arthurgailes R AI Ecosystem (Meta-Skill)
Overview
This is a meta-skill that helps you choose the right R AI package. The R AI stack consists of 5 specialized packages, each with its own skill.
Install all: install.packages(c("ellmer", "btw", "mcptools", "ragnar", "vitals"))
API keys: Set OPENAI_API_KEY or ANTHROPIC_API_KEY in .Renviron
References
references/integration-patterns.md- How packages work together in workflows
For per-package API docs, invoke the corresponding package skill (/r-ellmer, /r-btw, /r-mcptools, /r-ragnar, /r-vitals).
When to Use Which Package
| Package | Use When | Skill |
|---|---|---|
| ellmer | Chat with any LLM from R | /r-ellmer |
| btw | Provide R context (docs, data) to LLMs | /r-btw |
| mcptools | Let agents (Claude Code) run R code | /r-mcptools |
| ragnar | LLM searches your documents (RAG) | /r-ragnar |
| vitals | Test LLM output quality | /r-vitals |
Navigation: When you know which package you need, invoke the specific skill above.
When NOT to Use
- Single one-off API call (use httr2)
- Non-chat ML workflows (use tidymodels)
- Production REST APIs (use plumber)
Key Distinctions
btw vs mcptools:
- btw = Give tools TO ellmer (R → LLM): Chat can read R docs/data
- mcptools = Let agents INTO R (LLM → R): Agents can run R code
ellmer vs ragnar:
- ellmer = Direct chat, limited by context window
- ragnar = Chat searches documents first (RAG)
Common Integration Patterns
Chat + Context Tools
library(ellmer)
library(btw)
chat <- chat_openai()
chat$set_tools(btw_tools())
Chat + RAG
library(ellmer)
library(ragnar)
chat <- chat_openai()
ragnar_register_tool_retrieve(chat, store)
Agent Access to R
library(mcptools)
mcp_session() # In console, each startup
# Now Claude Code can run R code
Fully Local (Ollama)
library(ellmer)
chat <- chat_ollama(model = "llama3.2")
library(ragnar)
store <- ragnar_store_create("local.duckdb",
embed = ragnar_embed_ollama(model = "nomic-embed-text"))
RAG Evaluation
library(vitals)
library(ellmer)
library(ragnar)
chat <- chat_openai()
ragnar_register_tool_retrieve(chat, store)
task <- Task$new(
dataset = test_cases,
solver = function(input) chat$chat(input, echo = "none"),
scorer = model_graded_qa()
)
task$run()
Advanced
See references/ for:
- integration-patterns.md: Full cross-package integration examples
For package-specific documentation, invoke the individual package skills above.