r-ai

# 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 ```r library(ellmer) library(btw) chat <- chat_openai() chat$set_tools(btw_tools()) ``` ### Chat + RAG ```r library(ellmer) library(ragnar) chat <- chat_openai() ragnar_register_tool_retrieve(chat, store) ``` ### Agent Access to R ```r library(mcptools) mcp_session() # In console, each startup # Now Claude Code can run R code ``` ### Fully Local (Ollama) ```r library(ellmer) chat <- chat_ollama(model = "llama3.2") library(ragnar) store <- ragnar_store_create("local.duckdb", embed = ragnar_embed_ollama(model = "nomic-embed-text")) ``` ### RAG Evaluation ```r library(vitals) library(ellmer) library(ragnar) chat <- chat_openai() ragnar_register_tool_retrieve(chat, store) task <- Task$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.