n8n

---

name: n8n description: 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. metadata: {"openclaw":{"emoji":"\u2699\ufe0f","requires":{"env":["N8N_API_KEY","N8N_BASE_URL"]},"primaryEnv":"N8N_API_KEY"}}

---

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:

1. ✅ Generate COMPLETE workflows with all functional nodes 2. ✅ Include actual HTTP Request nodes for API calls (ImageFX, Gemini, Veo, Suno, etc.) 3. ✅ Add Code nodes for data transformation and logic 4. ✅ Create proper connections between all nodes 5. ✅ 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  --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 
python3 scripts/n8n_api.py deactivate --id 

Testing & Validation

#### Validate Workflow Structure

# Validate existing workflow
python3 scripts/n8n_tester.py validate --id 
Validate from file
python3 scripts/n8n_tester.py validate --file workflow.json --pretty
Generate validation report
python3 scripts/n8n_tester.py report --id 

#### Dry Run Testing

# Test with data
python3 scripts/n8n_tester.py dry-run --id  --data '{"email": "test@example.com"}'
Test with data file
python3 scripts/n8n_tester.py dry-run --id  --data-file test-data.json
Full test report (validation + dry run)
python3 scripts/n8n_tester.py dry-run --id  --data-file test.json --report

#### Test Suite

# Run multiple test cases
python3 scripts/n8n_tester.py test-suite --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  --limit 20 --pretty

#### Get Execution Details

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

#### Manual Execution

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

Performance Optimization

#### Analyze Performance

# Full performance analysis
python3 scripts/n8n_optimizer.py analyze --id  --pretty
Analyze specific period
python3 scripts/n8n_optimizer.py analyze --id  --days 30 --pretty

#### Get Optimization Suggestions

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

#### Generate Optimization Report

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

#### Get Workflow Statistics

# Execution statistics
python3 scripts/n8n_api.py stats --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  --pretty
Test with sample data
python3 scripts/n8n_tester.py dry-run --id  \
  --data '{"email": "test@example.com", "name": "Test User"}'
If tests pass, activate
python3 scripts/n8n_api.py activate --id 

2. Debug Failed Workflow

# Check recent executions
python3 scripts/n8n_api.py list-executions --id  --limit 10 --pretty
Get specific execution details
python3 scripts/n8n_api.py get-execution --id  --pretty
Validate workflow structure
python3 scripts/n8n_tester.py validate --id 
Generate test report
python3 scripts/n8n_tester.py report --id 
Check for optimization issues
python3 scripts/n8n_optimizer.py report --id 

3. Optimize Workflow Performance

# Analyze current performance
python3 scripts/n8n_optimizer.py analyze --id  --days 30 --pretty
Get actionable suggestions
python3 scripts/n8n_optimizer.py suggest --id  --pretty
Generate comprehensive report
python3 scripts/n8n_optimizer.py report --id 
Review execution statistics
python3 scripts/n8n_api.py stats --id  --days 30 --pretty
Test optimizations with dry run
python3 scripts/n8n_tester.py dry-run --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  --pretty
Generate health reports
python3 scripts/n8n_optimizer.py report --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

1. Plan Structure: Design workflow nodes and connections before building 2. Validate First: Always validate before deployment 3. Test Thoroughly: Use dry-run with multiple test cases 4. Error Handling: Add error nodes for reliability 5. Documentation: Comment complex logic in Code nodes

Testing

1. Sample Data: Create realistic test data files 2. Edge Cases: Test boundary conditions and errors 3. Incremental: Test each node addition 4. Regression: Retest after changes 5. Production-like: Use staging environment that mirrors production

Deployment

1. Inactive First: Deploy workflows in inactive state 2. Gradual Rollout: Test with limited traffic initially 3. Monitor Closely: Watch first executions carefully 4. Quick Rollback: Be ready to deactivate if issues arise 5. Document Changes: Keep changelog of modifications

Optimization

1. Baseline Metrics: Capture performance before changes 2. One Change at a Time: Isolate optimization impacts 3. Measure Results: Compare before/after metrics 4. Regular Reviews: Schedule monthly optimization reviews 5. Cost Awareness: Monitor API usage and execution costs

Maintenance

1. Health Checks: Weekly execution statistics review 2. Error Analysis: Investigate failure patterns 3. Performance Monitoring: Track execution times 4. Credential Rotation: Update credentials regularly 5. 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: 1. Verify API key is correct 2. Check N8N_BASE_URL is set correctly 3. 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: 1. Check workflow for infinite loops 2. Reduce dataset size for testing 3. Optimize expensive operations 4. Set execution timeout in workflow settings

Rate Limiting

Error: HTTP 429: Too Many Requests

Solution: 1. Add Wait nodes between API calls 2. Implement exponential backoff 3. Use batch processing 4. Check API rate limits

Missing Credentials

Warning: Node 'HTTP_Request' may require credentials

Solution: 1. Configure credentials in n8n UI 2. Assign credentials to node 3. 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: 1. Use validation: python3 scripts/n8n_tester.py validate --id 2. Check execution logs: python3 scripts/n8n_api.py get-execution --id 3. Review optimization report: python3 scripts/n8n_optimizer.py report --id 4. Test with dry-run: python3 scripts/n8n_tester.py dry-run --id --data-file test.json

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.

Development

1. Plan Structure: Design workflow nodes and connections before building 2. Validate First: Always validate before deployment 3. Test Thoroughly: Use dry-run with multiple test cases 4. Error Handling: Add error nodes for reliability 5. Documentation: Comment complex logic in Code nodes

Testing

1. Sample Data: Create realistic test data files 2. Edge Cases: Test boundary conditions and errors 3. Incremental: Test each node addition 4. Regression: Retest after changes 5. Production-like: Use staging environment that mirrors production

Deployment

1. Inactive First: Deploy workflows in inactive state 2. Gradual Rollout: Test with limited traffic initially 3. Monitor Closely: Watch first executions carefully 4. Quick Rollback: Be ready to deactivate if issues arise 5. Document Changes: Keep changelog of modifications

Optimization

1. Baseline Metrics: Capture performance before changes 2. One Change at a Time: Isolate optimization impacts 3. Measure Results: Compare before/after metrics 4. Regular Reviews: Schedule monthly optimization reviews 5. Cost Awareness: Monitor API usage and execution costs

Maintenance

1. Health Checks: Weekly execution statistics review 2. Error Analysis: Investigate failure patterns 3. Performance Monitoring: Track execution times 4. Credential Rotation: Update credentials regularly 5. Cleanup: Archive or delete unused workflows