Vector Search Workflows
Skill Verified ActiveVector search indexing and querying workflows using MCP Vector Search, including setup, reindexing, auto-index strategies, and MCP integration.
To streamline and manage vector search indexing and querying workflows for codebases, enabling efficient semantic search capabilities.
Features
- Vector search indexing and setup
- Automatic index management and reindexing
- Semantic search querying
- MCP integration for development environments
- Troubleshooting and status checks
Use Cases
- Building semantic search for codebases
- Setting up MCP search tools
- Troubleshooting indexing and reindexing workflows
- Keeping code search data fresh with auto-indexing
Non-Goals
- Managing vector databases other than those supported by mcp-vector-search
- Providing a GUI for search operations
- Complex natural language query understanding beyond semantic search
Installation
npx skills add bobmatnyc/claude-mpm-skillsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedSimilar Extensions
Embeddings
99Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Setup
100Use first for install/update routing — sends setup, doctor, or MCP requests to the correct OMC setup flow
Mcp Setup
100Configure popular MCP servers for enhanced agent capabilities
Context7 Cli
100Use the ctx7 CLI to fetch library documentation, manage AI coding skills, and configure Context7 MCP. Activate when the user mentions "ctx7" or "context7", needs current docs for any library, wants to install/search/generate skills, or needs to set up Context7 for their AI coding agent.
Treat
100Prune bloated session with a prescription. Removes progress ticks, stale reads, duplicate content, and more.
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.