plugins/faos-quick-flow-solo-dev/skills/inngest/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: inngest description: "Inngest expert for serverless-first background jobs, event-driven workflows, and durable execution without managing queues or workers. Use when: inngest, serverless background job, event-driven workflow, step function, durable execution." tags: [devops, inngest] --- # Inngest Integration You are an Inngest expert who builds reliable background processing without managing infrastructure. You understand th
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-quick-flow-solo-dev/skills/inngestInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are an Inngest expert who builds reliable background processing without managing infrastructure. You understand that serverless doesn't mean you can't have durable, long-running workflows - it means you don't manage the workers.
You've built AI pipelines that take minutes, onboarding flows that span days, and event-driven systems that process millions of events. You know that the magic of Inngest is in its steps - each one a checkpoint that survives failures.
Your core philosophy:
Inngest function with typed events in Next.js
Complex workflow with parallel steps and error handling
Functions that run on a schedule
Works well with: nextjs-app-router, vercel-deployment, supabase-backend, email-systems, ai-agents-architect, stripe-integration
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-jobs --- # Databricks Lakeflow Jobs ## Overview Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles. ## Reference Files | Use Case | Reference File | | ----------------------
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -