inngest
Inngest expert for serverless-first background jobs, event-driven workflows, and durable execution without managing queues or workers.
About this skill
This skill transforms an AI agent into an Inngest expert, equipping it with the knowledge to design, advise on, and conceptually implement robust background processing and long-running, event-driven workflows. The agent understands Inngest's serverless architecture, emphasizing that durability and complex multi-step processes are achievable without manual infrastructure management. It can reason about use cases such as multi-minute AI pipelines, multi-day onboarding flows, and systems processing millions of events, leveraging Inngest's step-based execution for reliability, observability, and automatic retries.
Best use case
Designing and architecting reliable backend systems for AI applications; automating long-running, multi-step tasks in a fault-tolerant manner; building event-driven architectures without managing workers; advising on serverless best practices for durable workflows; troubleshooting or explaining Inngest-related concepts.
Inngest expert for serverless-first background jobs, event-driven workflows, and durable execution without managing queues or workers.
Well-reasoned advice on using Inngest for specific workflow challenges; conceptual designs for robust, serverless background processing pipelines; explanations of Inngest's features, benefits, and architectural patterns; guidance on building durable and observable event-driven systems.
Practical example
Example input
I need to build an AI pipeline that processes user-uploaded videos, which can take several minutes per video. How can I ensure this process is reliable and scalable without managing servers, and what would an Inngest-based architecture look like?
Example output
As an Inngest expert, I can help you design a robust and scalable AI video processing pipeline. Inngest is perfect for this as it handles durable, long-running workflows without you managing any workers.
Here’s how an Inngest-based architecture could work for your video processing pipeline:
1. **Trigger Event**: When a user uploads a video, an event (e.g., `video.uploaded`) is sent to Inngest. This could be from your frontend, an API endpoint, or a cloud storage webhook.
2. **Initial Inngest Function**: An Inngest function triggers on `video.uploaded`.
* **Step 1: Download Video**: The first step would be to securely download the video from storage (e.g., S3, Google Cloud Storage) into a temporary processing environment.
* **Step 2: AI Processing (Long-running)**: This is where the core AI model runs. Inngest steps are designed for long-running operations. You could have substeps here:
* `transcribe_audio`: Use an ASR model.
* `detect_objects`: Run object detection on video frames.
* `generate_summary`: Create a text summary of the video content.
* **Step 3: Store Results**: Once processing is complete, the results (transcripts, object lists, summaries, processed video segments) are uploaded back to durable storage.
* **Step 4: Notify User/System**: A final step could publish an event (e.g., `video.processed`) or send a notification to the user or another service, indicating completion and providing a link to results.
**Key Benefits of Inngest for this Use Case:**
* **Durability**: If any step fails (e.g., a network error during download, AI model timeout), Inngest automatically retries the step, ensuring your workflow completes eventually.
* **Observability**: You get a clear visual trace of each step's execution, duration, and status in the Inngest dashboard, making debugging easy.
* **Scalability**: Inngest automatically scales the workers up or down based on your event load, so you don't worry about provisioning or managing servers.
* **Developer Experience**: You define your workflow steps in code, making it versionable and easy to maintain.
This approach ensures your video processing is reliable, resilient to failures, and scales effortlessly as your user base grows.When to use this skill
- When a user needs to design a system for background tasks (e.g., image processing, data ingestion, complex computations); when durable workflows are required (e.g., onboarding sequences, multi-stage data pipelines); when event-driven processing is essential (e.g., reacting to user actions, system events); when the user specifically asks about 'Inngest', 'serverless workflows', 'reliable background jobs', or 'durable task queues'; when infrastructure management is a concern and a serverless solution is preferred.
When not to use this skill
- For simple, synchronous API calls that don't require background processing; for highly latency-sensitive, real-time operations where immediate, non-queued responses are critical; when the agent needs to perform tasks unrelated to background processing, workflow automation, or Inngest; if the required background processing infrastructure is already established and not Inngest.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/inngest/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How inngest Compares
| Feature / Agent | inngest | Standard Approach |
|---|---|---|
| Platform Support | Claude | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | easy | N/A |
Frequently Asked Questions
What does this skill do?
Inngest expert for serverless-first background jobs, event-driven workflows, and durable execution without managing queues or workers.
Which AI agents support this skill?
This skill is designed for Claude.
How difficult is it to install?
The installation complexity is rated as easy. You can find the installation instructions above.
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.
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SKILL.md Source
# Inngest Integration
Inngest expert for serverless-first background jobs, event-driven workflows,
and durable execution without managing queues or workers.
## Principles
- Events are the primitive - everything triggers from events, not queues
- Steps are your checkpoints - each step result is durably stored
- Sleep is not a hack - Inngest sleeps are real, not blocking threads
- Retries are automatic - but you control the policy
- Functions are just HTTP handlers - deploy anywhere that serves HTTP
- Concurrency is a first-class concern - protect downstream services
- Idempotency keys prevent duplicates - use them for critical operations
- Fan-out is built-in - one event can trigger many functions
## Capabilities
- inngest-functions
- event-driven-workflows
- step-functions
- serverless-background-jobs
- durable-sleep
- fan-out-patterns
- concurrency-control
- scheduled-functions
## Scope
- redis-queues -> bullmq-specialist
- workflow-orchestration -> temporal-craftsman
- message-streaming -> event-architect
- infrastructure -> infra-architect
## Tooling
### Core
- inngest
- inngest-cli
### Frameworks
- nextjs
- express
- hono
- remix
- sveltekit
### Deployment
- vercel
- cloudflare-workers
- netlify
- railway
- fly-io
### Patterns
- step-functions
- event-fan-out
- scheduled-cron
- webhook-handling
## Patterns
### Basic Function Setup
Inngest function with typed events in Next.js
**When to use**: Starting with Inngest in any Next.js project
// lib/inngest/client.ts
import { Inngest } from 'inngest';
export const inngest = new Inngest({
id: 'my-app',
schemas: new EventSchemas().fromRecord<Events>(),
});
// Define your events with types
type Events = {
'user/signed.up': { data: { userId: string; email: string } };
'order/placed': { data: { orderId: string; total: number } };
};
// lib/inngest/functions.ts
import { inngest } from './client';
export const sendWelcomeEmail = inngest.createFunction(
{ id: 'send-welcome-email' },
{ event: 'user/signed.up' },
async ({ event, step }) => {
// Step 1: Get user details
const user = await step.run('get-user', async () => {
return await db.users.findUnique({ where: { id: event.data.userId } });
});
// Step 2: Send welcome email
await step.run('send-email', async () => {
await resend.emails.send({
to: user.email,
subject: 'Welcome!',
template: 'welcome',
});
});
// Step 3: Wait 24 hours, then send tips
await step.sleep('wait-for-tips', '24h');
await step.run('send-tips', async () => {
await resend.emails.send({
to: user.email,
subject: 'Getting Started Tips',
template: 'tips',
});
});
}
);
// app/api/inngest/route.ts (Next.js App Router)
import { serve } from 'inngest/next';
import { inngest } from '@/lib/inngest/client';
import { sendWelcomeEmail } from '@/lib/inngest/functions';
export const { GET, POST, PUT } = serve({
client: inngest,
functions: [sendWelcomeEmail],
});
### Multi-Step Workflow
Complex workflow with parallel steps and error handling
**When to use**: Processing that involves multiple services or long waits
export const processOrder = inngest.createFunction(
{
id: 'process-order',
retries: 3,
concurrency: { limit: 10 }, // Max 10 orders processing at once
},
{ event: 'order/placed' },
async ({ event, step }) => {
const { orderId } = event.data;
// Parallel steps - both run simultaneously
const [inventory, payment] = await Promise.all([
step.run('check-inventory', () => checkInventory(orderId)),
step.run('validate-payment', () => validatePayment(orderId)),
]);
if (!inventory.available) {
// Send event instead of direct call (fan-out pattern)
await step.sendEvent('notify-backorder', {
name: 'order/backordered',
data: { orderId, items: inventory.missing },
});
return { status: 'backordered' };
}
// Process payment
const charge = await step.run('charge-payment', async () => {
return await stripe.charges.create({
amount: event.data.total,
customer: payment.customerId,
});
});
// Ship order
await step.run('ship-order', () => fulfillment.ship(orderId));
return { status: 'completed', chargeId: charge.id };
}
);
### Scheduled/Cron Functions
Functions that run on a schedule
**When to use**: Recurring tasks like daily reports or cleanup jobs
export const dailyDigest = inngest.createFunction(
{ id: 'daily-digest' },
{ cron: '0 9 * * *' }, // Every day at 9am UTC
async ({ step }) => {
// Get all users who want digests
const users = await step.run('get-users', async () => {
return await db.users.findMany({
where: { digestEnabled: true },
});
});
// Send to each user (creates child events)
await step.sendEvent(
'send-digests',
users.map(user => ({
name: 'digest/send',
data: { userId: user.id },
}))
);
return { sent: users.length };
}
);
// Separate function handles individual digest sending
export const sendDigest = inngest.createFunction(
{ id: 'send-digest', concurrency: { limit: 50 } },
{ event: 'digest/send' },
async ({ event, step }) => {
// ... send individual digest
}
);
### Webhook Handler with Idempotency
Safely process webhooks with deduplication
**When to use**: Handling Stripe, GitHub, or other webhooks
export const handleStripeWebhook = inngest.createFunction(
{
id: 'stripe-webhook',
// Deduplicate by Stripe event ID
idempotency: 'event.data.stripeEventId',
},
{ event: 'stripe/webhook.received' },
async ({ event, step }) => {
const { type, data } = event.data;
switch (type) {
case 'checkout.session.completed':
await step.run('fulfill-order', async () => {
await fulfillOrder(data.session.id);
});
break;
case 'customer.subscription.deleted':
await step.run('cancel-subscription', async () => {
await cancelSubscription(data.subscription.id);
});
break;
}
}
);
### AI Pipeline with Long Processing
Multi-step AI processing with chunked work
**When to use**: AI workflows that may take minutes to complete
export const processDocument = inngest.createFunction(
{
id: 'process-document',
retries: 2,
concurrency: { limit: 5 }, // Limit API usage
},
{ event: 'document/uploaded' },
async ({ event, step }) => {
// Step 1: Extract text (may take a while)
const text = await step.run('extract-text', async () => {
return await extractTextFromPDF(event.data.fileUrl);
});
// Step 2: Chunk for embedding
const chunks = await step.run('chunk-text', async () => {
return chunkText(text, { maxTokens: 500 });
});
// Step 3: Generate embeddings (API rate limited)
const embeddings = await step.run('generate-embeddings', async () => {
return await openai.embeddings.create({
model: 'text-embedding-3-small',
input: chunks,
});
});
// Step 4: Store in vector DB
await step.run('store-vectors', async () => {
await vectorDb.upsert({
vectors: embeddings.data.map((e, i) => ({
id: `${event.data.documentId}-${i}`,
values: e.embedding,
metadata: { chunk: chunks[i] },
})),
});
});
return { chunks: chunks.length, status: 'indexed' };
}
);
## Validation Checks
### Inngest serve handler present
Severity: CRITICAL
Message: Inngest requires a serve handler to receive events
Fix action: Create app/api/inngest/route.ts with serve() export
### Functions registered with serve
Severity: ERROR
Message: Ensure all Inngest functions are registered in the serve() call
Fix action: Add function to the functions array in serve()
### Step.run has descriptive name
Severity: WARNING
Message: Step names should be kebab-case and descriptive
Fix action: Use descriptive step names like 'fetch-user' or 'send-email'
### waitForEvent has timeout
Severity: ERROR
Message: waitForEvent should have a timeout to prevent infinite waits
Fix action: Add timeout option: { timeout: '24h' }
### Function has concurrency limit
Severity: WARNING
Message: Consider adding concurrency limits to protect downstream services
Fix action: Add concurrency: { limit: 10 } to function config
### Event types defined
Severity: WARNING
Message: Inngest client should define event schemas for type safety
Fix action: Add schemas: new EventSchemas().fromRecord<Events>()
### Function has unique ID
Severity: CRITICAL
Message: Every Inngest function must have a unique ID
Fix action: Add id: 'my-function-name' to function config
### Sleep uses duration string
Severity: WARNING
Message: step.sleep should use duration strings like '1h' or '30m', not milliseconds
Fix action: Use duration string: step.sleep('wait', '1h')
### Retry policy configured
Severity: WARNING
Message: Consider configuring retry policy for failure handling
Fix action: Add retries: 3 or retries: { attempts: 3, backoff: { ... } }
### Idempotency key for payment functions
Severity: ERROR
Message: Payment-related functions should use idempotency keys
Fix action: Add idempotency: 'event.data.orderId' to function config
## Collaboration
### Delegation Triggers
- redis|queue infrastructure|bullmq -> bullmq-specialist (Need Redis-based queue with existing infrastructure)
- saga|compensation|rollback|long-running workflow -> temporal-craftsman (Need complex workflow orchestration with compensation)
- event sourcing|event store|cqrs -> event-architect (Need event sourcing patterns)
- vercel|deploy|production -> vercel-deployment (Need deployment configuration)
- database|schema|data model -> supabase-backend (Need database for event data)
- api|endpoint|route -> backend (Need API to trigger events)
### Vercel Background Jobs
Skills: inngest, nextjs-app-router, vercel-deployment
Workflow:
```
1. Define Inngest functions (inngest)
2. Set up serve handler in Next.js (nextjs-app-router)
3. Configure function timeouts (vercel-deployment)
4. Deploy and test (vercel-deployment)
```
### AI Pipeline
Skills: inngest, ai-agents-architect, supabase-backend
Workflow:
```
1. Design AI workflow steps (ai-agents-architect)
2. Implement with Inngest durability (inngest)
3. Store results in database (supabase-backend)
4. Handle retries for API failures (inngest)
```
### Webhook Processing
Skills: inngest, stripe-integration, backend
Workflow:
```
1. Receive webhook (backend)
2. Send to Inngest with idempotency (inngest)
3. Process payment logic (stripe-integration)
4. Update application state (backend)
```
### Email Automation
Skills: inngest, email-systems, supabase-backend
Workflow:
```
1. Trigger event from user action (inngest)
2. Schedule drip emails with step.sleep (inngest)
3. Send emails with retry (email-systems)
4. Track email status (supabase-backend)
```
### Scheduled Tasks
Skills: inngest, backend, analytics-architecture
Workflow:
```
1. Define cron triggers (inngest)
2. Implement processing logic (backend)
3. Aggregate and report data (analytics-architecture)
4. Handle failures with alerting (inngest)
```
## Related Skills
Works well with: `nextjs-app-router`, `vercel-deployment`, `supabase-backend`, `email-systems`, `ai-agents-architect`, `stripe-integration`
## When to Use
- User mentions or implies: inngest
- User mentions or implies: serverless background job
- User mentions or implies: event-driven workflow
- User mentions or implies: step function
- User mentions or implies: durable execution
- User mentions or implies: vercel background job
- User mentions or implies: scheduled function
- User mentions or implies: fan outRelated Skills
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