RRepoGEO

REPOGEO REPORT · LITE

theDakshJaitly/mex

Default branch main · commit a390a1b2 · scanned 6/2/2026, 6:02:16 PM

GitHub: 737 stars · 46 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 theDakshJaitly/mex, 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 clear disambiguation statement in the README's opening

    Why:

    CURRENT
    **Persistent project memory for AI coding agents.**
    COPY-PASTE FIX
    **mex: Persistent project memory for AI coding agents.** This CLI tool is designed for LLM-powered development workflows, providing structured context management and drift detection. *Note: This project is unrelated to MATLAB MEX files.*
  • mediumtopics#2
    Expand topics to better reflect AI agent memory and context management

    Why:

    CURRENT
    claude-code, claude-code-skills, cli-tool, codex, context-management, cursor, developer-tools, documentation, llm, memory-management, typescript
    COPY-PASTE FIX
    ai-agents, llm-agents, agent-memory, context-management, project-memory, code-scaffold, drift-detection, cli-tool, typescript, developer-tools, llm-context, ai-coding-assistant
  • lowreadme#3
    Clarify the 'What It Does' section's opening

    Why:

    CURRENT
    mex creates a structured markdown scaffold for agent memory:
    COPY-PASTE FIX
    mex establishes a structured markdown scaffold for persistent agent memory, organizing context into key files:

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 theDakshJaitly/mex
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Pinecone · recommended 1×
  4. Weaviate · recommended 1×
  5. ChromaDB · recommended 1×
  • CATEGORY QUERY
    How can I give my AI coding assistant persistent memory across development sessions?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Weaviate
    4. ChromaDB
    5. LlamaIndex
    6. FAISS
    7. Milvus
    8. PostgreSQL
    9. pgvector
    10. MongoDB
    11. SQLite
    12. Redis

    AI recommended 12 alternatives but never named theDakshJaitly/mex. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to manage project context for LLM agents and detect code documentation drift?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. DocuMentor
    4. GitHub Copilot
    5. Sourcegraph
    6. tree-sitter
    7. OpenAI API
    8. Anthropic API

    AI recommended 8 alternatives but never named theDakshJaitly/mex. 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 theDakshJaitly/mex?
    pass
    AI named theDakshJaitly/mex explicitly

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

  • If a team adopts theDakshJaitly/mex in production, what risks or prerequisites should they evaluate first?
    pass
    AI named theDakshJaitly/mex 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 theDakshJaitly/mex solve, and who is the primary audience?
    pass
    AI named theDakshJaitly/mex 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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theDakshJaitly/mex — 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