RRepoGEO

REPOGEO REPORT · LITE

atomicmemory/llm-wiki-compiler

Default branch main · commit 4269f9c0 · scanned 6/17/2026, 8:47:01 PM

GitHub: 1,539 stars · 155 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 atomicmemory/llm-wiki-compiler, 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
    Add a concise, category-defining tagline to the README's opening.

    Why:

    CURRENT
    The current README starts with a 'Breaking News' section after the H1, which delays the core value proposition.
    COPY-PASTE FIX
    Add the following line directly after the `# llmwiki` heading: `The knowledge compiler. Raw sources in, interlinked wiki out. Inspired by Karpathy's LLM Wiki pattern.`
  • mediumtopics#2
    Expand topics to include more specific terms related to knowledge compilation and agent context.

    Why:

    CURRENT
    cli, compiler, context-engineering, karpathy, knowledge-base, knowledge-compilation, llm, markdown, obsidian, wiki
    COPY-PASTE FIX
    cli, compiler, context-engineering, karpathy, knowledge-base, knowledge-compilation, llm, markdown, obsidian, wiki, knowledge-graph, auditable-ai, agent-context, llm-ops, semantic-wiki, content-compilation
  • mediumcomparison#3
    Add a dedicated 'Comparison' section to the README.

    Why:

    COPY-PASTE FIX
    Add a new section titled `## How llmwiki differs` that explicitly contrasts `llmwiki` with RAG frameworks (LlamaIndex, LangChain), vector/graph databases (Weaviate, Neo4j, Pinecone), and general wiki/note-taking tools (Obsidian, Confluence), highlighting `llmwiki`'s focus on durable, compiled, auditable knowledge for agents.

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 atomicmemory/llm-wiki-compiler
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
run-llama/llama_index
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. run-llama/llama_index · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. weaviate/weaviate · recommended 1×
  4. neo4j/neo4j · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to build a persistent, interlinked knowledge base for LLM context from diverse documents?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (run-llama/llama_index)
    2. LangChain (langchain-ai/langchain)
    3. Weaviate (weaviate/weaviate)
    4. Neo4j (neo4j/neo4j)
    5. Pinecone
    6. OpenSearch (opensearch-project/OpenSearch)
    7. Elasticsearch (elastic/elasticsearch)

    AI recommended 7 alternatives but never named atomicmemory/llm-wiki-compiler. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to compile raw data into an auditable, citation-traceable wiki for AI agent context?
    you: not recommended
    AI recommended (in order):
    1. Nuclino
    2. Confluence
    3. Obsidian
    4. DokuWiki
    5. MediaWiki
    6. Notion
    7. BookStack

    AI recommended 7 alternatives but never named atomicmemory/llm-wiki-compiler. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 atomicmemory/llm-wiki-compiler?
    pass
    AI did not name atomicmemory/llm-wiki-compiler — 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 atomicmemory/llm-wiki-compiler in production, what risks or prerequisites should they evaluate first?
    pass
    AI named atomicmemory/llm-wiki-compiler 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 atomicmemory/llm-wiki-compiler solve, and who is the primary audience?
    pass
    AI named atomicmemory/llm-wiki-compiler explicitly

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

Embed your GEO score

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MARKDOWN (README)
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