RRepoGEO

REPOGEO REPORT · LITE

MinishLab/semble

Default branch main · commit 62ecab2a · scanned 5/7/2026, 9:22:29 PM

GitHub: 735 stars · 59 forks

AI VISIBILITY SCORE
40 /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
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 MinishLab/semble, 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
    Strengthen the README's opening statement for AI clarity

    Why:

    CURRENT
    Semble is a code search library built for agents.
    COPY-PASTE FIX
    Semble is a specialized code search engine for AI agents, delivering precise, token-efficient code snippets instantly.
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Comparison to Alternatives' that explicitly contrasts Semble with popular AI agent frameworks (e.g., LlamaIndex, LangChain) and traditional code search tools (e.g., grep, ripgrep, Sourcegraph) on speed, token efficiency, and agent-specific features.
  • lowtopics#3
    Expand GitHub topics with relevant AI/RAG keywords

    Why:

    CURRENT
    agents, code-search, embeddings, mcp, mcp-server, model-context-protocol, retrieval
    COPY-PASTE FIX
    agents, code-search, embeddings, mcp, mcp-server, model-context-protocol, retrieval, rag, llm-tools, ai-agents

Category GEO backends resolved for this scan: google/gemini-2.0-flash-001, deepseek/deepseek-chat

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.0-flash-001. 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 MinishLab/semble
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Semantic Kernel
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Semantic Kernel · recommended 1×
  2. LlamaIndex · recommended 1×
  3. LangChain · recommended 1×
  4. Haystack · recommended 1×
  5. Faiss · recommended 1×
  • CATEGORY QUERY
    How to reduce token usage for AI agents performing code analysis and retrieval?
    you: not recommended
    AI recommended (in order):
    1. Semantic Kernel
    2. LlamaIndex
    3. LangChain
    4. Haystack
    5. Faiss
    6. Hugging Face Transformers

    AI recommended 6 alternatives but never named MinishLab/semble. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient, local code search tools for integrating with AI development workflows?
    you: not recommended
    AI recommended (in order):
    1. grep
    2. ripgrep (BurntSushi/ripgrep)
    3. Sourcegraph (sourcegraph/sourcegraph)
    4. CodeSearch
    5. ack (beyondgrep/ack3)
    6. The Silver Searcher (ggreer/the_silver_searcher)
    7. Visual Studio Code (microsoft/vscode)
    8. GitHub Copilot
    9. Tabnine

    AI recommended 9 alternatives but never named MinishLab/semble. 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 MinishLab/semble?
    pass
    AI named MinishLab/semble explicitly

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

  • If a team adopts MinishLab/semble in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MinishLab/semble 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 MinishLab/semble solve, and who is the primary audience?
    pass
    AI named MinishLab/semble 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
  • Prioritized action items8 vs 3 in Lite