القائمة
MUST use when writing PostgreSQL queries.
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npx skills add https://github.com/windmill-labs/windmill --skill write-script-postgresqlيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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عرض SKILL.md
| name | write-script-postgresql |
| description | MUST use when writing PostgreSQL queries. |
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.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".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:
Only use sync push when:
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changing main's arguments — invalidates that hash and leaves the .lock, the .script.yaml input schema, and the hash row out of date.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.
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.
Arguments are obtained directly in the statement with $1::{type}, $2::{type}, etc.
Name the parameters by adding comments at the beginning of the script (without specifying the type):
-- $1 name1
-- $2 name2 = default_value
SELECT * FROM users WHERE name = $1::TEXT AND age > $2::INT;
Declare the arg with type (s3object). Windmill renders an S3 file picker for
it, downloads the file, and binds it as a jsonb parameter — Parquet/CSV files
are decoded server-side into a JSON array of records, JSON/JSONL pass through.
Consume with jsonb_to_recordset (or any jsonb API):
-- $1 file (s3object)
SELECT *
FROM jsonb_to_recordset($1::jsonb) AS r(id INT, name TEXT);
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.
-- 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 as the script return value.
Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.
Build and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, or when another skill needs to maintain the domain model.
A relentless interview to sharpen a plan or design.