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bobmatnyc
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vector-search-workflows

Index and query codebases with vector embeddings for semantic search.

68|19|Updated Nov 21, 2025
Search Tags:#semantic search#embeddings#mcp#vector search#code indexing#chromadb
One-click install
npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill vector-search-workflows
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vector-search-workflows
Source: https://github.com/bobmatnyc/claude-mpm-skills/tree/main/toolchains/ai/techniques/vector-search-workflows
Command: npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill vector-search-workflows

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mcp-vector-search, and includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of indexing codebases for semantic search and managing the freshness of this index, enabling efficient code exploration and retrieval.

Core Features & Use Cases

  • Codebase Indexing: Creates vector embeddings for code files, allowing for semantic search.
  • Automated Reindexing: Implements strategies to automatically update the index when code changes.
  • MCP Integration: Seamlessly integrates with the MCP ecosystem for broader toolchain utilization.

Quick Start

Use mcp-vector-search to set up indexing and search for authentication logic in the codebase.

Frequently Asked Questions about vector-search-workflows

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

FAQPage Schema
How do I set up semantic search for my codebase using vector embeddings?▼

To set up semantic search for your codebase, you use the mcp-vector-search framework to generate vector embeddings for code files, enabling efficient code exploration and retrieval via ChromaDB.

What is vector search for code indexing and how does it work?▼

Vector search for code indexing works by creating vector embeddings for code files, allowing you to query the codebase semantically rather than relying on exact string matches to find relevant logic.

Can I automatically reindex my codebase when code changes?▼

Yes, you can automatically reindex your codebase when code changes by implementing automated reindexing strategies within the MCP Vector Search framework to manage index freshness.

Do I need ChromaDB and mcp-vector-search installed to use this workflow?▼

Yes, you need to install mcp-vector-search and configure ChromaDB and MCP integrations to enable indexing and querying of codebases using vector embeddings.

What's the best way to find authentication logic in a codebase using semantic search?▼

The best way to find authentication logic is by indexing your codebase with mcp-vector-search, which allows you to query for semantically related code files instead of searching by keyword.

Does mcp-vector-search integrate with the broader MCP ecosystem?▼

Yes, mcp-vector-search integrates seamlessly with the MCP ecosystem, allowing you to utilize vector search for codebases within a broader toolchain for enhanced code exploration.

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