RRepoGEO

REPOGEO REPORT · LITE

diet103/claude-code-infrastructure-showcase

Default branch main · commit a5818cb9 · scanned 6/22/2026, 5:52:57 AM

GitHub: 9,711 stars · 1,218 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to clarify Claude-specific skill activation

    Why:

    CURRENT
    # Claude Code Infrastructure Showcase

**A curated reference library of production-tested Claude Code infrastructure.**

Born from 6 months of real-world use managing a complex TypeScript microservices project, this showcase provides the patterns and systems that solved the "skills don't activate automatically" problem and scaled Claude Code for enterprise development.
    COPY-PASTE FIX
    # Claude Code Infrastructure Showcase

**A curated reference library of production-tested patterns and infrastructure for Claude Code developers, specifically focused on solving skill auto-activation and building modular AI agents.**

Born from 6 months of real-world use managing a complex TypeScript microservices project, this showcase provides the systems that scaled Claude Code for enterprise development.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    claude, anthropic-claude, ai-agent, skill-activation, generative-ai, code-infrastructure, ai-development, modular-ai
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

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

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
GitHub Copilot X
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot X · recommended 1×
  2. Cursor · recommended 1×
  3. JetBrains AI Assistant · recommended 1×
  4. Tabnine Pro/Enterprise · recommended 1×
  5. Codeium · recommended 1×
  • CATEGORY QUERY
    How to get AI coding assistants to automatically activate relevant skills?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot X
    2. Cursor
    3. JetBrains AI Assistant
    4. Tabnine Pro/Enterprise
    5. Codeium
    6. Amazon CodeWhisperer

    AI recommended 6 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 agents for enterprise code development?
    you: not recommended
    AI recommended (in order):
    1. OpenAPI
    2. gRPC
    3. JSON Schema
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Git
    7. MLflow (mlflow/mlflow)
    8. Docker
    9. Kubernetes (kubernetes/kubernetes)
    10. Prometheus (prometheus/prometheus)
    11. Grafana (grafana/grafana)
    12. OpenTelemetry (open-telemetry/opentelemetry-specification)
    13. Weights & Biases (wandb/wandb)

    AI recommended 13 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