# Getting started with chores

Chore helpers are persistent, ergonomic LLM assistants designed to help you complete repetitive, hard-to-automate tasks quickly.

    library(chores)

The chores package ships with a number of pre-engineered “chore helpers.” A chore is a keyword that succinctly describes what the helper is intended to do and serves as an identifier to match the helper with its prompt and interface. A helper’s prompt is just a markdown file with enough context and examples to teach a model to carry out a given task well. A helper’s interface determines whether it replaces, prefixes, or suffixes the selected code. For example:

- The `"testthat"` helper helps you transition your R package’s unit tests to the third edition of testthat. Its prompt shows the model how to convert to snapshot tests, disentangle nested expectations, and transition from deprecated functions. It replaces the selected code.
- The `"roxygen"` helper helps you quickly template out roxygen documentation for a function. Its prompt shows the model how to write high-quality stub `@param` and `@returns` entries that can be later extended by the developer. It prefixes the selected code.

## Choosing a model

The chores addin supports any model supported by ellmer. When choosing a model for use with chores, you’ll want to the use the most performant model possible that satisfies your privacy needs; chores automatically passes along your selected code to your chosen model, so it’s especially important to consider data privacy when using LLMs with chores.

chores uses the `chores.chat` option to configure which model powers the addin. `chores.chat` should be set to an ellmer Chat object. For example, to use Anthropic’s Claude, you might write `options(chores.chat = ellmer::chat_claude())`. Paste that code in your `.Rprofile` via `usethis::edit_r_profile()` to always use the same model every time you start an R session.

If you’re using ellmer inside a organization, you’ll be limited to what your IT department allows, which is likely to be one provided by a big cloud provider, e.g. `chat_azure()`, `chat_bedrock()`, `chat_databricks()`, or `chat_snowflake()`. If you’re using ellmer for your own exploration, you’ll have a lot more freedom, so we have a few recommendations to help you get started:

- As of early 2026, Anthropic’s Claude Haiku 4.5 is a powerful and inexpensive model for code assistance. If you want to use Claude, you’ll need to register an API key at `https://console.anthropic.com/` to the environment variable `ANTHROPIC_API_KEY` and then set `options(chores.chat = ellmer::chat_claude(model = "claude-haiku-4-5"))`.

- Regarding OpenAI’s models, we recommend GPT-4.1, or `model = "gpt-4.1-mini"` for a cheaper, lower-quality model; to use an OpenAI model, you’ll need to set the options `options(chores.chat = ellmer::chat_openai(model = "gpt-4.1"))` and register your OpenAI API key with the `OPENAI_API_KEY` environment variable.

- Google’s Gemini 3 Flash is also a strong option for code assistance. We recommend setting the thinking level to `"minimal"` to reduce latency. Register an API key at `https://aistudio.google.com/apikey` to the environment variable `GOOGLE_API_KEY` (or `GEMINI_API_KEY`) and then configure chores with:

    options(
      chores.chat = ellmer::chat_google_gemini(
        model = "gemini-3-flash-preview",
        api_args = list(
          generationConfig = list(
            thinkingConfig = list(thinkingLevel = "minimal")
          )
        )
      )
    )

- You can use a local model, which allows you to run models on your own computer. Local models don’t share your data and are free to use, though they’re slightly less accurate than the state of the art hosted models. We recommend Qwen3 4B Instruct 2507 for local use with chores; at least with MLX on Mac, it takes up 2.5GB of space and requires 2.5GB of RAM to run.

  On Apple Silicon (Mac M-series), we recommend LM Studio. Click “Discover”, search “Qwen3 4B Instruct 2507”, and click “Download.” Once downloaded, click the “Developer” tab and change the Status from Stopped to Running. Then configure chores with:

    qwen3_4b <- ellmer::chat_openai_compatible(
      base_url = "http://127.0.0.1:1234/v1",
      model = "qwen/qwen3-4b-2507"
    )

    options(chores.chat = qwen3_4b)

On other systems, use Ollama. Run `ollama pull qwen3:4b` at the terminal, then set `options(chores.chat = ellmer::chat_ollama(model = "qwen3:4b"))`.

## The chores addin

Rather than through package functions directly, helpers are interfaced with via the chores addin. Once you have a default model set up, you’re ready to use the package in any RStudio session (even if you haven’t loaded the package yet).

Just:

- Select some code.
- Trigger the chores addin.
- Type in a helper “chore.” Once it’s autocompleted, press Enter.
- Watch your code be rewritten.

Chore helpers are interfaced with the via the chores addin. For easiest access, we recommend registering the chores addin to a keyboard shortcut.

In RStudio, navigate to `Tools > Modify Keyboard Shortcuts > Search "Chores"`—we suggest `Ctrl+Alt+C` (or `Ctrl+Cmd+C` on macOS).

In Positron, you’ll need to open the command palette, run “Open Keyboard Shortcuts (JSON)”, and paste the following into your `keybindings.json`:

        {
            "key": "Ctrl+Cmd+C",
            "command": "workbench.action.executeCode.console",
            "when": "editorTextFocus",
            "args": {
                "langId": "r",
                "code": "chores::.init_addin()",
                "focus": true
            }
        }

The analogous keybinding on non-macOS is `Ctrl+Alt+C`. That said, change the `"key"` entry to any keybinding you wish!

Once those steps are completed, you’re ready to use helpers with a keyboard shortcut.

## Adding custom helpers

While the chores package comes with three helpers for package development, one can use helpers for all sorts of coding tasks in R, from interactive data analysis to authoring with Quarto, or even for coding tasks in languages other than R! All you need to set up your own helper is a markdown file.

To learn more about adding custom helpers as well as how to share them with others, see the “Custom helpers” vignette with `vignette("custom", package = "chores")`.