trigger-dev

name	trigger-dev
description	Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design.
risk	unknown
source	vibeship-spawner-skills (Apache 2.0)
date_added	"2026-02-27T00:00:00.000Z"

Trigger.dev Integration

Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design.

Principles

• Tasks are the building blocks - each task is independently retryable
• Runs are durable - state survives crashes and restarts
• Integrations are first-class - use built-in API wrappers for reliability
• Logs are your debugging lifeline - log liberally in tasks
• Concurrency protects your resources - always set limits
• Delays and schedules are built-in - no external cron needed
• AI-ready by design - long-running AI tasks just work
• Local development matches production - use the CLI

Capabilities

• trigger-dev-tasks
• ai-background-jobs
• integration-tasks
• scheduled-triggers
• webhook-handlers
• long-running-tasks
• task-queues
• batch-processing

Scope

• redis-queues -> bullmq-specialist
• pure-event-driven -> inngest
• workflow-orchestration -> temporal-craftsman
• infrastructure -> infra-architect

Tooling

Core

• trigger-dev-sdk
• trigger-cli

Frameworks

• nextjs
• remix
• express
• hono

Integrations

• openai
• anthropic
• resend
• stripe
• slack
• supabase

Deployment

• trigger-cloud
• self-hosted
• docker

Patterns

Basic Task Setup

Setting up Trigger.dev in a Next.js project

When to use: Starting with Trigger.dev in any project

// trigger.config.ts import { defineConfig } from '@trigger.dev/sdk/v3';

export default defineConfig({ project: 'my-project', runtime: 'node', logLevel: 'log', retries: { enabledInDev: true, default: { maxAttempts: 3, minTimeoutInMs: 1000, maxTimeoutInMs: 10000, factor: 2, }, }, });

// src/trigger/tasks.ts import { task, logger } from '@trigger.dev/sdk/v3';

export const helloWorld = task({ id: 'hello-world', run: async (payload: { name: string }) => { logger.log('Processing hello world', { payload });

// Simulate work
await new Promise(resolve => setTimeout(resolve, 1000));

return { message: `Hello, ${payload.name}!` };

}, });

// Triggering from your app import { helloWorld } from '@/trigger/tasks';

// Fire and forget await helloWorld.trigger({ name: 'World' });

// Wait for result const handle = await helloWorld.trigger({ name: 'World' }); const result = await handle.wait();

AI Task with OpenAI Integration

Using built-in OpenAI integration with automatic retries

When to use: Building AI-powered background tasks

import { task, logger } from '@trigger.dev/sdk/v3'; import { openai } from '@trigger.dev/openai';

// Configure OpenAI with Trigger.dev const openaiClient = openai.configure({ id: 'openai', apiKey: process.env.OPENAI_API_KEY, });

export const generateContent = task({ id: 'generate-content', retry: { maxAttempts: 3, }, run: async (payload: { topic: string; style: string }) => { logger.log('Generating content', { topic: payload.topic });

// Uses Trigger.dev's OpenAI integration - handles retries automatically
const completion = await openaiClient.chat.completions.create({
  model: 'gpt-4-turbo-preview',
  messages: [
    {
      role: 'system',
      content: `You are a ${payload.style} writer.`,
    },
    {
      role: 'user',
      content: `Write about: ${payload.topic}`,
    },
  ],
});

const content = completion.choices[0].message.content;
logger.log('Generated content', { length: content?.length });

return { content, tokens: completion.usage?.total_tokens };

}, });

Scheduled Task with Cron

Tasks that run on a schedule

When to use: Periodic jobs like reports, cleanup, or syncs

import { schedules, task, logger } from '@trigger.dev/sdk/v3';

export const dailyCleanup = schedules.task({ id: 'daily-cleanup', cron: '0 2 * * *', // 2 AM daily run: async () => { logger.log('Starting daily cleanup');

// Clean up old records
const deleted = await db.logs.deleteMany({
  where: {
    createdAt: { lt: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000) },
  },
});

logger.log('Cleanup complete', { deletedCount: deleted.count });

return { deleted: deleted.count };

}, });

// Weekly report export const weeklyReport = schedules.task({ id: 'weekly-report', cron: '0 9 * * 1', // Monday 9 AM run: async () => { const stats = await generateWeeklyStats(); await sendReportEmail(stats); return stats; }, });

Batch Processing

Processing large datasets in batches

When to use: Need to process many items with rate limiting

import { task, logger, wait } from '@trigger.dev/sdk/v3';

export const processBatch = task({ id: 'process-batch', queue: { concurrencyLimit: 5, // Only 5 running at once }, run: async (payload: { items: string[] }) => { const results = [];

for (const item of payload.items) {
  logger.log('Processing item', { item });

  const result = await processItem(item);
  results.push(result);

  // Respect rate limits
  await wait.for({ seconds: 1 });
}

return { processed: results.length, results };

}, });

// Trigger batch processing export const startBatchJob = task({ id: 'start-batch', run: async (payload: { datasetId: string }) => { const items = await fetchDataset(payload.datasetId);

// Split into chunks of 100
const chunks = chunkArray(items, 100);

// Trigger parallel batch tasks
const handles = await Promise.all(
  chunks.map(chunk => processBatch.trigger({ items: chunk }))
);

logger.log('Started batch processing', {
  totalItems: items.length,
  batches: chunks.length,
});

return { batches: handles.length };

}, });

Webhook Handler

Processing webhooks reliably with deduplication

When to use: Handling webhooks from Stripe, GitHub, etc.

import { task, logger, idempotencyKeys } from '@trigger.dev/sdk/v3';

export const handleStripeEvent = task({ id: 'handle-stripe-event', run: async (payload: { eventId: string; type: string; data: any; }) => { // Idempotency based on Stripe event ID const idempotencyKey = await idempotencyKeys.create(payload.eventId);

if (idempotencyKey.isNew === false) {
  logger.log('Duplicate event, skipping', { eventId: payload.eventId });
  return { skipped: true };
}

logger.log('Processing Stripe event', {
  type: payload.type,
  eventId: payload.eventId,
});

switch (payload.type) {
  case 'checkout.session.completed':
    await handleCheckoutComplete(payload.data);
    break;
  case 'customer.subscription.updated':
    await handleSubscriptionUpdate(payload.data);
    break;
}

return { processed: true, type: payload.type };

}, });

Sharp Edges

Task timeout kills execution without clear error

Severity: CRITICAL

Situation: Long-running AI task or batch process suddenly stops. No error in logs. Task shows as failed in dashboard but no stack trace. Data partially processed.

Symptoms:

• Task fails with no error message
• Partial data processing
• Works locally, fails in production
• "Task timed out" in dashboard

Why this breaks: Trigger.dev has execution timeouts (defaults vary by plan). When exceeded, the task is killed mid-execution. If you're not logging progress, you won't know where it stopped. This is especially common with AI tasks that can take minutes.

Recommended fix:

Configure explicit timeouts:

export const processDocument = task({
  id: 'process-document',
  machine: {
    preset: 'large-2x',  // More resources = longer allowed time
  },
  run: async (payload) => {
    logger.log('Starting document processing', { docId: payload.id });

    // Log progress at each step
    logger.log('Step 1: Extracting text');
    const text = await extractText(payload.fileUrl);

    logger.log('Step 2: Generating embeddings', { textLength: text.length });
    const embeddings = await generateEmbeddings(text);

    logger.log('Step 3: Storing vectors', { count: embeddings.length });
    await storeVectors(embeddings);

    logger.log('Completed successfully');
    return { processed: true };
  },
});

For very long tasks, break into subtasks:

• Use triggerAndWait for sequential steps
• Each subtask has its own timeout
• Progress is visible in dashboard

Non-serializable payload causes silent task failure

Severity: CRITICAL

Situation: Passing Date objects, class instances, or circular references in payload. Task queued but never runs. Or runs with undefined/null values.

Symptoms:

• Payload values are undefined in task
• Date objects become strings
• Class methods not available
• "Converting circular structure to JSON"

Why this breaks: Trigger.dev serializes payloads to JSON. Dates become strings, class instances lose methods, functions disappear, circular refs throw. Your task sees different data than you sent.

Recommended fix:

Always use plain objects:

// WRONG - Date becomes string
await myTask.trigger({ createdAt: new Date() });

// RIGHT - ISO string
await myTask.trigger({ createdAt: new Date().toISOString() });

// WRONG - Class instance
await myTask.trigger({ user: new User(data) });

// RIGHT - Plain object
await myTask.trigger({ user: { id: data.id, email: data.email } });

// WRONG - Circular reference
const obj = { parent: null };
obj.parent = obj;
await myTask.trigger(obj);  // Throws!

In task, reconstitute as needed:

run: async (payload: { createdAt: string }) => {
  const date = new Date(payload.createdAt);
  // ...
}

Enprojectnment variables not synced to Trigger.dev cloud

Severity: CRITICAL

Situation: Task works locally but fails in production. Env var that exists in Vercel is undefined in Trigger.dev. API calls fail, database connections fail.

Symptoms:

• "Enprojectnment variable not found"
• API calls return 401 in production tasks
• Works in dev, fails in production
• Database connection errors in tasks

Why this breaks: Trigger.dev runs tasks in its own cloud, separate from your Vercel/Railway deployment. Enprojectnment variables must be configured in BOTH places. They don't automatically sync.

Recommended fix:

Sync env vars to Trigger.dev:

1. Go to Trigger.dev dashboard
2. Project Settings > Enprojectnment Variables
3. Add ALL required env vars

Or use CLI:

# Create .env.trigger file
DATABASE_URL=postgres://...
OPENAI_API_KEY=sk-...
STRIPE_SECRET_KEY=sk_live_...

# Push to Trigger.dev
npx trigger.dev@latest env push

Common missing vars:

• DATABASE_URL
• OPENAI_API_KEY / ANTHROPIC_API_KEY
• STRIPE_SECRET_KEY
• Service API keys
• Internal service URLs

Test in staging:

Trigger.dev has separate envs - configure staging too

SDK version mismatch between CLI and package

Severity: HIGH

Situation: Updated @trigger.dev/sdk but forgot to update CLI. Or vice versa. Tasks fail to register. Weird type errors. Dev server crashes.

Symptoms:

• Tasks not appearing in dashboard
• Type errors in trigger.config.ts
• "Failed to register task"
• Dev server crashes on start

Why this breaks: The Trigger.dev SDK and CLI must be on compatible versions. Breaking changes between versions cause registration failures. The CLI generates types that must match the SDK.

Recommended fix:

Always update together:

# Update both SDK and CLI
npm install @trigger.dev/sdk@latest
npx trigger.dev@latest dev

# Or pin to same version
npm install @trigger.dev/sdk@3.3.0
npx trigger.dev@3.3.0 dev

Check versions:

npx trigger.dev@latest --version
npm list @trigger.dev/sdk

In CI/CD:

- run: npm install @trigger.dev/sdk@${{ env.TRIGGER_VERSION }}
- run: npx trigger.dev@${{ env.TRIGGER_VERSION }} deploy

Task retries cause duplicate side effects

Severity: HIGH

Situation: Task sends email, then fails on next step. Retry sends email again. Customer gets 3 identical emails. Or 3 Stripe charges. Or 3 Slack messages.

Symptoms:

• Duplicate emails on retry
• Multiple charges for same order
• Duplicate webhook deliveries
• Data inserted multiple times

Why this breaks: Trigger.dev retries failed tasks from the beginning. If your task has side effects before the failure point, those execute again. Without idempotency, you create duplicates.

Recommended fix:

Use idempotency keys:

import { task, idempotencyKeys } from '@trigger.dev/sdk/v3';

export const sendOrderEmail = task({
  id: 'send-order-email',
  run: async (payload: { orderId: string }) => {
    // Check if already sent
    const key = await idempotencyKeys.create(`email-${payload.orderId}`);

    if (!key.isNew) {
      logger.log('Email already sent, skipping');
      return { skipped: true };
    }

    await sendEmail(payload.orderId);
    return { sent: true };
  },
});

Alternative: Track in database

const existing = await db.emailLogs.findUnique({
  where: { orderId_type: { orderId, type: 'order_confirmation' } }
});

if (existing) {
  logger.log('Already sent');
  return;
}

await sendEmail(orderId);
await db.emailLogs.create({ data: { orderId, type: 'order_confirmation' } });

High concurrency overwhelms downstream services

Severity: HIGH

Situation: Burst of 1000 tasks triggered. All hit OpenAI API simultaneously. Rate limited. All fail. Retry. Rate limited again. Vicious cycle.

Symptoms:

• Rate limit errors (429)
• Database connection pool exhausted
• API returns "too many requests"
• Mass task failures

Why this breaks: Trigger.dev scales to handle many concurrent tasks. But your downstream APIs (OpenAI, databases, external services) have rate limits. Without concurrency control, you overwhelm them.

Recommended fix:

Set queue concurrency limits:

export const callOpenAI = task({
  id: 'call-openai',
  queue: {
    concurrencyLimit: 10,  // Only 10 running at once
  },
  run: async (payload) => {
    // Protected by concurrency limit
    return await openai.chat.completions.create(payload);
  },
});

For rate-limited APIs:

export const callRateLimitedAPI = task({
  id: 'call-api',
  queue: {
    concurrencyLimit: 5,
  },
  retry: {
    maxAttempts: 5,
    minTimeoutInMs: 5000,  // Wait before retry
    factor: 2,  // Exponential backoff
  },
  run: async (payload) => {
    // Add delay between calls
    await wait.for({ milliseconds: 200 });
    return await externalAPI.call(payload);
  },
});

Start conservative:

• 5-10 for external APIs
• 20-50 for databases
• Increase based on monitoring

trigger.config.ts not at project root

Severity: HIGH

Situation: Running npx trigger.dev dev but CLI can't find config. Or config exists but in wrong location (monorepo issue).

Symptoms:

• "Could not find trigger.config.ts"
• Tasks not discovered
• Empty task list in dashboard
• Works for one package, not another

Why this breaks: The CLI looks for trigger.config.ts at the current working directory. In monorepos, you must run from the package directory, not the root. Wrong location = tasks not discovered.

Recommended fix:

Config must be at package root:

my-app/
├── trigger.config.ts  <- Here
├── package.json
├── src/
│   └── trigger/
│       └── tasks.ts

In monorepos:

monorepo/
├── apps/
│   └── web/
│       ├── trigger.config.ts  <- Here, not at monorepo root
│       ├── package.json
│       └── src/trigger/

# Run from package directory
cd apps/web && npx trigger.dev dev

Specify config location:

npx trigger.dev dev --config ./apps/web/trigger.config.ts

wait.for in loops causes memory issues

Severity: MEDIUM

Situation: Processing thousands of items with wait.for between each. Task memory grows. Eventually killed for memory.

Symptoms:

• Task killed for memory
• Slow task execution
• State blob too large error
• Works for small batches, fails for large

Why this breaks: Each wait.for creates checkpoint state. In a loop with thousands of iterations, this accumulates. The task's state blob grows until it hits memory limits.

Recommended fix:

Batch instead of individual waits:

// WRONG - Wait per item
for (const item of items) {
  await processItem(item);
  await wait.for({ milliseconds: 100 });  // 1000 waits = bloated state
}

// RIGHT - Batch processing
const chunks = chunkArray(items, 50);
for (const chunk of chunks) {
  await Promise.all(chunk.map(processItem));
  await wait.for({ milliseconds: 500 });  // Only 20 waits
}

For very large datasets, use subtasks:

export const processAll = task({
  id: 'process-all',
  run: async (payload: { items: string[] }) => {
    const chunks = chunkArray(payload.items, 100);

    // Each chunk is a separate task
    await Promise.all(
      chunks.map(chunk =>
        processChunk.triggerAndWait({ items: chunk })
      )
    );
  },
});

Using raw SDK instead of Trigger.dev integrations

Severity: MEDIUM

Situation: Using OpenAI SDK directly. API call fails. No automatic retry. Rate limits not handled. Have to implement all resilience manually.

Symptoms:

• Manual retry logic in tasks
• Rate limit errors not handled
• No automatic logging of API calls
• Inconsistent error handling

Why this breaks: Trigger.dev integrations wrap SDKs with automatic retries, rate limit handling, and proper logging. Using raw SDKs means you lose these features and have to implement them yourself.

Recommended fix:

Use integrations when available:

// WRONG - Raw SDK
import OpenAI from 'openai';
const openai = new OpenAI();

// RIGHT - Trigger.dev integration
import { openai } from '@trigger.dev/openai';

const openaiClient = openai.configure({
  id: 'openai',
  apiKey: process.env.OPENAI_API_KEY,
});

// Now has automatic retries and rate limiting
export const generateContent = task({
  id: 'generate-content',
  run: async (payload) => {
    const response = await openaiClient.chat.completions.create({
      model: 'gpt-4-turbo-preview',
      messages: [{ role: 'user', content: payload.prompt }],
    });
    return response;
  },
});

Available integrations:

• @trigger.dev/openai
• @trigger.dev/anthropic
• @trigger.dev/resend
• @trigger.dev/slack
• @trigger.dev/stripe

Triggering tasks without dev server running

Severity: MEDIUM

Situation: Called task.trigger() but nothing happens. No errors either. Task just disappears into void. Dev server wasn't running.

Symptoms:

• Triggers don't run
• No task in dashboard
• No errors, just silence
• Works in production, not dev

Why this breaks: In development, tasks run through the local dev server (npx trigger.dev dev). If it's not running, triggers queue up or fail silently depending on configuration. Production works differently.

Recommended fix:

Always run dev server during development:

# Terminal 1: Your app
npm run dev

# Terminal 2: Trigger.dev dev server
npx trigger.dev dev

Check dev server is connected:

• Should show "Connected to Trigger.dev"
• Tasks should appear in console
• Dashboard shows task registrations

In package.json:

{
  "scripts": {
    "dev": "next dev",
    "trigger:dev": "trigger.dev dev",
    "dev:all": "concurrently \"npm run dev\" \"npm run trigger:dev\""
  }
}

Validation Checks

Task without logging

Severity: WARNING

Message: Task has no logging. Add logger.log() calls for debugging in production.

Fix action: Import { logger } from '@trigger.dev/sdk/v3' and add log statements

Task without error handling

Severity: ERROR

Message: Task lacks explicit error handling. Unhandled errors may cause unclear failures.

Fix action: Wrap task logic in try/catch and log errors with context

Task without concurrency limit

Severity: WARNING

Message: Task has no concurrency limit. High load may overwhelm downstream services.

Fix action: Add queue: { concurrencyLimit: 10 } to protect APIs and databases

Date object in trigger payload

Severity: ERROR

Message: Date objects are serialized to strings. Use ISO string format instead.

Fix action: Use date.toISOString() instead of new Date()

Class instance in trigger payload

Severity: ERROR

Message: Class instances lose methods when serialized. Use plain objects.

Fix action: Convert class instance to plain object before triggering

Task without explicit ID

Severity: ERROR

Message: Task must have an explicit id property for registration.

Fix action: Add id: 'my-task-name' to task definition

Trigger.dev API key hardcoded

Severity: CRITICAL

Message: Trigger.dev API key should not be hardcoded - use TRIGGER_SECRET_KEY env var

Fix action: Remove hardcoded key and use process.env.TRIGGER_SECRET_KEY

Using raw OpenAI SDK instead of integration

Severity: WARNING

Message: Consider using @trigger.dev/openai for automatic retries and rate limiting

Fix action: Replace with: import { openai } from '@trigger.dev/openai'

Using raw Anthropic SDK instead of integration

Severity: WARNING

Message: Consider using @trigger.dev/anthropic for automatic retries and rate limiting

Fix action: Replace with: import { anthropic } from '@trigger.dev/anthropic'

wait.for inside loop

Severity: WARNING

Message: wait.for in loops creates many checkpoints. Consider batching instead.

Fix action: Batch items and use fewer waits, or split into subtasks

Collaboration

Delegation Triggers

• redis|bullmq|traditional queue -> bullmq-specialist (Need Redis-backed queues instead of managed service)
• vercel|deployment|serverless -> vercel-deployment (Trigger.dev needs deployment config)
• database|postgres|supabase -> supabase-backend (Tasks need database access)
• openai|anthropic|ai model|llm -> llm-architect (Tasks need AI model integration)
• event-driven|event sourcing|fan out -> inngest (Need pure event-driven model)

AI Background Processing

Skills: trigger-dev, llm-architect, nextjs-app-router, supabase-backend

Workflow:

1. User triggers via UI (nextjs-app-router)
2. Task queued (trigger-dev)
3. AI processing (llm-architect)
4. Results stored (supabase-backend)

Webhook Processing Pipeline

Skills: trigger-dev, stripe-integration, email-systems, supabase-backend

Workflow:

1. Webhook received (stripe-integration)
2. Task triggered (trigger-dev)
3. Database updated (supabase-backend)
4. Notification sent (email-systems)

Batch Data Processing

Skills: trigger-dev, supabase-backend, backend

Workflow:

1. Batch job triggered (backend)
2. Data chunked and processed (trigger-dev)
3. Results aggregated (supabase-backend)

Scheduled Reports

Skills: trigger-dev, supabase-backend, email-systems

Workflow:

1. Cron triggers task (trigger-dev)
2. Data aggregated (supabase-backend)
3. Report generated and sent (email-systems)

Related Skills

Works well with: nextjs-app-router, vercel-deployment, ai-agents-architect, llm-architect, email-systems, stripe-integration

When to Use

• User mentions or implies: trigger.dev
• User mentions or implies: trigger dev
• User mentions or implies: background task
• User mentions or implies: ai background job
• User mentions or implies: long running task
• User mentions or implies: integration task
• User mentions or implies: scheduled task

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for enprojectnment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.