RRepoGEO

REPOGEO REPORT · LITE

Azure-Samples/miyagi

Default branch main · commit 2e25a228 · scanned 6/5/2026, 3:03:06 AM

GitHub: 752 stars · 255 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 Azure-Samples/miyagi, 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 the README's opening paragraph to correct miscategorization

    Why:

    CURRENT
    Project Miyagi showcases Microsoft's Copilot Stack in an envisioning workshop aimed at designing, developing, and deploying enterprise-grade intelligent apps. By exploring both generative and traditional ML use cases, Miyagi offers an experiential approach to developing AI-infused product experiences that enhance productivity and enable hyper-personalization. Additionally, the workshop introduces traditional software engineers to emerging design patterns in prompt engineering, such as chain-of-thought and retrieval-augmentation, as well as to techniques like vectorization for long-term memory, fine-tuning of OSS models, agent-like orchestration, and plugins or tools for augmenting and grounding LLMs.
    COPY-PASTE FIX
    Project Miyagi is an envisioning workshop and comprehensive sample demonstrating how to design, develop, and deploy enterprise-grade intelligent applications using Microsoft's Copilot Stack. It provides practical guidance and examples for building AI-infused product experiences, exploring both generative and traditional ML use cases. This workshop introduces traditional software engineers to emerging design patterns in prompt engineering, such as chain-of-thought and retrieval-augmentation, as well as to techniques like vectorization for long-term memory, fine-tuning of OSS models, agent-like orchestration, and plugins or tools for augmenting and grounding LLMs.
  • mediumreadme#2
    Add a sentence to differentiate Miyagi from standalone AI frameworks

    Why:

    COPY-PASTE FIX
    Unlike standalone AI frameworks, Miyagi provides an end-to-end solution sample and architectural guidance for integrating technologies like Semantic Kernel, Promptflow, LlamaIndex, and LangChain into enterprise applications.
  • lowlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details on the applicable licenses.

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 Azure-Samples/miyagi
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. OpenAI API · recommended 1×
  4. Azure OpenAI Service · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to build enterprise-grade intelligent applications using generative AI and agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Azure OpenAI Service
    5. Hugging Face Transformers
    6. Pinecone
    7. Weaviate
    8. Kubernetes
    9. MLflow

    AI recommended 9 alternatives but never named Azure-Samples/miyagi. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking frameworks for advanced prompt engineering, RAG, and LLM agent orchestration.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. AutoGen (microsoft/autogen)
    5. DSPy (stanfordnlp/dspy)
    6. Magentic (jacksmith15/magentic)
    7. OpenAI Assistants API

    AI recommended 7 alternatives but never named Azure-Samples/miyagi. 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 Azure-Samples/miyagi?
    pass
    AI named Azure-Samples/miyagi explicitly

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

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

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

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