Can this skill help with error handling in n8n?

Yes, it includes specific architectural patterns and best practices for implementing robust error handling, including error triggers and conditional routing.

How does this help with workflow deployment?

It provides a comprehensive validation and deployment checklist to ensure your workflow is correctly configured and tested before manual activation.

Does it support AI-specific workflows?

Yes, it provides dedicated patterns for building AI agent workflows that incorporate language models, tools, and window buffer memory.

n8n Workflow Patterns

Name: n8n Workflow Patterns
Author: thanhtoan105

bythanhtoan105

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Productivity & Workflow

Provides architectural guidance and proven design patterns for building efficient n8n automation workflows.

About

This skill offers a comprehensive library of architectural patterns for n8n, enabling users to design, build, and optimize automation workflows with professional-grade structure. It covers five core patterns—webhook processing, HTTP API integration, database operations, AI agent workflows, and scheduled tasks—while providing practical checklists, error-handling strategies, and troubleshooting guides to ensure workflow reliability and scalability. Whether you are building simple notifications or complex AI-driven data pipelines, this skill provides the blueprint for robust automation.

Key Features

Five core architectural patterns for diverse automation scenarios
Integration strategies for AI agent workflows with tools and memory
Standardized error handling patterns and common troubleshooting solutions
Step-by-step workflow creation, validation, and deployment checklists
1 GitHub stars
Guidance on data flow structures including branching, looping, and parallel processing

Use Cases

Setting up scheduled data synchronization and ETL processes between databases
Designing a complex multi-step API integration with robust error handling
Building an AI-powered agent workflow with tool access and conversation memory

About

Key Features

Five core architectural patterns for diverse automation scenarios
Integration strategies for AI agent workflows with tools and memory
Standardized error handling patterns and common troubleshooting solutions
Step-by-step workflow creation, validation, and deployment checklists
1 GitHub stars
Guidance on data flow structures including branching, looping, and parallel processing

Use Cases

Setting up scheduled data synchronization and ETL processes between databases
Designing a complex multi-step API integration with robust error handling
Building an AI-powered agent workflow with tool access and conversation memory