RRepoGEO

REPOGEO REPORT · LITE

Nasiko-Labs/nasiko

Default branch main · commit 6c988651 · scanned 5/11/2026, 12:02:05 AM

GitHub: 1,691 stars · 153 forks

AI VISIBILITY SCORE
35 /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
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 Nasiko-Labs/nasiko, 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
  • hightopics#1
    Add specific topics to the repository

    Why:

    COPY-PASTE FIX
    ai-agents, agent-orchestration, control-plane, microservices, langchain, kubernetes, api-gateway, agent-lifecycle-management, intelligent-routing, observability
  • mediumreadme#2
    Add a clear statement about the project's license to the README

    Why:

    COPY-PASTE FIX
    ## ⚖️ License
This project is licensed under the Apache-2.0 License. See the [LICENSE](LICENSE) file for details.
  • lowhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://nasiko.dev

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 Nasiko-Labs/nasiko
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure Machine Learning
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure Machine Learning · recommended 2×
  2. kubernetes/kubernetes · recommended 2×
  3. mlflow/mlflow · recommended 2×
  4. tiangolo/fastapi · recommended 2×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    How can I centrally manage and deploy multiple AI agents with intelligent routing?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LangServe (langchain-ai/langserve)
    3. LangSmith (langchain-ai/langsmith)
    4. Microsoft Azure AI Studio
    5. Azure Machine Learning
    6. Azure AI Search
    7. AWS Step Functions
    8. AWS Lambda
    9. Amazon API Gateway
    10. Kubernetes (kubernetes/kubernetes)
    11. KServe (kserve/kserve)
    12. MLflow (mlflow/mlflow)
    13. Haystack (deepset-ai/haystack)
    14. FastAPI (tiangolo/fastapi)
    15. Streamlit (streamlit/streamlit)
    16. LlamaIndex (run-llama/llama_index)
    17. FastAPI (tiangolo/fastapi)

    AI recommended 17 alternatives but never named Nasiko-Labs/nasiko. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help orchestrate and automate the deployment lifecycle for AI agent services?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. MLflow (mlflow/mlflow)
    3. AWS SageMaker
    4. Azure Machine Learning
    5. Google Cloud Vertex AI
    6. Hugging Face Transformers (huggingface/transformers)
    7. Docker Compose (docker/compose)

    AI recommended 7 alternatives but never named Nasiko-Labs/nasiko. 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 Nasiko-Labs/nasiko?
    pass
    AI named Nasiko-Labs/nasiko explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of Nasiko-Labs/nasiko. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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Nasiko-Labs/nasiko — RepoGEO report