2×
  3. PostgreSQL · recommended 2×
  4. LangChain · recommended 1×
  5. ArangoDB · recommended 1×
  • CATEGORY QUERY
    How to build an LLM-maintained knowledge base for synthesized information, not just RAG?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Neo4j
    3. ArangoDB
    4. Amazon Neptune
    5. LlamaIndex
    6. Kuzu
    7. Neo4j
    8. Haystack
    9. Elasticsearch
    10. OpenSearch
    11. Weaviate
    12. OpenAI API
    13. Anthropic Claude
    14. Google Gemini
    15. PostgreSQL
    16. MySQL
    17. MongoDB
    18. DynamoDB
    19. Kestra
    20. Apache Airflow
    21. OpenAI API
    22. PostgreSQL
    23. Neo4j

    AI recommended 23 alternatives but never named Astro-Han/karpathy-llm-wiki. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to manage coding agent knowledge, compiling raw sources into structured, citable wiki pages?
    you: not recommended
    AI recommended (in order):
    1. Nuclia (nuclia/nuclia-db)
    2. Obsidian
    3. Confluence
    4. Docusaurus (facebook/docusaurus)
    5. BookStack (BookStackApp/BookStack)
    6. MediaWiki (wikimedia/mediawiki)

    AI recommended 6 alternatives but never named Astro-Han/karpathy-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 Astro-Han/karpathy-llm-wiki?
    pass
    AI named Astro-Han/karpathy-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 Astro-Han/karpathy-llm-wiki in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Astro-Han/karpathy-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 Astro-Han/karpathy-llm-wiki solve, and who is the primary audience?
    pass
    AI did not name Astro-Han/karpathy-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?

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Astro-Han/karpathy-llm-wiki — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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