Does this skill support automatic index updates?

Yes, during setup you can enable a PostToolUse hook that automatically updates the search index whenever you modify or create files in your Secondbrain.

How large are the embedding models used for search?

The first time you run the indexer, it will download approximately 1.5GB of embedding models (like Gemma and Qwen) to handle semantic processing.

Can I force a complete rebuild of my search index?

Yes, you can run the skill with the --rebuild flag or use the 'qmd index --rebuild' command to refresh the entire search database.

What are the prerequisites for using this skill?

You need an existing Secondbrain project (containing .claude/data/config.yaml) and the qmd engine installed via Bun or npm.

What is the qmd tool used for in this skill?

qmd is a high-performance command-line utility that creates and queries semantic search indexes for local markdown documents using embedding models.

Secondbrain Semantic Search Setup

Name: Secondbrain Semantic Search Setup
Author: sergio-bershadsky

bysergio-bershadsky

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コンテンツ管理

Configures and initializes high-performance semantic search for Secondbrain knowledge bases using the qmd engine.

概要

This skill automates the complex setup process for enabling semantic search within a Secondbrain project. It streamlines the installation of the qmd engine, generates optimized JSON configurations, and manages the initial indexing of documents using advanced embedding models. By integrating automatic re-indexing hooks, it ensures that your knowledge base remains searchable via natural language queries, allowing you to find relevant information across hundreds of documents instantly without manual index maintenance.

主な機能

Smart configuration generation for document indexing
High-performance semantic index building with embedding models
Automatic PostToolUse hooks for real-time index updates
4 GitHub stars
Built-in troubleshooting for model downloads and index corruption
Automated qmd environment validation and setup

ユースケース

Adding natural language search capabilities to a personal knowledge base
Initializing a local RAG system for technical documentation
Enabling automatic indexing for frequently updated project notes

概要

主な機能

Smart configuration generation for document indexing
High-performance semantic index building with embedding models
Automatic PostToolUse hooks for real-time index updates
4 GitHub stars
Built-in troubleshooting for model downloads and index corruption
Automated qmd environment validation and setup

ユースケース

Adding natural language search capabilities to a personal knowledge base
Initializing a local RAG system for technical documentation
Enabling automatic indexing for frequently updated project notes