workflow-automation

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Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.

AI & Automation 39,227 stars 6374 forks Updated today MIT

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# Workflow Automation Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility, Temporal for correctness, Inngest for developer experience. Pick based on your actual needs, not hype. ## Principles - Durable execution is non-negotiable for money or state-critical workflows - Events are the universal language of workflow triggers - Steps are checkpoints - each should be independently retryable - Start simple, add complexity only when reliability demands it - Observability isn't optional - you need to see where workflows fail - Workflows and agents co-evolve - design for both ## Capabilities - workflow-automation - workflow-orchestration - durable-execution - event-driven-workflows - step-functions - job-queues - background-jobs - scheduled-tasks ## Scope - multi-agent-coordination → multi-agent-orchestration - ci-cd-pipelines → devops - data-pipelines → data-engineer - api-design → api-designer ## Tooling ### Platforms - n8n - When: Low-code automation, quick prototyping, non-technical users Note: Self-hostable, 400+ integrations, g...

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Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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