write-script-bigquery
MUST use when writing BigQuery queries.
What this skill does
## 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` — generate `.script.yaml` and `.lock` files for the script you modified. - `wmill sync push` — deploy local changes to the workspace. Only suggest/run this 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. ### 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`, `$2`, … for Bash, `param(...)` for PowerShell. `wmill script preview` does not deploy, but it still executes script code and may cause side effects; run it yourself when the user asked to test/preview (or after confirming that execution is intended). `wmill sync push` and `wmill generate-metadata` modify workspace state or local files — only run these when the user explicitly asks; otherwise tell them which to run. For a **visual** open-the-script-in-the-dev-page preview (rather than `script preview`'s run-and-print-result), use the `preview` skill. Use `wmill resource-type list --schema` to discover available resource types. # BigQuery Arguments use `@name` syntax. Name the parameters by adding comments before the statement: ```sql -- @name1 (string) -- @name2 (int64) = 0 SELECT * FROM users WHERE name = @name1 AND age > @name2; ``` ## Receiving an S3Object as a script parameter Declare the arg with type `(s3object)`. Windmill renders an S3 file picker for it, downloads the file, and binds it as a `STRING` JSON parameter — Parquet/CSV files are decoded server-side into a JSON array of records, JSON/JSONL pass through. Consume with `JSON_EXTRACT_ARRAY` / `JSON_VALUE`: ```sql -- @file (s3object) SELECT CAST(JSON_VALUE(row, '$.id') AS INT64) AS id, JSON_VALUE(row, '$.name') AS name FROM UNNEST(JSON_EXTRACT_ARRAY(@file)) AS row; ``` ## Streaming query results to S3 Add a `-- s3` directive at the top of the script to stream the result set to S3 instead of returning rows. Windmill writes the file and returns its `S3Object` as the script result. ```sql -- s3 prefix=exports/users format=parquet SELECT id, name FROM users; ``` All keys are optional: `prefix` (object key prefix), `storage` (named storage — omit to use the workspace default), `format` (`json` (default), `parquet`, or `csv`). Use this for large result sets — rows stream directly to S3 instead of being buffered, bypassing the 10000-row return cap.
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