Master Inngest's durable execution model for building fault-tolerant, long-running workflows. This skill covers the complete lifecycle from triggers to error handling.
Fetches inngest-durable-functions from inngest/inngest-skills and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
.cursor/skills/inngest-durable-functions
Restart Cursor to activate inngest-durable-functions. Access via /inngest-durable-functions in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Master Inngest's durable execution model for building fault-tolerant, long-running workflows. This skill covers the complete lifecycle from triggers to error handling.
These skills are focused on TypeScript. For Python or Go, refer to the Inngest documentation for language-specific guidance. Core concepts apply across all languages.
Core Concepts You Need to Know
Durable Execution Model
Each step should encapsulate side-effects and non-deterministic code
Memoization prevents re-execution of completed steps
State persistence survives infrastructure failures
Automatic retries with configurable retry count
Step Execution Flow
// ❌ BAD: Non-deterministic logic outside stepsasync({ event, step })=>{const timestamp = Date.now();// This runs multiple times!const result =await step.run("process-data",()=>{returnprocessData(event.data);});};// ✅ GOOD: All non-deterministic logic in stepsasync({ event, step })=>{const result =await step.run("process-with-timestamp",()=>{const timestamp = Date.now();// Only runs oncereturnprocessData(event.data, timestamp);});};
Function Limits
Every Inngest function has these hard limits:
Maximum 1,000 steps per function run
Maximum 4MB returned data for each step
Maximum 32MB combined function run state including, event data, step output, and function output
Each step = separate HTTP request (~50-100ms overhead)
If you're hitting these limits, break your function into smaller functions connected via step.invoke() or step.sendEvent().
When to Use Steps
Always wrap in step.run():
API calls and network requests
Database reads and writes
File I/O operations
Any non-deterministic operation
Anything you want retried independently on failure
Never wrap in step.run():
Pure calculations and data transformations
Simple validation logic
Deterministic operations with no side effects
Logging (use outside steps)
Function Creation
Basic Function Structure
const processOrder = inngest.createFunction({ id:"process-order",// Unique, never change this triggers:[{ event:"order/created"}], retries:4,// Default: 4 retries per step concurrency:10// Max concurrent executions},async({ event, step })=>{// Your durable workflow});
Step IDs and Memoization
// Step IDs can be reused - Inngest handles counters automaticallyconst data =await step.run("fetch-data",()=>fetchUserData());const more =await step.run("fetch-data",()=>fetchOrderData());// Different execution// Use descriptive IDs for clarityawait step.run("validate-payment",()=>validatePayment(event.data.paymentId));await step.run("charge-customer",()=>chargeCustomer(event.data));await step.run("send-confirmation",()=>sendEmail(event.data.email));
Triggers and Events
Event Triggers
Triggers are defined in the triggers array in the first argument of createFunction:
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
