Configure Windmill triggers that start scripts or flows when external events arrive. The guidance covers trigger file naming and email payloads, including extra address arguments and attachments stored in S3, so you can handle incoming messages in a runnable.
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MUST use when configuring triggers.
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Triggers allow external events to invoke your scripts and flows.
Trigger configuration files use the pattern: {path}.{trigger_type}_trigger.yaml
Examples:
u/user/webhook.http_trigger.yamlf/data/kafka_consumer.kafka_trigger.yamlf/sync/postgres_cdc.postgres_trigger.yamlf/inbound/orders.email_trigger.yamlAn email trigger routes incoming emails to a script or flow. Each trigger reserves a local-part: emails sent to <local_part>@<windmill_email_domain> are delivered to the configured runnable. Set workspaced_local_part: true to namespace it per workspace (the actual recipient becomes <workspace_id>-<local_part>@…); on Windmill Cloud this is required.
Senders may append URL-style extras to the local-part with +: mytrigger+foo=bar+baz=qux@…. They flow through to the script as email_extra_args (see below).
The runnable receives:
parsed_email — { headers, text_body, html_body, attachments[] }. Each attachment has { headers, body }.raw_email — the raw RFC 822 message as a string, or an S3 object ({ s3: "windmill_emails/<job_id>/raw.eml" }) if the message exceeds 1 MiB.email_extra_args (optional, only when sender appended +key=value extras) — a flat object of the parsed extras.With a preprocessor, all of the above are nested under event along with event.kind = "email" and event.trigger_path (the trigger's path). Without a preprocessor, trigger_path is not exposed — add a preprocessor if you need it.
Binary attachments are uploaded to the workspace S3 bucket and surface in parsed_email.attachments[i].body as:
{ "s3": "windmill_emails/<job_id>/attachments/<filename>" }
To read the bytes inside a script, use the wmill SDK:
// TypeScript
import * as wmill from "windmill-client"
const file = await wmill.loadS3File(parsed_email.attachments[0].body)
# Python
import wmill
data = wmill.load_s3_file(parsed_email["attachments"][0]["body"])
If the workspace has no S3 resource configured (Workspace Settings → Object storage), body falls back to the string "configure s3 in the workspace settings to handle attachments". The same applies to large raw_email bodies. Email attachment storage requires the server to be built with the parquet feature.
Text/HTML/inline parts are placed inline in body as strings.
Deploying local changes to the workspace can be destructive to remote state — only suggest/run a deploy when the user explicitly asks to deploy/publish/push, not when they say "run", "try", or "test". Deploy via git push or wmill sync push depending on how the repo is wired (see the Deploying section in AGENTS.wmill.md). sync pull is safe to run yourself — it never mutates remote state, though it does overwrite local files to match the remote (use sync pull --dry-run to only preview).
# Push trigger configuration — only when the user explicitly asks to deploy
wmill sync push
# Pull triggers from Windmill
wmill sync pull
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Plan the code changes needed to add a TriggerCrud-based trigger to Windmill. The checklist covers backend, database, API, CLI, frontend, capture, and validation work so the new trigger can be managed across the product.
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Improve Windmill’s AI chat by reviewing its tools, prompts, result shapes, and context usage. The guidance helps place shared instructions where chat and CLI can use them, reduce repeated token costs, and compare behavior before and after changes with A/B evaluations.
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Create and run black-box benchmarks for Windmill AI generation modes such as flows, apps, scripts, CLI, and global requests. Each case exercises the production path, checks hard requirements, and uses an LLM judge to assess user-facing behavior before and after changes.
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Use this reference to work with Windmill resources from the command line, including scripts, flows, datatables, and workspace settings. It also covers listing jobs, inspecting details and logs, retrieving results, and rerunning or restarting executions when investigating failures.
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Use this skill to shape modules with small interfaces and substantial behavior behind them. It helps you decide where a seam belongs, reduce complexity exposed to callers, and make code easier to test and maintain.
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Use this skill to review pending changes, select only the relevant files for staging, and create a focused Git commit. It formats the single-line message with a Conventional Commits type such as feat, fix, or docs, and checks the resulting status.
中文简介正在准备中。当前显示英语简介。