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Run npx skillmds@latest add arthurgailes/r-ragnar 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.

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Is R Ragnar free to use?

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Who published R Ragnar?

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

R Ragnar by arthurgailes — Agent Skill

Name: R Ragnar
Author: arthurgailes

File contents

ragnar: Retrieval Augmented Generation in R

Overview

ragnar implements RAG in R. Create vector stores, embed documents, register retrieval tools with ellmer chat sessions. LLMs search your documents before answering.

Install: install.packages("ragnar")

References

Read references/API.md before writing code.

references/API.md - Complete function reference
references/package-docs.md - Vector store setup and usage
references/rag.md - RAG patterns and ellmer integration

When to Use

LLM needs to search your documentation
Implement semantic search over documents
Create vector database from documents
RAG workflows in R

When NOT to Use

Just need keyword search (use grep/Grep tool)
Documents fit in single prompt (<100k tokens)
Building chatbot without document search (use r-ellmer only)

Quick Reference

library(ragnar)

# Create store
docs <- read_as_markdown("docs/")
chunks <- markdown_chunk(docs)
store <- ragnar_store_create("docs.duckdb")
ragnar_store_insert(store, chunks)

# Register with ellmer
library(ellmer)
chat <- chat_openai()
ragnar_register_tool_retrieve(chat, store)

# Now chat searches docs before answering
chat$chat("What does the documentation say about...?")

# Local embeddings (Ollama)
store <- ragnar_store_create("local.duckdb",
  embed = ragnar_embed_ollama(model = "nomic-embed-text"))

Common Mistakes

Issue	Solution
Embedding mismatch	Store and retrieval must use same provider
Store not registered	Call `ragnar_register_tool_retrieve(chat, store)`
Ollama embeddings not working	Start `ollama serve` first
Poor retrieval quality	Check chunking strategy, embedding model

Core Functions

Store Management:

ragnar_store_create(): Create vector database
ragnar_store_connect(): Connect to existing store
ragnar_store_insert(): Add documents

Document Processing:

read_as_markdown(): Read documents
markdown_chunk(): Split into chunks

Integration:

ragnar_register_tool_retrieve(): Register with ellmer
ragnar_embed_ollama(): Local embeddings

Advanced

See references/ for:

API.md: Complete function reference
rag.md: RAG workflow details
Package docs: Full package documentation

Integration

With ellmer: ragnar_register_tool_retrieve(chat, store) With vitals (evaluation): See r-vitals skill for RAG testing Cross-package patterns: See r-ai meta-skill

---name: r-ragnardescription: Use when code loads or uses ragnar (library(ragnar), ragnar::), implementing RAG in R, creating vector stores, embedding documents for semantic search, or building retrieval pipelines---# ragnar: Retrieval Augmented Generation in R## Overview**ragnar implements RAG in R.** Create vector stores, embed documents, register retrieval tools with ellmer chat sessions. LLMs search your documents before answering.**Install:** `install.packages("ragnar")`## ReferencesRead `references/API.md` before writing code.- `references/API.md` - Complete function reference- `references/package-docs.md` - Vector store setup and usage- `references/rag.md` - RAG patterns and ellmer integration## When to Use- LLM needs to search your documentation- Implement semantic search over documents- Create vector database from documents- RAG workflows in R## When NOT to Use- Just need keyword search (use grep/Grep tool)- Documents fit in single prompt (<100k tokens)- Building chatbot without document search (use r-ellmer only)## Quick Reference```rlibrary(ragnar)# Create storedocs <- read_as_markdown("docs/")chunks <- markdown_chunk(docs)store <- ragnar_store_create("docs.duckdb")ragnar_store_insert(store, chunks)# Register with ellmerlibrary(ellmer)chat <- chat_openai()ragnar_register_tool_retrieve(chat, store)# Now chat searches docs before answeringchat$chat("What does the documentation say about...?")# Local embeddings (Ollama)store <- ragnar_store_create("local.duckdb", embed = ragnar_embed_ollama(model = "nomic-embed-text"))```## Common Mistakes| Issue | Solution ||-------|----------|| Embedding mismatch | Store and retrieval must use same provider || Store not registered | Call `ragnar_register_tool_retrieve(chat, store)` || Ollama embeddings not working | Start `ollama serve` first || Poor retrieval quality | Check chunking strategy, embedding model |## Core Functions**Store Management:**- `ragnar_store_create()`: Create vector database- `ragnar_store_connect()`: Connect to existing store- `ragnar_store_insert()`: Add documents**Document Processing:**- `read_as_markdown()`: Read documents- `markdown_chunk()`: Split into chunks**Integration:**- `ragnar_register_tool_retrieve()`: Register with ellmer- `ragnar_embed_ollama()`: Local embeddings## AdvancedSee `references/` for:- **API.md**: Complete function reference- **rag.md**: RAG workflow details- **Package docs**: Full package documentation## Integration**With ellmer:** `ragnar_register_tool_retrieve(chat, store)`**With vitals (evaluation):** See r-vitals skill for RAG testing**Cross-package patterns:** See r-ai meta-skill

Frequently asked questions

R Ragnar