Implement GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes...
npx skills add sgcarstrends/sgcarstrends --skill "workflow-management"
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# Description
Create, debug, or modify QStash workflows for data updates and social media posting in the API service. Use when adding new automated jobs, fixing workflow errors, or updating scheduling logic.
# SKILL.md
name: workflow-management
description: Create, debug, or modify QStash workflows for data updates and social media posting in the API service. Use when adding new automated jobs, fixing workflow errors, or updating scheduling logic.
allowed-tools: Read, Edit, Write, Bash, Grep, Glob
Workflow Management Skill
This skill helps you work with QStash-based workflows in apps/api/src/lib/workflows/.
When to Use This Skill
- Adding new scheduled workflows for data fetching
- Debugging workflow execution errors
- Modifying existing workflow schedules or logic
- Integrating new data sources into the update pipeline
- Adding new social media posting workflows
Workflow Architecture
The project uses QStash workflows with the following structure:
apps/api/src/lib/workflows/
โโโ cars/ # Car registration data workflows
โ โโโ update.ts # Scheduled car data updates
โโโ coe/ # COE bidding data workflows
โ โโโ update.ts # Scheduled COE data updates
โโโ social/ # Social media posting workflows
โโโ discord.ts
โโโ linkedin.ts
โโโ telegram.ts
โโโ twitter.ts
Key Patterns
1. Workflow Definition
Workflows are defined using QStash SDK:
import { serve } from "@upstash/workflow";
export const POST = serve(async (context) => {
// Step 1: Fetch data
await context.run("fetch-data", async () => {
// Fetching logic
});
// Step 2: Process data
const processed = await context.run("process-data", async () => {
// Processing logic
});
// Step 3: Store results
await context.run("store-results", async () => {
// Storage logic
});
});
2. Scheduling Workflows
Workflows are triggered via cron schedules configured in:
- SST infrastructure (infra/)
- QStash console
- Manual API calls to workflow endpoints
3. Error Handling
Always include comprehensive error handling:
await context.run("step-name", async () => {
try {
// Logic here
} catch (error) {
console.error("Step failed:", error);
// Log to monitoring service
throw error; // Re-throw for workflow retry
}
});
Common Tasks
Adding a New Workflow
- Create workflow file in appropriate directory
- Define workflow steps using
context.run() - Add route handler in
apps/api/src/routes/ - Configure scheduling (if needed)
- Add tests for workflow logic
Debugging Workflow Failures
- Check QStash dashboard for execution logs
- Review CloudWatch logs for Lambda errors
- Verify environment variables are set correctly
- Test workflow locally using development server
- Check database connectivity and Redis availability
Modifying Existing Workflows
- Read existing workflow implementation
- Identify which step needs modification
- Update step logic while maintaining error handling
- Test changes locally
- Deploy and monitor execution
Environment Variables
Workflows typically need:
- DATABASE_URL - PostgreSQL connection
- UPSTASH_REDIS_REST_URL / UPSTASH_REDIS_REST_TOKEN - Redis
- QSTASH_TOKEN - QStash authentication
- Service-specific tokens (Discord webhook, Twitter API, etc.)
Testing Workflows
Run workflow tests:
pnpm -F @sgcarstrends/api test -- src/lib/workflows
Test individual workflow locally:
# Start dev server
pnpm dev
# Trigger workflow via HTTP
curl -X POST http://localhost:3000/api/workflows/cars/update
References
- QStash Workflows: Check Context7 for Upstash QStash documentation
- Related files:
apps/api/src/routes/workflows.ts- Workflow route handlersapps/api/src/config/qstash.ts- QStash configurationapps/api/CLAUDE.md- API service documentation
Best Practices
- Idempotency: Ensure workflows can safely retry without duplicating data
- Step Granularity: Break workflows into small, focused steps
- Logging: Add comprehensive logging for debugging
- Timeouts: Configure appropriate timeouts for long-running operations
- Testing: Write unit tests for workflow logic
- Monitoring: Track workflow execution metrics
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.