rag-search

name	rag-search
provider	qmd
available-providers	["qmd","pinecone","weaviate"]
description	Search the knowledge base for relevant documents. Use when the user wants to find documents in their indexed corpus, has questions that could be answered by their documents, or needs context from their knowledge base. Triggers on keywords like "search documents", "find in knowledge base", "query index".
license	MIT
allowed-tools	Bash

rag-search Skill

Search the qmd index for relevant documents. This skill uses qmd under the hood.

Prerequisites

• qmd installed: bun install -g @tobilu/qmd
• Collection set up: Use /rag-index first

Verify setup:

qmd status

Workflow

1. Verify Knowledge Base

qmd status

Should show your collection(s) with document counts.

2. Run Search

qmd query "<query>" --json

Examples:

qmd query "authentication flow" --json
qmd query "API design patterns" --json
qmd query "deployment process" --json

3. Present Results

Parse the JSON output and present:

• Document path
• Relevance score
• Relevant excerpt

Arguments

Argument	Type	Default	Description
query	string	required	Search query
mode	string	query	Search mode: query, vsearch, search
limit	int	5	Number of results
collection	string	all	Restrict to specific collection

Search Modes

Mode	Description
query	Semantic search (default)
vsearch	Vector search with scores
search	Hybrid search

Examples

# Basic search
qmd query "authentication" --json

# Limit results
qmd query "API design" --limit 10 --json

# Search specific collection
qmd query "deployment" --collection api-docs --json

# Vector search with scores
qmd vsearch "configuration" --json

Output Format

JSON output structure:

{
  "results": [
    {
      "path": "docs/guide.md",
      "score": 0.89,
      "content": "..."
    }
  ]
}

Integration with Agents

When using this skill:

1. Run the search query
2. Parse JSON results
3. Present top results with scores
4. Optionally read full documents for deeper context

Troubleshooting

If no results:

1. Check collection exists: qmd status
2. Verify embeddings generated: qmd embed
3. Try broader query terms

Provider-Specific Notes

qmd (current)

• Storage: Local SQLite with sqlite-vec extension
• Embeddings: Local model (no API key required)
• Best for: Small to medium corpora, offline usage

pinecone (planned)

• Storage: Pinecone cloud
• Embeddings: OpenAI or custom embeddings
• Best for: Large-scale production deployments

weaviate (planned)

• Storage: Weaviate instance (self-hosted or cloud)
• Embeddings: Configurable
• Best for: Enterprise deployments with hybrid search