RRepoGEO

REPOGEO REPORT · LITE

shuyu-labs/AntSK

Default branch main · commit 5e032e37 · scanned 5/14/2026, 6:53:29 PM

GitHub: 1,318 stars · 211 forks

AI VISIBILITY SCORE
33 /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
2 / 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 shuyu-labs/AntSK, 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 statement to clearly define the project type

    Why:

    CURRENT
    ## 使用.Net9 + Blazor+SemanticKernel 打造的AI知识库/智能体
AntSK 是一个基于 .NET 9 和 Blazor 技术栈构建的企业级AI知识库和智能体平台，集成了 Semantic Kernel 和 Kernel Memory，提供完整的AI应用开发解决方案。
    COPY-PASTE FIX
    ## AntSK: Your Full-Stack, Offline-Capable AI Knowledge Base & Agent Platform
AntSK is an enterprise-grade AI knowledge base and agent platform built with .NET 9 and Blazor, integrating Semantic Kernel and Kernel Memory. It provides a complete, out-of-the-box solution for AI applications, emphasizing local offline AI large model support and full-stack capabilities.
  • mediumtopics#2
    Update and expand repository topics for better categorization

    Why:

    CURRENT
    agnet, ai, dotnet, rag, sk
    COPY-PASTE FIX
    ai, dotnet, rag, llm-agent, knowledge-base, offline-ai, semantic-kernel, kernel-memory, blazor, aspire, full-stack
  • mediumreadme#3
    Clarify the existing license(s) in the README

    Why:

    CURRENT
    ## 🚨 使用协议
    COPY-PASTE FIX
    ## 🚨 使用协议
This project is licensed under [Specify License Name(s) here, e.g., 'a custom license combining Apache 2.0 and MIT terms']. Please refer to the 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 shuyu-labs/AntSK
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/semantic-kernel
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/semantic-kernel · recommended 1×
  2. SciSharp/LLamaSharp · recommended 1×
  3. ollama/ollama · recommended 1×
  4. mbdavid/LiteDB · recommended 1×
  5. facebookresearch/faiss · recommended 1×
  • CATEGORY QUERY
    How to build an offline AI knowledge base using .NET with local large language models?
    you: not recommended
    AI recommended (in order):
    1. Semantic Kernel (microsoft/semantic-kernel)
    2. LLamaSharp (SciSharp/LLamaSharp)
    3. Ollama (ollama/ollama)
    4. LiteDB (mbdavid/LiteDB)
    5. Faiss (facebookresearch/faiss)
    6. FaissSharp (SciSharp/FaissSharp)
    7. SentenceTransformers (UKPLab/sentence-transformers)
    8. ONNX Runtime (microsoft/onnxruntime)
    9. SQL Server Compact
    10. SQLite
    11. Microsoft.Data.Sqlite

    AI recommended 11 alternatives but never named shuyu-labs/AntSK. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a .NET framework for RAG applications, processing various documents with local LLMs.
    you: not recommended
    AI recommended (in order):
    1. Semantic Kernel
    2. LLamaSharp
    3. LangChain for .NET
    4. Ollama
    5. Microsoft.ML.OnnxRuntime
    6. Pinecone.NET
    7. Weaviate.NET
    8. Faiss.NET
    9. PdfPig
    10. NPOI
    11. iTextSharp

    AI recommended 11 alternatives but never named shuyu-labs/AntSK. 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 shuyu-labs/AntSK?
    pass
    AI did not name shuyu-labs/AntSK — 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 shuyu-labs/AntSK in production, what risks or prerequisites should they evaluate first?
    pass
    AI named shuyu-labs/AntSK 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 shuyu-labs/AntSK solve, and who is the primary audience?
    pass
    AI named shuyu-labs/AntSK explicitly

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

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shuyu-labs/AntSK — 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