write-script-rlang

Imported from windmill-labs/windmill (system_prompts/auto-generated/skills/write-script-rlang/SKILL.md). Install upstream with npx skills add windmill-labs/windmill --skill write-script-rlang. Copyright stays with the author.

CLI Commands

Place scripts in a folder.

After writing, tell the user which command fits what they want to do:

• wmill script preview <script_path> — default when iterating on a local script. Runs the local file without deploying.
• wmill script run <path> — runs the script already deployed in the workspace. Use only when the user explicitly wants to test the deployed version, not local edits.
• wmill generate-metadata — regenerate the local .script.yaml (input schema) and .lock (resolved dependencies) for scripts you changed, and refresh their content hashes in wmill-lock.yaml. Local files only — not a deploy. See "Keep metadata in sync" below.
• Deploy local changes to the workspace — via git push or wmill sync push depending on how the repo is wired (see the Deploying section in AGENTS.wmill.md). Only suggest/run a deploy when the user explicitly asks to deploy/publish/push — not when they say "run", "try", or "test".

Preview vs run — choose by intent, not habit

If the user says "run the script", "try it", "test it", "does it work" while there are local edits to the script file, use script preview. Do NOT push the script to then script run it — pushing is a deploy, and deploying just to test overwrites the workspace version with untested changes.

Only use script run when:

• The user explicitly says "run the deployed version" / "run what's on the server".
• There is no local script being edited (you're just invoking an existing script).

Only use sync push when:

• The user explicitly asks to deploy, publish, push, or ship.
• The preview has already validated the change and the user wants it in the workspace.

Keep metadata in sync after editing

wmill-lock.yaml tracks a content hash for each item. Editing a script's content — most importantly adding or removing an import or changing main's arguments — invalidates that hash and leaves the .lock, the .script.yaml input schema, and the hash row out of date. Run wmill generate-metadata (scoped to what you touched) after such edits so the resolved lock, the auto-generated args UI (driven by .script.yaml), and wmill-lock.yaml all match the code. Leaving them stale produces spurious diffs in git-sync and CI.

This only writes local files (it is not a deploy), but it re-resolves dependencies, so it can bump unpinned versions (the same as deploying from the UI; expected, not a bug). So by default offer it and run it once the user agrees, rather than running it silently after every edit — unless the project's AGENTS.md opts into running metadata automatically (see the "Keeping metadata in sync" preference there). Either way YOU run the command, not the user. After running it, diff the regenerated .lock / .script.lock files and tell the user which dependency versions changed (e.g. requests 2.31.0 → 2.32.0), so they can catch an unwanted bump before deploying — even under Metadata: auto, since it's information, not a confirmation gate. Pin versions in code to keep them fixed.

With no path argument, generate-metadata regenerates only the items whose content hash drifted — not everything. Imports propagate: editing a script that others import marks every importer stale too, so a one-line change to a shared module can regenerate many locks (by design — their locks must reflect the imported code). If it touches more than you expect, run wmill generate-metadata --dry-run — it lists each stale item with a reason (content changed or depends on <path>) without changing anything — then narrow with a path argument (wmill generate-metadata f/foo) or --strict-folder-boundaries.

If the on-disk .lock and .script.yaml are already correct and only wmill-lock.yaml needs its hashes refreshed (hash drift, or bootstrapping missing entries), use wmill generate-metadata rehash — it re-records hashes from disk with no backend round-trip and no dependency changes.

After writing — offer to test, don't wait passively

If the user hasn't already told you to run/test/preview the script, offer it as a one-sentence next step (e.g. "Want me to run wmill script preview with sample args?"). Do not present a multi-option menu.

If the user already asked to test/run/try the script in their original request, skip the offer and just execute wmill script preview <path> -d '<args>' directly — pick plausible args from the script's declared parameters. The shape varies by language: main(...) for code languages, the SQL dialect's own placeholder syntax ($1 for PostgreSQL, ? for MySQL/Snowflake, @P1 for MSSQL, @name for BigQuery, etc.), positional $1, $2, … for Bash, param(...) for PowerShell.

wmill script preview does not deploy, but it still executes script code and may cause side effects; run it yourself when the user asked to test/preview (or after confirming that execution is intended). wmill generate-metadata does not deploy either — it only writes local files (locks, schemas, hashes) — but offer it before running (or run automatically if the project's AGENTS.md opts in), per "Keep metadata in sync" above. Deploying to the workspace (git push or wmill sync push depending on how the repo is wired — see the Deploying section) is the only step that mutates remote state — do it only when the user explicitly asks to deploy/publish/push.

For a visual open-the-script-in-the-dev-page preview (rather than script preview's run-and-print-result), use the preview skill.

Use wmill resource-type list --schema to discover available resource types.

R

Structure

Define a main function using <- or = assignment. Parameters become the script inputs:

library(dplyr)
library(jsonlite)

main <- function(x, name = "default", flag = TRUE) {
    df <- tibble(x = x, name = name)
    result <- df %>% mutate(greeting = paste("Hello", name))
    return(toJSON(result, auto_unbox = TRUE))
}

Important:

• The main function is required
• Use library() to load packages — they are resolved and installed automatically
• jsonlite is always available (used internally for argument parsing)
• Return values must be JSON-serializable

Parameters

R types map to Windmill types:

• numeric → float/int
• character → string
• logical → bool (use TRUE/FALSE)
• list → object/dict
• NULL → null

Default values are inferred from the function signature:

main <- function(
    name,              # required string
    count = 10,        # optional int, default 10
    verbose = FALSE    # optional bool, default FALSE
) {
    # ...
}

Resources and Variables

Use the built-in Windmill helpers (no import needed):

main <- function() {
    # Get a variable
    api_key <- get_variable("f/my_folder/api_key")

    # Get a resource (returns a list)
    db <- get_resource("f/my_folder/postgres_config")
    host <- db$host
    port <- db$port

    return(list(host = host, port = port))
}

Output

Return any JSON-serializable value from main. The return value becomes the step result:

main <- function(x) {
    # Return a scalar
    return(x + 1)

    # Or a list (becomes JSON object)
    return(list(result = x + 1, status = "ok"))
}

Annotations

Control execution behavior with comment annotations:

#renv_verbose = true        # Show verbose renv output during resolution
#renv_install_verbose = true # Show verbose output during package installation
#sandbox = true              # Run in nsjail sandbox (requires nsjail)