skills.rest
MacPhobos
MacPhobosCOMMUNITY
Explore all skills by this author →
GitHub

Vector Search Workflows (MCP Vector Search)

Index codebases into ChromaDB and perform semantic searches via mcp-vector-search.

Updated Jan 30, 2026
Search Tags:#semantic search#codebase analysis#vector search#MCP integration#code indexing#ChromaDB
One-click install
npx skills add https://github.com/MacPhobos/research-mind --skill vector-search-workflows-mcp-vector-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Vector Search Workflows (MCP Vector Search)
Source: https://github.com/MacPhobos/research-mind/tree/main/.claude/skills/toolchains-ai-techniques-vector-search-workflows
Command: npx skills add https://github.com/MacPhobos/research-mind --skill vector-search-workflows-mcp-vector-search

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of indexing codebases into a vector database and performing semantic searches, making it easier to find relevant code snippets and understand project structure.

Core Features & Use Cases

  • Automated Indexing: Indexes codebases into ChromaDB using semantic embeddings.
  • Semantic Search: Enables searching code based on natural language queries.
  • MCP Integration: Integrates with MCP (e.g., Claude Code, Cursor) for enhanced developer workflows.
  • Use Case: Quickly find all instances of "error handling patterns" within a large Python project by using a semantic search query.

Quick Start

Use mcp-vector-search to set up the indexing and search capabilities for your current project.

Frequently Asked Questions about Vector Search Workflows (MCP Vector Search)

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

FAQPage Schema
How do I perform semantic search on a codebase to find error handling patterns?▼

You can index codebases into ChromaDB to perform semantic search using natural language queries. This enables rapid discovery of relevant code snippets and helps analyze project structure without relying on exact keyword matching.

What is the best way to index a large Python project for code discovery?▼

Automated indexing into ChromaDB using semantic embeddings is the best way to index a large Python project. This process maps your codebase for semantic search, streamlining code discovery and structural analysis.

Does MCP integration support semantic search within developer tools like Claude Code?▼

Yes, MCP integration supports semantic search within developer tools like Claude Code and Cursor. This Skill facilitates indexing and search capabilities directly in your workflow, enhancing code discovery for software engineering projects.

Can I use natural language queries to search for code snippets in my current project?▼

Yes, you can use natural language queries to search for code snippets after indexing your codebase. This Skill sets up the indexing and search capabilities using mcp-vector-search, allowing you to find relevant code based on meaning rather than exact matches.

When do I need to reindex a codebase in ChromaDB?▼

You need to reindex a codebase in ChromaDB when your project structure changes or new code is added. This Skill supports automated reindexing to ensure your semantic search queries return accurate and up-to-date results from the vector database.

Related Skills

Explore More Software Engineering Skills→
bobmatnycbobmatnyc
68

vector-search-workflows

Index and query codebases with vector embeddings for semantic search.

Community
Intermediate
roach88roach88
2

mgrep

Search code and web documentation using natural language intent queries.

Community
Intermediate
korchasakorchasa

reflect-by-history

Analyze past IDE session history to identify recurring patterns and propose improvements.

Community
Advanced
shinnytechshinnytech

Remembering Conversations

Index and search historical Claude Code conversations with semantic and exact text matching.

Official
Advanced