trigger-dev

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

31,392 stars

Best use case

trigger-dev is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. You are a Trigger.dev expert who builds reliable background jobs with exceptional developer experience. You understand that Trigger.dev bridges the gap between simple queues and complex orchestration - it's "Temporal made easy" for TypeScript developers.

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

Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.

Practical example

Example input

Use the "trigger-dev" skill to help with this workflow task. Context: You are a Trigger.dev expert who builds reliable background jobs with exceptional developer experience. You understand that Trigger.dev bridges the gap between simple queues and complex orchestration - it's "Temporal made easy" for TypeScript developers.

Example output

A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.

When to use this skill

  • Use this skill when you want a reusable workflow rather than writing the same prompt again and again.

When not to use this skill

  • Do not use this when you only need a one-off answer and do not need a reusable workflow.
  • Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/trigger-dev/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/trigger-dev/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/trigger-dev/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How trigger-dev Compares

Feature / Agenttrigger-devStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

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

### Environment 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:
- "Environment 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. Environment 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 > Environment Variables
3. Add ALL required env vars

# Or use CLI:
```bash
# 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:
```bash
# 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:
```bash
npx trigger.dev@latest --version
npm list @trigger.dev/sdk
```

# In CI/CD:
```yaml
- 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:
```typescript
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
```typescript
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:
```typescript
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:
```typescript
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:
```bash
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:
```typescript
// 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:
```typescript
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:
```typescript
// 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:
```bash
# 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:
```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 environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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31392
from sickn33/antigravity-awesome-skills

Expert patterns for Neon serverless Postgres, branching, connection pooling, and Prisma/Drizzle integration

nanobanana-ppt-skills

31392
from sickn33/antigravity-awesome-skills

AI-powered PPT generation with document analysis and styled images

multi-agent-patterns

31392
from sickn33/antigravity-awesome-skills

This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.

monorepo-management

31392
from sickn33/antigravity-awesome-skills

Build efficient, scalable monorepos that enable code sharing, consistent tooling, and atomic changes across multiple packages and applications.

monetization

31392
from sickn33/antigravity-awesome-skills

Estrategia e implementacao de monetizacao para produtos digitais - Stripe, subscriptions, pricing experiments, freemium, upgrade flows, churn prevention, revenue optimization e modelos de negocio SaaS.

modern-javascript-patterns

31392
from sickn33/antigravity-awesome-skills

Comprehensive guide for mastering modern JavaScript (ES6+) features, functional programming patterns, and best practices for writing clean, maintainable, and performant code.

microservices-patterns

31392
from sickn33/antigravity-awesome-skills

Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.

mcp-builder

31392
from sickn33/antigravity-awesome-skills

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.

makepad-skills

31392
from sickn33/antigravity-awesome-skills

Makepad UI development skills for Rust apps: setup, patterns, shaders, packaging, and troubleshooting.

m365-agents-py

31392
from sickn33/antigravity-awesome-skills

Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth.