How do I install the RAG Search skill?

Run npx skillmds@latest add etalab-ia/rag-search in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is RAG Search safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with RAG Search?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is RAG Search free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published RAG Search?

etalab-ia (@etalab-ia) published this skill. Their other Agent Skills are listed on their SkillMD profile.

RAG Search by etalab-ia — Agent Skill

Name: RAG Search
Author: etalab-ia

File contents

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:

Run the search query
Parse JSON results
Present top results with scores
Optionally read full documents for deeper context

Troubleshooting

If no results:

Check collection exists: qmd status
Verify embeddings generated: qmd embed
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

---name: rag-searchdescription: Rechercher dans une base de connaissances indexée. Utiliser quand l'utilisateur veut trouver des documents, effectuer une recherche sémantique, ou interroger un index de documents.---# rag-search SkillSearch 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` firstVerify setup:```bashqmd status```## Workflow### 1. Verify Knowledge Base```bashqmd status```Should show your collection(s) with document counts.### 2. Run Search```bashqmd query "<query>" --json```Examples:```bashqmd query "authentication flow" --jsonqmd query "API design patterns" --jsonqmd query "deployment process" --json```### 3. Present ResultsParse 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```bash# Basic searchqmd query "authentication" --json# Limit resultsqmd query "API design" --limit 10 --json# Search specific collectionqmd query "deployment" --collection api-docs --json# Vector search with scoresqmd vsearch "configuration" --json```## Output FormatJSON output structure:```json{ "results": [ { "path": "docs/guide.md", "score": 0.89, "content": "..." } ]}```## Integration with AgentsWhen using this skill:1. Run the search query2. Parse JSON results3. Present top results with scores4. Optionally read full documents for deeper context## TroubleshootingIf 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

Frequently asked questions

RAG Search