n8n Workflow Automation Skill

Purpose

Provide specialized guidance for developing workflows, custom nodes, and integrations on the n8n automation platform. Enable AI assistants to design workflows, write custom code nodes, build TypeScript-based custom nodes, integrate external services, and implement AI agent patterns.

When to Use This Skill

Invoke this skill when:

• Designing automation workflows combining multiple services
• Writing JavaScript/Python code within workflow nodes
• Building custom nodes in TypeScript
• Integrating APIs, databases, and cloud services
• Creating AI agent workflows with LangChain
• Troubleshooting workflow execution errors
• Planning self-hosted n8n deployments
• Converting manual processes to automated workflows

Do NOT use this skill for:

• Generic automation advice (use appropriate language/platform skill)
• Cloud platform-specific integrations (combine with cloud provider skill)
• Database design (use database-specialist skill)
• Frontend development (n8n has minimal UI customization)

Core n8n Concepts

Platform Architecture

Runtime Environment:

• Node.js-based execution engine
• TypeScript (90.7%) and Vue.js frontend
• pnpm monorepo structure
• Self-hosted or cloud deployment options

Workflow Execution Models:

1. Manual trigger - User-initiated execution
2. Webhook trigger - HTTP endpoint activation
3. Schedule trigger - Cron-based timing
4. Event trigger - External service events (database changes, file uploads)
5. Error trigger - Workflow failure handling

Fair-code License:

• Apache 2.0 with Commons Clause
• Free for self-hosting and unlimited executions
• Commercial restrictions for SaaS offerings

Node Types and Categories

Core Nodes (Data manipulation):

• Code - Execute JavaScript/Python
• Set - Assign variable values
• If - Conditional branching
• Switch - Multi-branch routing
• Merge - Combine data streams
• Split In Batches - Process large datasets incrementally
• Loop Over Items - Iterate through data

Trigger Nodes (Workflow initiation):

• Webhook - HTTP endpoint
• Schedule - Time-based execution
• Manual Trigger - User activation
• Error Trigger - Catch workflow failures
• Start - Default entry point

Action Nodes (500+ integrations):

• API connectors (REST, GraphQL, SOAP)
• Database clients (PostgreSQL, MongoDB, MySQL, Redis)
• Cloud services (AWS, GCP, Azure, Cloudflare)
• Communication (Email, Slack, Discord, SMS)
• File operations (FTP, S3, Google Drive, Dropbox)
• Authentication (OAuth2, API keys, JWT)

AI Nodes (LangChain integration):

• AI Agent - Autonomous decision-making
• AI Chain - Sequential LLM operations
• AI Transform - Data manipulation with LLMs
• Vector Store - Embedding storage and retrieval
• Document Loaders - Text extraction from files

Data Flow and Connections

Connection Types:

1. Main connection - Primary data flow (solid line)
2. Error connection - Failure routing (dashed red line)

Data Structure:

// Input/output format for all nodes
[
  {
    json: { /* Your data object */ },
    binary: { /* Optional binary data (files, images) */ },
    pairedItem: { /* Reference to source item */ }
  }
]

Data Access Patterns:

• Expression - {{ $json.field }} (current node output)
• Input reference - {{ $('NodeName').item.json.field }} (specific node)
• All items - {{ $input.all() }} (entire dataset)
• First item - {{ $input.first() }} (single item)
• Item index - {{ $itemIndex }} (current iteration)

Credentials and Authentication

Credential Types:

• Predefined - Pre-configured for popular services (OAuth2, API key)
• Generic - HTTP authentication (Basic, Digest, Header Auth)
• Custom - User-defined credential structures

Security Practices:

• Credentials stored encrypted in database
• Environment variable support for sensitive values
• Credential sharing across workflows (optional)
• Rotation: Manual update required

Workflow Design Methodology

Planning Phase

Step 1: Define Requirements

• Input sources (webhooks, schedules, databases)
• Data transformations needed
• Output destinations (APIs, files, databases)
• Error handling requirements
• Execution frequency and volume

Step 2: Map Data Flow

• Identify trigger events
• List transformation steps
• Specify validation rules
• Define branching logic
• Plan error recovery

Step 3: Select Nodes

Decision criteria:

• Use native nodes when available (optimized, maintained)
• Use Code node for custom logic <50 lines
• Build custom node for reusable complex logic >100 lines
• Use HTTP Request node for APIs without native nodes
• Use Execute Command node for system operations (security risk)

Implementation Phase

Workflow Structure Pattern:

[Trigger] → [Validation] → [Branch (If/Switch)] → [Processing] → [Error Handler]
                                ↓                      ↓
                          [Path A nodes]        [Path B nodes]
                                ↓                      ↓
                          [Merge/Output]         [Output]

Modular Design:

• Extract reusable logic to sub-workflows
• Use Execute Workflow node for modularity
• Limit main workflow to 15-20 nodes (readability)
• Parameterize workflows with input variables

Error Handling Strategy:

1. Error Trigger workflows - Capture all failures
2. Try/Catch pattern - Error output connections on nodes
3. Retry logic - Configure per-node retry settings
4. Validation nodes - If/Switch for data checks
5. Notification - Alert on critical failures (Email, Slack)

Testing Phase

Local Testing:

• Execute with sample data
• Verify each node output (inspect data panel)
• Test error paths with invalid data
• Check credential connections

Production Validation:

• Enable workflow, monitor executions
• Review execution history for failures
• Check resource usage (execution time, memory)
• Validate output data quality

Code Execution in Workflows

Code Node (JavaScript)

Available APIs:

• Node.js built-ins - fs, path, crypto, https
• Lodash - _.groupBy(), _.sortBy(), etc.
• Luxon - DateTime manipulation
• n8n helpers - $input, $json, $binary

Basic Structure:

// Access input items
const items = $input.all();

// Process data
const processedItems = items.map(item => {
  const inputData = item.json;

  return {
    json: {
      // Output fields
      processed: inputData.field.toUpperCase(),
      timestamp: new Date().toISOString()
    }
  };
});

// Return transformed items
return processedItems;

Data Transformation Patterns:

Filtering:

const items = $input.all();
return items.filter(item => item.json.status === 'active');

Aggregation:

const items = $input.all();
const grouped = _.groupBy(items, item => item.json.category);

return [{
  json: {
    summary: Object.keys(grouped).map(category => ({
      category,
      count: grouped[category].length
    }))
  }
}];

API calls (async):

const items = $input.all();
const results = [];

for (const item of items) {
  const response = await fetch(`https://api.example.com/data/${item.json.id}`);
  const data = await response.json();

  results.push({
    json: {
      original: item.json,
      enriched: data
    }
  });
}

return results;

Error Handling in Code:

const items = $input.all();

return items.map(item => {
  try {
    // Risky operation
    const result = JSON.parse(item.json.data);
    return { json: { parsed: result } };
  } catch (error) {
    return {
      json: {
        error: error
how to use n8n
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use n8n on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add n8n
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/vladm3105/aidoc-flow-framework --skill n8ncopy
The skills CLI fetches n8n from GitHub repository vladm3105/aidoc-flow-framework and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/n8n
Reload or restart Cursor to activate n8n. Access the skill through slash commands (e.g., /n8n) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?