RRepoGEO

REPOGEO REPORT · LITE

yvgude/lean-ctx

Default branch main · commit 61bba7df · scanned 5/15/2026, 5:41:40 AM

GitHub: 1,652 stars · 171 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 yvgude/lean-ctx, 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 disambiguation statement to the README's introduction

    Why:

    CURRENT
    The README excerpt starts with ASCII art and then "<h3 align="center">The context layer for AI coding agents</h3>".
    COPY-PASTE FIX
    Add the following sentence immediately after the main title/tagline: "Note: `lean-ctx` is a Context OS for AI Development and is not related to the Lean 4 theorem prover."
  • mediumreadme#2
    Rephrase README's opening to highlight context management for AI agents

    Why:

    CURRENT
    <p align="center"> <strong>Reduce token waste in Cursor, Claude Code, Copilot, Windsurf, Codex, Gemini & more by 60–95% (up to 99% on cached reads)</strong><br/> Shell Hook + MCP Server · 60 tools · 10 read modes · 95+ patterns · Single Rust binary<br/> <strong>Context Intelligence:</strong> Bounce detection, context gate with graph/intent/knowledge-based mode routing, MCP resources &amp; prompts, dynamic tool categories, client capability detection across 9+ IDEs </p>
    COPY-PASTE FIX
    <strong>`lean-ctx` is the context management system for AI agent development, drastically reducing token waste (60–95%, up to 99% on cached reads) in tools like Cursor, Claude Code, Copilot, and Gemini.</strong> It features a Shell Hook + MCP Server, 60+ tools, 10 read modes, and advanced Context Intelligence for efficient AI coding workflows.
  • mediumreadme#3
    Add a 'Comparison' or 'How it fits' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled "How `lean-ctx` Compares" or "Integration with AI Frameworks" that clarifies its role. For example: "While frameworks like LangChain and LlamaIndex focus on LLM orchestration and data retrieval, `lean-ctx` operates as a foundational Context OS, optimizing the input context for *any* AI coding assistant or agent, complementing these frameworks by reducing token costs and improving context relevance at a lower level."

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 yvgude/lean-ctx
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 3×
  2. Pinecone · recommended 2×
  3. BerriAI/litellm · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. huggingface/peft · recommended 1×
  • CATEGORY QUERY
    How to reduce token usage and improve efficiency for LLM-powered coding assistants?
    you: not recommended
    AI recommended (in order):
    1. LiteLLM (BerriAI/litellm)
    2. LangChain (langchain-ai/langchain)
    3. Hugging Face Transformers (huggingface/transformers)
    4. PEFT (huggingface/peft)
    5. LlamaIndex (run-llama/llama_index)
    6. LangChain (langchain-ai/langchain)
    7. FAISS (facebookresearch/faiss)
    8. Pinecone
    9. Weaviate (weaviate/weaviate)
    10. Tiktoken (openai/tiktoken)
    11. Hugging Face Tokenizers (huggingface/tokenizers)
    12. OpenAI API
    13. LangChain (langchain-ai/langchain)

    AI recommended 13 alternatives but never named yvgude/lean-ctx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a context management system to enhance AI agent development workflows.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Deepset's Haystack
    5. OpenAI's Assistants API
    6. Pinecone
    7. Weaviate
    8. Milvus

    AI recommended 8 alternatives but never named yvgude/lean-ctx. 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 yvgude/lean-ctx?
    pass
    AI named yvgude/lean-ctx explicitly

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

  • If a team adopts yvgude/lean-ctx in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yvgude/lean-ctx 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 yvgude/lean-ctx solve, and who is the primary audience?
    pass
    AI named yvgude/lean-ctx explicitly

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

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yvgude/lean-ctx — 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