RRepoGEO

REPOGEO REPORT · LITE

Nasiko-Labs/nasiko

Default branch main · commit 6c988651 · scanned 6/21/2026, 1:46:48 AM

GitHub: 2,302 stars · 194 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
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 for AI agent orchestration and control plane

    Why:

    COPY-PASTE FIX
    ai-agents, agent-orchestration, control-plane, ai-platform, agent-management, langchain, microservices, fastapi, docker, kubernetes
  • highreadme#2
    Clarify the README's opening to prevent mis-categorization

    Why:

    CURRENT
    Nasiko is a developer control plane that transforms how you build, deploy, and manage AI agents at scale.
    COPY-PASTE FIX
    Nasiko is an open-source developer control plane and orchestration platform that transforms how you build, deploy, and manage AI agents at scale.
  • mediumlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [SPECIFY LICENSE(S) HERE]. Refer to the [LICENSE](LICENSE) file for full details.

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
Hugging Face Inference Endpoints
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Inference Endpoints · recommended 2×
  2. Microsoft Azure AI Studio · recommended 1×
  3. Azure Machine Learning · recommended 1×
  4. Google Cloud Vertex AI Agent Builder · recommended 1×
  5. AWS Bedrock · recommended 1×
  • CATEGORY QUERY
    What platforms offer centralized management and orchestration for multiple AI agents?
    you: not recommended
    AI recommended (in order):
    1. Microsoft Azure AI Studio
    2. Azure Machine Learning
    3. Google Cloud Vertex AI Agent Builder
    4. AWS Bedrock
    5. AWS Step Functions
    6. LangChain (langchain-ai/langchain)
    7. MLflow (mlflow/mlflow)
    8. OpenAI Assistants API
    9. Hugging Face Inference Endpoints
    10. Hugging Face Spaces

    AI recommended 10 alternatives but never named Nasiko-Labs/nasiko. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I deploy, route, and scale my AI agents using a unified system?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. KubeFlow (kubeflow/kubeflow)
    3. OpenShift
    4. AWS SageMaker Endpoints
    5. Azure Machine Learning Endpoints
    6. Google Cloud Vertex AI Endpoints
    7. Hugging Face Inference Endpoints
    8. Ray Serve (ray-project/ray)

    AI recommended 8 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?

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite