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MichaelYagi
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kb_semantic_search

Perform embedding-based semantic search across knowledge_base entries.

Updated Aug 27, 2026
Search Tags:#embeddings#retrieval#NLP#similarity#semantic-search#knowledge-base#information-retrieval
One-click install
npx skills add https://github.com/MichaelYagi/mcp_a2a --skill kb-semantic-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kb_semantic_search
Source: https://github.com/MichaelYagi/mcp_a2a/tree/main/servers/knowledge_base/skills/kb_semantic_search
Command: npx skills add https://github.com/MichaelYagi/mcp_a2a --skill kb-semantic-search

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables fast retrieval of conceptually related knowledge from the knowledge base by measuring semantic similarity between queries and entries.

Core Features & Use Cases

  • Embedding-based semantic search across knowledge_base articles to surface conceptually related content.
  • Similarity-based retrieval to identify related ideas, topics, or documents given a user query.
  • Use Case: When asked for related content like "show me entries on model deployment strategies," the skill returns the most relevant knowledge_base entries.

Quick Start

Use this skill to search for conceptually related knowledge by providing a natural language query, such as "show me entries related to reinforcement learning in our KB."

Frequently Asked Questions about kb_semantic_search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does semantic search retrieve conceptually related knowledge base entries?▼

Semantic search retrieves conceptually related knowledge base entries by generating embeddings for your query and comparing them against stored entry embeddings to measure semantic similarity. This surfaces content matching underlying concepts rather than exact keywords.

What is the best way to find related topics in a knowledge base using natural language?▼

The best way to find related topics is using embedding-based semantic similarity search with natural language queries. You provide a conceptual phrase like "model deployment strategies" and the skill returns the most relevant knowledge base entries based on meaning.

Can I use semantic similarity search to find documents about ideas instead of exact keywords?▼

Yes, semantic similarity search finds documents about ideas instead of exact keywords. It uses NLP embeddings to compute conceptual relationships, returning knowledge base entries that share topic-level meaning with your query.

When do I need embedding-based information retrieval for my knowledge base?▼

You need embedding-based information retrieval when your knowledge base requires concept-level matching rather than keyword matching. It is ideal for surfacing related ideas, topics, or documents when users query with natural language phrases.

Does semantic search require any external dependencies or APIs to compute embeddings?▼

No external dependencies are required. The skill operates standalone without listed dependencies, handling embedding infrastructure and similarity computation internally to deliver semantic search results across your knowledge base.

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