Agent Skills: trigger-getting-started
>
ID: triggerdotdev/trigger.dev/trigger-getting-started
16,3331,463
Install this agent skill to your local
Skill Files
The files that make up trigger-getting-started.
More agent skills
- span-timeline-eventsUse when adding, modifying, or debugging OTel span timeline events in the trace view. Covers event structure, ClickHouse storage constraints, rendering in SpanTimeline component, admin visibility, and the step-by-step process for adding new events.
- trigger-dev-tasksUse this skill when writing, designing, or optimizing Trigger.dev background tasks and workflows. This includes creating reliable async tasks, implementing AI workflows, setting up scheduled jobs, structuring complex task hierarchies with subtasks, configuring build extensions for tools like ffmpeg or Puppeteer/Playwright, and handling task schemas with Zod validation.
- drizzleUse this skill when writing or modifying Drizzle ORM schemas, queries, or migrations in this repo — specifically the `@internal/dashboard-agent-db` package (the dashboard agent's conversation datastore). Covers pg-core schema definition, the postgres-js driver, drizzle-kit migrations, and this repo's conventions: a dedicated Postgres schema, foreign-key-free cross-database design, pooler-safe connections, and the access-pattern query layer. Drizzle is NOT the main database — that's Prisma.
- errors-api-e2eEnd-to-end smoke test for the public Errors HTTP API (error groups). Seeds failed runs into ClickHouse so the error materialized views populate, then drives the real endpoints against the running webapp — list (with filters + pagination), retrieve, resolve/ignore/unresolve, the `filter[error]` runs filter, user attribution via the `trigger.dev mint-token` -> JWT exchange, and the 401/403/404 negatives. Use for "smoke test the errors API", "test the errors API e2e", "prove the errors endpoints work", or to re-verify after changes.
- molfeatMolecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
- flood-detectionDetect flood events by comparing water levels to thresholds. Use when determining if flooding occurred, counting flood days, aggregating instantaneous data to daily values, or classifying flood severity.