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:

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):

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

Verify connection:

bash
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

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

Get Workflow Details

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

Create Workflows

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

Activate/Deactivate

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

Testing & Validation

Validate Workflow Structure

bash
# 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

bash
# 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

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

Execution Monitoring

List Executions

bash
# 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

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

Manual Execution

bash
# 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

bash
# 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

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

Generate Optimization Report

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

Get Workflow Statistics

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

Python API

Basic Usage

python
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

python
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

python
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

bash
# 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

bash
# 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

bash
# 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

bash
# 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

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:

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