n8n

Manage n8n workflows and automations via API. Use when working with n8n workflows, executions, or automation tasks - listing workflows, activating/deactivating, checking execution status, manually triggering workflows, or debugging automation issues.

Audits

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Pattern checks against bundled files.

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Multi-engine malware detections and file reputation.

Install

openclaw skills install n8n

n8n Workflow Management

Comprehensive workflow automation management for n8n platform with creation, testing, execution monitoring, and performance optimization capabilities.

⚠️ CRITICAL: Workflow Creation Rules

When creating n8n workflows, ALWAYS:

✅ Generate COMPLETE workflows with all functional nodes
✅ Include actual HTTP Request nodes for API calls (ImageFX, Gemini, Veo, Suno, etc.)
✅ Add Code nodes for data transformation and logic
✅ Create proper connections between all nodes
✅ Use real node types (n8n-nodes-base.httpRequest, n8n-nodes-base.code, n8n-nodes-base.set)

NEVER:

❌ Create "Setup Instructions" placeholder nodes
❌ Generate workflows with only TODO comments
❌ Make incomplete workflows requiring manual node addition
❌ Use text-only nodes as substitutes for real functionality

Example GOOD workflow:

Manual Trigger → Set Config → HTTP Request (API call) → Code (parse) → Response

Example BAD workflow:

Manual Trigger → Code ("Add HTTP nodes here, configure APIs...")

Always build the complete, functional workflow with all necessary nodes configured and connected.

Setup

Required environment variables:

N8N_API_KEY — Your n8n API key (Settings → API in the n8n UI)
N8N_BASE_URL — Your n8n instance URL

Configure credentials via OpenClaw settings:

Add to ~/.config/openclaw/settings.json:

{
  "skills": {
    "n8n": {
      "env": {
        "N8N_API_KEY": "your-api-key-here",
        "N8N_BASE_URL": "your-n8n-url-here"
      }
    }
  }
}

Or set per-session (do not persist secrets in shell rc files):

export N8N_API_KEY="your-api-key-here"
export N8N_BASE_URL="your-n8n-url-here"

Verify connection:

python3 scripts/n8n_api.py list-workflows --pretty

Security note: Never store API keys in plaintext shell config files (~/.bashrc, ~/.zshrc). Use the OpenClaw settings file or a secure secret manager.

Quick Reference

Workflow Management

List Workflows

python3 scripts/n8n_api.py list-workflows --pretty
python3 scripts/n8n_api.py list-workflows --active true --pretty

Get Workflow Details

python3 scripts/n8n_api.py get-workflow --id <workflow-id> --pretty

Create Workflows

# From JSON file
python3 scripts/n8n_api.py create --from-file workflow.json

Activate/Deactivate

python3 scripts/n8n_api.py activate --id <workflow-id>
python3 scripts/n8n_api.py deactivate --id <workflow-id>

Testing & Validation

Validate Workflow Structure

# Validate existing workflow
python3 scripts/n8n_tester.py validate --id <workflow-id>

# Validate from file
python3 scripts/n8n_tester.py validate --file workflow.json --pretty

# Generate validation report
python3 scripts/n8n_tester.py report --id <workflow-id>

Dry Run Testing

# Test with data
python3 scripts/n8n_tester.py dry-run --id <workflow-id> --data '{"email": "test@example.com"}'

# Test with data file
python3 scripts/n8n_tester.py dry-run --id <workflow-id> --data-file test-data.json

# Full test report (validation + dry run)
python3 scripts/n8n_tester.py dry-run --id <workflow-id> --data-file test.json --report

Test Suite

# Run multiple test cases
python3 scripts/n8n_tester.py test-suite --id <workflow-id> --test-suite test-cases.json

Execution Monitoring

List Executions

# Recent executions (all workflows)
python3 scripts/n8n_api.py list-executions --limit 10 --pretty

# Specific workflow executions
python3 scripts/n8n_api.py list-executions --id <workflow-id> --limit 20 --pretty

Get Execution Details

python3 scripts/n8n_api.py get-execution --id <execution-id> --pretty

Manual Execution

# Trigger workflow
python3 scripts/n8n_api.py execute --id <workflow-id>

# Execute with data
python3 scripts/n8n_api.py execute --id <workflow-id> --data '{"key": "value"}'

Performance Optimization

Analyze Performance

# Full performance analysis
python3 scripts/n8n_optimizer.py analyze --id <workflow-id> --pretty

# Analyze specific period
python3 scripts/n8n_optimizer.py analyze --id <workflow-id> --days 30 --pretty

Get Optimization Suggestions

# Priority-ranked suggestions
python3 scripts/n8n_optimizer.py suggest --id <workflow-id> --pretty

Generate Optimization Report

# Human-readable report with metrics, bottlenecks, and suggestions
python3 scripts/n8n_optimizer.py report --id <workflow-id>

Get Workflow Statistics

# Execution statistics
python3 scripts/n8n_api.py stats --id <workflow-id> --days 7 --pretty

Python API

Basic Usage

from scripts.n8n_api import N8nClient

client = N8nClient()

# List workflows
workflows = client.list_workflows(active=True)

# Get workflow
workflow = client.get_workflow('workflow-id')

# Create workflow
new_workflow = client.create_workflow({
    'name': 'My Workflow',
    'nodes': [...],
    'connections': {...}
})

# Activate/deactivate
client.activate_workflow('workflow-id')
client.deactivate_workflow('workflow-id')

# Executions
executions = client.list_executions(workflow_id='workflow-id', limit=10)
execution = client.get_execution('execution-id')

# Execute workflow
result = client.execute_workflow('workflow-id', data={'key': 'value'})

Validation & Testing

from scripts.n8n_api import N8nClient
from scripts.n8n_tester import WorkflowTester

client = N8nClient()
tester = WorkflowTester(client)

# Validate workflow
validation = tester.validate_workflow(workflow_id='123')
print(f"Valid: {validation['valid']}")
print(f"Errors: {validation['errors']}")
print(f"Warnings: {validation['warnings']}")

# Dry run
result = tester.dry_run(
    workflow_id='123',
    test_data={'email': 'test@example.com'}
)
print(f"Status: {result['status']}")

# Test suite
test_cases = [
    {'name': 'Test 1', 'input': {...}, 'expected': {...}},
    {'name': 'Test 2', 'input': {...}, 'expected': {...}}
]
results = tester.test_suite('123', test_cases)
print(f"Passed: {results['passed']}/{results['total_tests']}")

# Generate report
report = tester.generate_test_report(validation, result)
print(report)

Performance Optimization

from scripts.n8n_optimizer import WorkflowOptimizer

optimizer = WorkflowOptimizer()

# Analyze performance
analysis = optimizer.analyze_performance('workflow-id', days=7)
print(f"Performance Score: {analysis['performance_score']}/100")
print(f"Health: {analysis['execution_metrics']['health']}")

# Get suggestions
suggestions = optimizer.suggest_optimizations('workflow-id')
print(f"Priority Actions: {len(suggestions['priority_actions'])}")
print(f"Quick Wins: {len(suggestions['quick_wins'])}")

# Generate report
report = optimizer.generate_optimization_report(analysis)
print(report)

Common Workflows

1. Validate and Test Workflow

# Validate workflow structure
python3 scripts/n8n_tester.py validate --id <workflow-id> --pretty

# Test with sample data
python3 scripts/n8n_tester.py dry-run --id <workflow-id> \
  --data '{"email": "test@example.com", "name": "Test User"}'

# If tests pass, activate
python3 scripts/n8n_api.py activate --id <workflow-id>

2. Debug Failed Workflow

# Check recent executions
python3 scripts/n8n_api.py list-executions --id <workflow-id> --limit 10 --pretty

# Get specific execution details
python3 scripts/n8n_api.py get-execution --id <execution-id> --pretty

# Validate workflow structure
python3 scripts/n8n_tester.py validate --id <workflow-id>

# Generate test report
python3 scripts/n8n_tester.py report --id <workflow-id>

# Check for optimization issues
python3 scripts/n8n_optimizer.py report --id <workflow-id>

3. Optimize Workflow Performance

# Analyze current performance
python3 scripts/n8n_optimizer.py analyze --id <workflow-id> --days 30 --pretty

# Get actionable suggestions
python3 scripts/n8n_optimizer.py suggest --id <workflow-id> --pretty

# Generate comprehensive report
python3 scripts/n8n_optimizer.py report --id <workflow-id>

# Review execution statistics
python3 scripts/n8n_api.py stats --id <workflow-id> --days 30 --pretty

# Test optimizations with dry run
python3 scripts/n8n_tester.py dry-run --id <workflow-id> --data-file test-data.json

4. Monitor Workflow Health

# Check active workflows
python3 scripts/n8n_api.py list-workflows --active true --pretty

# Review recent execution status
python3 scripts/n8n_api.py list-executions --limit 20 --pretty

# Get statistics for each critical workflow
python3 scripts/n8n_api.py stats --id <workflow-id> --pretty

# Generate health reports
python3 scripts/n8n_optimizer.py report --id <workflow-id>

Validation Checks

The testing module performs comprehensive validation:

Structure Validation

✓ Required fields present (nodes, connections)
✓ All nodes have names and types
✓ Connection targets exist
✓ No disconnected nodes (warning)

Configuration Validation

✓ Nodes requiring credentials are configured
✓ Required parameters are set
✓ HTTP nodes have URLs
✓ Webhook nodes have paths
✓ Email nodes have content

Flow Validation

✓ Workflow has trigger nodes
✓ Proper execution flow
✓ No circular dependencies
✓ End nodes identified

Optimization Analysis

The optimizer analyzes multiple dimensions:

Execution Metrics

Total executions
Success/failure rates
Health status (excellent/good/fair/poor)
Error patterns

Performance Metrics

Node count and complexity
Connection patterns
Expensive operations (API calls, database queries)
Parallel execution opportunities

Bottleneck Detection

Sequential expensive operations
High failure rates
Missing error handling
Rate limit issues

Optimization Opportunities

Parallel Execution: Identify nodes that can run concurrently
Caching: Suggest caching for repeated API calls
Batch Processing: Recommend batching for large datasets
Error Handling: Add error recovery mechanisms
Complexity Reduction: Split complex workflows
Timeout Settings: Configure execution limits

Performance Scoring

Workflows receive a performance score (0-100) based on:

Success Rate: Higher is better (50% weight)
Complexity: Lower is better (30% weight)
Bottlenecks: Fewer is better (critical: -20, high: -10, medium: -5)
Optimizations: Implemented best practices (+5 each)

Score interpretation:

90-100: Excellent - Well-optimized
70-89: Good - Minor improvements possible
50-69: Fair - Optimization recommended
0-49: Poor - Significant issues

Best Practices

Development

Plan Structure: Design workflow nodes and connections before building
Validate First: Always validate before deployment
Test Thoroughly: Use dry-run with multiple test cases
Error Handling: Add error nodes for reliability
Documentation: Comment complex logic in Code nodes

Testing

Sample Data: Create realistic test data files
Edge Cases: Test boundary conditions and errors
Incremental: Test each node addition
Regression: Retest after changes
Production-like: Use staging environment that mirrors production

Deployment

Inactive First: Deploy workflows in inactive state
Gradual Rollout: Test with limited traffic initially
Monitor Closely: Watch first executions carefully
Quick Rollback: Be ready to deactivate if issues arise
Document Changes: Keep changelog of modifications

Optimization

Baseline Metrics: Capture performance before changes
One Change at a Time: Isolate optimization impacts
Measure Results: Compare before/after metrics
Regular Reviews: Schedule monthly optimization reviews
Cost Awareness: Monitor API usage and execution costs

Maintenance

Health Checks: Weekly execution statistics review
Error Analysis: Investigate failure patterns
Performance Monitoring: Track execution times
Credential Rotation: Update credentials regularly
Cleanup: Archive or delete unused workflows

Troubleshooting

Authentication Error

Error: N8N_API_KEY not found in environment

Solution: Set environment variable:

export N8N_API_KEY="your-api-key"

Connection Error

Error: HTTP 401: Unauthorized

Solution:

Verify API key is correct
Check N8N_BASE_URL is set correctly
Confirm API access is enabled in n8n

Validation Errors

Validation failed: Node missing 'name' field

Solution: Check workflow JSON structure, ensure all required fields present

Execution Timeout

Status: timeout - Execution did not complete

Solution:

Check workflow for infinite loops
Reduce dataset size for testing
Optimize expensive operations
Set execution timeout in workflow settings

Rate Limiting

Error: HTTP 429: Too Many Requests

Solution:

Add Wait nodes between API calls
Implement exponential backoff
Use batch processing
Check API rate limits

Missing Credentials

Warning: Node 'HTTP_Request' may require credentials

Solution:

Configure credentials in n8n UI
Assign credentials to node
Test connection before activating

File Structure

~/clawd/skills/n8n/
├── SKILL.md                    # This file
├── scripts/
│   ├── n8n_api.py             # Core API client (extended)
│   ├── n8n_tester.py          # Testing & validation
│   └── n8n_optimizer.py       # Performance optimization
└── references/
    └── api.md                 # n8n API reference

API Reference

For detailed n8n REST API documentation, see references/api.md or visit: https://docs.n8n.io/api/

Support

Documentation:

n8n Official Docs: https://docs.n8n.io
n8n Community Forum: https://community.n8n.io
n8n API Reference: https://docs.n8n.io/api/

Debugging:

Use validation: python3 scripts/n8n_tester.py validate --id <workflow-id>
Check execution logs: python3 scripts/n8n_api.py get-execution --id <execution-id>
Review optimization report: python3 scripts/n8n_optimizer.py report --id <workflow-id>
Test with dry-run: python3 scripts/n8n_tester.py dry-run --id <workflow-id> --data-file test.json