Vector Search Workflows

Name: Vector Search Workflows
Author: bobmatnyc

bobmatnyc/claude-mpm-skills

214 installs
63 repo stars
Updated July 18, 2026
bobmatnyc/claude-mpm-skills

Design embedding pipelines, vector indexes, retrieval queries, and RAG orchestration that connect LLM apps to semantic search over documents and structured knowledge.

About

Guides end-to-end vector-search workflows for AI products: document chunking, embedding generation, index configuration, similarity and hybrid queries, RAG assembly, and operational tuning for accurate low-latency retrieval in agent and SaaS apps.

Chunking and embedding ingestion pipelines
Vector index selection and tuning
Hybrid keyword plus semantic retrieval
RAG prompt assembly and citation flow
Evaluation of recall, latency, and cost

Vector Search Workflows by the numbers

214 all-time installs (skills.sh)
Ranked #2,761 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
Data as of Aug 1, 2026 (Skillselion catalog sync)

npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill vector-search-workflows

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Installs	214
repo stars	★ 63
Last updated	July 18, 2026
Repository	bobmatnyc/claude-mpm-skills ↗

What it does

Design embedding pipelines, vector indexes, retrieval queries, and RAG orchestration that connect LLM apps to semantic search over documents and structured knowledge.

Files

SKILL.mdMarkdownGitHub ↗

Vector Search Workflows (MCP Vector Search)

Overview

Use mcp-vector-search to index codebases into ChromaDB and search via semantic embeddings. The recommended flow is setup (init + index + MCP integration), then search, and use index or auto-index to keep data fresh.

Quick Start

pip install mcp-vector-search
mcp-vector-search setup
mcp-vector-search search "authentication logic"

setup detects languages, initializes config, indexes the repo, and configures MCP integrations (Claude Code, Cursor, etc.).

Core Commands

Indexing

mcp-vector-search index
mcp-vector-search index --force
mcp-vector-search index reindex --all --force
mcp-vector-search index reindex path/to/file.py

Auto-Index Strategies

mcp-vector-search auto-index setup --method all
mcp-vector-search auto-index status
mcp-vector-search auto-index check --auto-reindex --max-files 10
mcp-vector-search auto-index teardown --method all

Search

mcp-vector-search search "error handling patterns"
mcp-vector-search search "vector store initialization"

Status + Doctor

mcp-vector-search status
mcp-vector-search doctor

MCP Integration Pattern

setup uses native claude mcp add when available, otherwise falls back to .mcp.json.

Typical .mcp.json entry:

{
  "mcpServers": {
    "mcp-vector-search": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "mcp-vector-search", "mcp"],
      "env": {
        "MCP_ENABLE_FILE_WATCHING": "true"
      }
    }
  }
}

Reindex Triggers

Dependency updates or parser changes
Large refactors
Adding new languages or file extensions
Tool upgrades (version tracking triggers reindex)

Local Patterns

Use uv for dev installs: uv sync --dev
Use setup --force to rebuild config + index after tool upgrades
Keep file watching on via MCP_ENABLE_FILE_WATCHING=true

Related Skills

toolchains/ai/protocols/mcp
universal/main/mcp-builder

{
  "name": "vector-search-workflows",
  "version": "1.0.0",
  "category": "toolchain",
  "toolchain": "ai",
  "framework": "vector-search-workflows",
  "tags": [
    "vector-search",
    "embeddings",
    "indexing",
    "search",
    "mcp"
  ],
  "entry_point_tokens": 118,
  "full_tokens": 700,
  "related_skills": [
    "../../protocols/mcp",
    "../../../universal/main/mcp-builder"
  ],
  "author": "Claude MPM Team",
  "license": "MIT",
  "updated": "2025-12-31",
  "source_path": "toolchains/ai/techniques/vector-search-workflows/SKILL.md",
  "repository": "https://github.com/bobmatnyc/claude-mpm-skills"
}