RRepoGEO

REPOGEO REPORT · LITE

diet103/claude-code-infrastructure-showcase

Default branch main · commit a5818cb9 · scanned 5/11/2026, 11:43:22 PM

GitHub: 9,633 stars · 1,224 forks

AI VISIBILITY SCORE
22 /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
1 / 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 diet103/claude-code-infrastructure-showcase, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Refine README's opening sentence for clearer positioning

    Why:

    CURRENT
    **A curated reference library of production-tested Claude Code infrastructure.**
    COPY-PASTE FIX
    **A curated reference library of production-tested infrastructure for building robust AI agents and enabling automatic skill activation with Claude Code.**
  • lowhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/diet103/claude-code-infrastructure-showcase

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 diet103/claude-code-infrastructure-showcase
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RasaHQ/rasa
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RasaHQ/rasa · recommended 1×
  2. microsoft/BotFramework-Composer · recommended 1×
  3. LUIS (Language Understanding Intelligent Service) · recommended 1×
  4. QnA Maker · recommended 1×
  5. Dialogflow ES (Essentials) · recommended 1×
  • CATEGORY QUERY
    How to implement automatic skill activation for AI assistants in enterprise projects?
    you: not recommended
    AI recommended (in order):
    1. Rasa (RasaHQ/rasa)
    2. Microsoft Bot Framework Composer (microsoft/BotFramework-Composer)
    3. LUIS (Language Understanding Intelligent Service)
    4. QnA Maker
    5. Dialogflow ES (Essentials)
    6. Dialogflow CX (Customer Experience)
    7. Amazon Lex
    8. AWS Lambda
    9. spaCy (explosion/spaCy)
    10. NLTK (nltk/nltk)
    11. Hugging Face Transformers (huggingface/transformers)
    12. IBM Watson Assistant

    AI recommended 12 alternatives but never named diet103/claude-code-infrastructure-showcase. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are best practices for building modular AI agent infrastructure for complex tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Function Calling
    5. Instructor
    6. Marvin
    7. Redis
    8. PostgreSQL
    9. LangSmith
    10. Weights & Biases
    11. Kubernetes
    12. FastAPI

    AI recommended 12 alternatives but never named diet103/claude-code-infrastructure-showcase. 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 diet103/claude-code-infrastructure-showcase?
    pass
    AI did not name diet103/claude-code-infrastructure-showcase — 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 diet103/claude-code-infrastructure-showcase in production, what risks or prerequisites should they evaluate first?
    pass
    AI named diet103/claude-code-infrastructure-showcase 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 diet103/claude-code-infrastructure-showcase solve, and who is the primary audience?
    pass
    AI did not name diet103/claude-code-infrastructure-showcase — 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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diet103/claude-code-infrastructure-showcase — 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
diet103/claude-code-infrastructure-showcase — RepoGEO report