n8n-workflow-patterns

This skill provides architectural patterns and best practices for designing, building, and optimizing n8n workflows. It covers six core patterns: Webhook Processing, HTTP API Integration, Database Operations, AI Agent Workflows, Scheduled Tasks, and Batch Processing. It includes a workflow creation checklist, performance optimization strategies for large datasets, and specific integration gotchas for Google Sheets and Google Drive.

8.3K
Installs
5
Use cases
5/10
Quality

Is n8n-workflow-patterns safe to install?

Safe to install

Safe to install: our audit of n8n-workflow-patterns's source files found 0 shell commands, 1 external URL, no file writes (none risk). Every command and URL listed appears verbatim in the skill's source. The skill provides architectural guidance and documentation. It does not execute code or perform network requests itself, though it references n8n API operations that an agent might perform.

How we audit skills: our security review methodology.

Who is this skill for?

Developers and automation engineers building or optimizing n8n workflows.

What can you do with it?

  • Designing workflow architecture for new automations
  • Selecting appropriate patterns for webhook, API, or database tasks
  • Optimizing slow workflows with large item counts
  • Implementing batch processing for API rate-limited tasks
  • Troubleshooting workflow execution order and data flow issues

How good is this skill?

Quality score: 5/10. The skill provides clear, actionable architectural patterns and specific technical advice for n8n. It is well-structured and directly addresses common pitfalls in workflow development.

What does the skill file contain?

SKILL.md
# n8n Workflow Patterns

Proven architectural patterns for building n8n workflows.

---

## The 6 Core Patterns

Based on analysis of real workflow usage:

1. **[Webhook Processing](webhook_processing.md)** (Most Common)
   - Receive HTTP requests → Process → Output
   - Pattern: Webhook → Validate → Transform → Respond/Notify

2. **[HTTP API Integration](http_api_integration.md)**
   - Fetch from REST APIs → Transform → Store/Use
   - Pattern: Trigger → HTTP Request → Transform → Action → Error Handler

3. **[Database Operations](database_operations.md)**
   - Read/Write/Sync database data
  ...

Frequently asked questions

When should I use the Batch Processing pattern?

Use it when processing large datasets that exceed API batch limits, when you need to accumulate results across multiple API calls, or when handling nested loops.

How do I optimize a workflow processing thousands of items?

Consolidate transform chains into fewer All-Items nodes, use Code nodes set to 'Run Once for All Items' instead of 'Each Item', and maximize the batchSize in SplitInBatches loops.

What is the recommended order for deploying a workflow?

Follow four gates: validate the workflow structure, verify connections, test with sample data, and finally activate the workflow.

Data sourced from czlonkowski/n8n-skills on GitHub. Install counts from skills.sh. The summary and security audit are derived from the skill's source files: every command and URL listed appears verbatim in the source.

Related skills

find-skills

2.3M

Users seeking to extend agent capabilities with specialized tools, workflows, or knowledge packages

The find-skills skill enables agents to search for, discover, and install modular packages from the open agent skills ecosystem using the Skills CLI.

highclipackage-managervercel-labs

video-edit

338.7K

Users of the RunComfy CLI who need to automate video editing tasks like restyling, background swapping, or motion transfer

The video-edit skill acts as a router for the RunComfy CLI, selecting between Wan 2.7 Edit-Video, Kling 2.6 Pro Motion Control, and Lucy Edit Restyle models based on user intent to perform video transformations.

highvideo-editingai-agentagentspace-so

lark-doc

305.2K

Users who need to automate document management, content updates, and media handling within the Lark/Feishu ecosystem

The lark-doc skill enables agents to read, create, and edit Lark (Feishu) documents, including Docx and Wiki formats. It supports content manipulation via XML or Markdown, media handling, and resource management for document covers. The skill integrates with other Lark skills by identifying and delegating operations for embedded objects like spreadsheets, databases, and mind notes.

highLarkFeishularksuite

supabase-postgres-best-practices

264.3K

Developers and database administrators working with Postgres and Supabase

This skill provides a structured set of Postgres performance optimization rules and best practices maintained by Supabase. It guides developers in writing, reviewing, and optimizing SQL queries, schema designs, and database configurations.

lowpostgressupabasesupabase