RRepoGEO

REPOGEO REPORT · LITE

foxminchan/BookWorm

Default branch main · commit d547507f · scanned 6/16/2026, 9:42:17 PM

GitHub: 504 stars · 67 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 foxminchan/BookWorm, 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 H1 to clarify project type

    Why:

    CURRENT
    # BookWorm
    COPY-PASTE FIX
    # BookWorm: A .NET Aspire Cloud-Native Microservices & AI Agent Reference Implementation
  • mediumabout#2
    Update repository description to emphasize "showcase" nature

    Why:

    CURRENT
    The practical implementation of Aspire using Microservices, AI-Agents
    COPY-PASTE FIX
    A practical reference implementation and showcase of cloud-native microservices with AI agents using .NET Aspire. Intended for architectural exploration, not production.
  • mediumreadme#3
    Add a dedicated "What is BookWorm?" section to the README

    Why:

    COPY-PASTE FIX
    ## What is BookWorm?

BookWorm is a reference implementation demonstrating how to build cloud-native microservices applications with AI agents using .NET Aspire, Domain-Driven Design (DDD), and Vertical Slice Architecture (VSA). It is intended for educational and architectural exploration purposes, not as a production-ready book management system.

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 foxminchan/BookWorm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
kubernetes/kubernetes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. kubernetes/kubernetes · recommended 1×
  2. Google Kubernetes Engine (GKE) · recommended 1×
  3. Amazon Elastic Kubernetes Service (EKS) · recommended 1×
  4. Azure Kubernetes Service (AKS) · recommended 1×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    How to build cloud-native microservices applications integrating AI agents?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. Google Kubernetes Engine (GKE)
    3. Amazon Elastic Kubernetes Service (EKS)
    4. Azure Kubernetes Service (AKS)
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. Docker (moby/moby)
    8. Kafka (apache/kafka)
    9. RabbitMQ (rabbitmq/rabbitmq-server)
    10. FastAPI (tiangolo/fastapi)
    11. Spring Boot (spring-projects/spring-boot)
    12. Express.js (expressjs/express)
    13. Prometheus (prometheus/prometheus)
    14. Grafana (grafana/grafana)
    15. OpenTelemetry

    AI recommended 15 alternatives but never named foxminchan/BookWorm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking architectural patterns for developing multi-agent AI systems within a distributed application.
    you: not recommended
    AI recommended (in order):
    1. Akka
    2. Erlang/OTP
    3. Kubernetes
    4. Apache Kafka
    5. RabbitMQ
    6. Amazon SQS
    7. Apache River
    8. GigaSpaces InsightEdge
    9. TensorFlow Federated
    10. PySyft

    AI recommended 10 alternatives but never named foxminchan/BookWorm. 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 foxminchan/BookWorm?
    pass
    AI named foxminchan/BookWorm explicitly

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

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

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

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foxminchan/BookWorm — 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