How do I install the RAG CLI skill?

Run npx skillmds@latest add tomevault-io/rag-cli 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 CLI safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with RAG CLI?

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 CLI free to use?

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

Who published RAG CLI?

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

RAG CLI by tomevault-io — Agent Skill

Name: RAG CLI
Author: tomevault-io

RAG CLI

This skill enables RAG (Retrieval-Augmented Generation) queries against your locally indexed documents. It uses semantic search to find relevant documents and generates answers using Claude Haiku. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

RAG Retrieval Skill

Query your local document knowledge base using semantic search and get AI-powered answers.

Overview

This skill enables RAG (Retrieval-Augmented Generation) queries against your locally indexed documents. It uses semantic search to find relevant documents and generates answers using Claude Haiku.

Usage

/skill rag-retrieval "How to configure the API?"

Features

Semantic Search: Uses vector similarity to find relevant documents
Hybrid Retrieval: Combines vector search with keyword matching for better accuracy
Context-Aware Answers: Uses claude-haiku-4-5-20251001 to generate responses
Citation Support: Shows sources for generated answers
Performance Monitoring: Tracks query latency and accuracy

Arguments

query (required): Your question or search query
--top-k (optional): Number of documents to retrieve (default: 5)
--threshold (optional): Minimum similarity score (default: 0.7)
--mode (optional): Search mode - "hybrid", "vector", or "keyword" (default: "hybrid")

Examples

Basic Query

/skill rag-retrieval "What is the authentication process?"

Retrieve More Context

/skill rag-retrieval "How to handle errors?" --top-k 10

Vector-Only Search

/skill rag-retrieval "API rate limits" --mode vector

Configuration

The skill uses the following configuration from config/default.yaml:

retrieval.top_k: Default number of documents to retrieve
retrieval.hybrid_ratio: Balance between vector and keyword search (0.7 = 70% vector)
claude.model: LLM model for response generation
claude.max_tokens: Maximum response length

Performance

Typical latencies:

Vector search: <100ms
End-to-end response: <5 seconds
Indexing: ~0.5s per 100 documents

Requirements

Indexed documents in data/vectors/
Valid Anthropic API key in environment
At least 2GB RAM for vector operations

Troubleshooting

No Results Found

Ensure documents are indexed: python scripts/index.py --input data/documents
Lower the similarity threshold: --threshold 0.5
Try keyword mode if vector search fails

Slow Responses

Reduce top_k value for faster retrieval
Check if vector index is optimized for your document count
Monitor memory usage with python -m src.monitoring.tcp_server

API Errors

Verify ANTHROPIC_API_KEY environment variable
Check API rate limits and quota
Review logs in logs/rag_cli.log

Source: ItMeDiaTech/rag-cli — distributed by TomeVault.

---name: rag-clidescription: This skill enables RAG (Retrieval-Augmented Generation) queries against your locally indexed documents. It uses semantic search to find relevant documents and generates answers using Claude Haiku. Use when this capability is needed.---# RAG Retrieval SkillQuery your local document knowledge base using semantic search and get AI-powered answers.## OverviewThis skill enables RAG (Retrieval-Augmented Generation) queries against your locally indexed documents. It uses semantic search to find relevant documents and generates answers using Claude Haiku.## Usage```/skill rag-retrieval "How to configure the API?"```## Features- **Semantic Search**: Uses vector similarity to find relevant documents- **Hybrid Retrieval**: Combines vector search with keyword matching for better accuracy- **Context-Aware Answers**: Uses claude-haiku-4-5-20251001 to generate responses- **Citation Support**: Shows sources for generated answers- **Performance Monitoring**: Tracks query latency and accuracy## Arguments- `query` (required): Your question or search query- `--top-k` (optional): Number of documents to retrieve (default: 5)- `--threshold` (optional): Minimum similarity score (default: 0.7)- `--mode` (optional): Search mode - "hybrid", "vector", or "keyword" (default: "hybrid")## Examples### Basic Query```/skill rag-retrieval "What is the authentication process?"```### Retrieve More Context```/skill rag-retrieval "How to handle errors?" --top-k 10```### Vector-Only Search```/skill rag-retrieval "API rate limits" --mode vector```## ConfigurationThe skill uses the following configuration from `config/default.yaml`:- `retrieval.top_k`: Default number of documents to retrieve- `retrieval.hybrid_ratio`: Balance between vector and keyword search (0.7 = 70% vector)- `claude.model`: LLM model for response generation- `claude.max_tokens`: Maximum response length## PerformanceTypical latencies:- Vector search: <100ms- End-to-end response: <5 seconds- Indexing: ~0.5s per 100 documents## Requirements- Indexed documents in `data/vectors/`- Valid Anthropic API key in environment- At least 2GB RAM for vector operations## Troubleshooting### No Results Found- Ensure documents are indexed: `python scripts/index.py --input data/documents`- Lower the similarity threshold: `--threshold 0.5`- Try keyword mode if vector search fails### Slow Responses- Reduce top_k value for faster retrieval- Check if vector index is optimized for your document count- Monitor memory usage with `python -m src.monitoring.tcp_server`### API Errors- Verify ANTHROPIC_API_KEY environment variable- Check API rate limits and quota- Review logs in `logs/rag_cli.log`---> Source: [ItMeDiaTech/rag-cli](https://github.com/ItMeDiaTech/rag-cli) — distributed by [TomeVault](https://tomevault.io).

Frequently asked questions