Vector Search Workflows (MCP Vector Search)
CommunityIndex and search codebases semantically.
Software Engineering#semantic search#codebase analysis#vector search#MCP integration#code indexing#ChromaDB
AuthorMacPhobos
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Vector Search Workflows (MCP Vector Search) Download link: https://github.com/MacPhobos/research-mind/archive/main.zip#vector-search-workflows-mcp-vector-search Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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