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n8n Workflow Management · leoyeai | DecimalAI

leoyeai/n8n

SkillScore83Maintained

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.

DevOpsby leoyeai

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

DevOps & SRE pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/leoyeai-n8n/SKILL.md

decimalai skills pull leoyeai-n8n --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/leoyeai-n8n/SKILL.md https://app.decimal.ai/s/leoyeai-n8n/SKILL.md

Raw file: /s/leoyeai-n8n/SKILL.md ↗ · machine-readable summary at /s/leoyeai-n8n.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

23 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 50d ago

—+131% tokens0% turns83% of whole cases pass with skill (19/23)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-04	✗→✓	▲ Improved	111%	0%
case-05	✗→✓	▲ Improved	100%	0%
case-06	✗→✓	▲ Improved	182%	0%
case-07	✗→✓	▲ Improved	243%	0%
case-08	✗→✓	▲ Improved	29%	0%

Showing 5 of 23 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/n8n/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 4 files · scripts

Skill contents

scripts/

references/

SKILL.md

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:

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

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:

bash
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

Illustrative Want to see what leoyeai/n8n would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

8/8/2026, 19:03 UTC · 23 cases

Measured on 19 comparable cases — below the 22 needed to publish a lift, so this result is shown here but not on the registry card or leaderboard.

Lift

+65%

83% vs 17%

Agent turns

1.0 vs 1.0

Tokens

+131%

5,067.26 vs 2,197.95

fail → pass · 15pass → pass · 4fail → fail · 4

Per-case results (23)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-22 How do I write a custom n8n community node in TypeScript that defines custom input properties using INodeType from n8n-workflow?	pass→pass	14,679	13,170	-10%	1	1	0%	2,938	7,168	+144%	0	0	—
case-23 What is the recommended Nginx configuration block to handle SSL termination and WebSocket connections for a self-hosted n8n instance on port 5678?	pass→pass	12,313	7,001	-43%	1	1	0%	2,358	5,339	+126%	0	0	—
case-01 My n8n workflow for user onboarding (ID: wf_98765) has been running slowly and failing intermittently over the past 30 days. Could you analyze its execution history and give me a full performance assessment report? I need to see health metrics, execution bottlenecks, and ranked suggestions for boosting speed and reliability.	fail→fail	22,921	5,971	-74%	1	1	0%	4,003	4,314	+8%	0	0	—
case-02 I have a new order-processing workflow in n8n (ID: wf_43210) and I want to verify it using sample order payload data before enabling it. Please run a dry-run validation using the sample payload `{"order_id": 551, "email": "buyer@test.com"}` and return a test report showing the validation checks, pass/fail status, and step output.	fail→fail	8,921	5,660	-37%	1	1	0%	1,790	4,359	+144%	0	0	—
case-03 I need an n8n workflow definition that fetches weather data from an external API, transforms the response using custom JavaScript, and sends a notification. Rather than inserting mock instruction nodes, please write the JSON workflow configuration.	pass→pass	22,685	16,247	-28%	1	1	0%	4,984	7,882	+58%	0	0	—
case-04 I am setting up environment credentials for connecting our automation scripts to our self-hosted n8n instance at https://n8n.example.com. Where should these secrets be persisted securely, and what environment variable names are used for the key and host URL?	fail→pass	12,657	4,950	-61%	1	1	0%	2,307	4,878	+111%	0	0	—
case-05 We have over 50 workflows on our n8n server, but I only want to see the currently enabled ones in formatted terminal output. What command line invocation should I run?	fail→pass	11,431	2,349	-79%	1	1	0%	2,166	4,331	+100%	0	0	—
case-06 I have prepared a workflow payload in deployments/lead_routing.json. How can I import and register this new workflow into our n8n instance via terminal commands?	fail→pass	10,400	6,403	-38%	1	1	0%	1,816	5,120	+182%	0	0	—
case-07 Workflow wf_88192 needs to be immediately paused for emergency maintenance, and then turned back on once verified. What CLI commands handle pausing and resuming this workflow?	fail→pass	6,306	3,122	-50%	1	1	0%	1,296	4,447	+243%	0	0	—
case-08 Before deploying workflows/payment_sync.json into production, I want to perform a static structural validation check on the local JSON file to detect missing connections or unconfigured node fields.	fail→pass	17,433	2,282	-87%	1	1	0%	3,331	4,282	+29%	0	0	—
case-09 We have a set of test cases saved in tests/customer_onboarding_tests.json. How do I execute this automated test suite against n8n workflow wf_33411 to verify multiple scenario inputs?	fail→pass	14,885	5,146	-65%	1	1	0%	2,997	4,958	+65%	0	0	—
case-10 I want to inspect the last 10 execution records across all workflows on our n8n instance using our local CLI helper.	fail→pass	11,416	1,716	-85%	1	1	0%	2,050	4,187	+104%	0	0	—
case-11 Execution run exec_99201 failed unexpectedly. What CLI command fetches the detailed execution payload and node status for this run?	fail→pass	9,679	2,497	-74%	1	1	0%	1,711	4,261	+149%	0	0	—
case-12 I need to trigger a manual execution of n8n workflow wf_12345 with input payload {"user_id": 42} and observe the return data.	fail→fail	8,885	5,868	-34%	1	1	0%	1,565	4,219	+170%	0	0	—
case-13 I want a priority-ranked list of optimization recommendations and quick wins for workflow wf_55123 based on execution data.	fail→fail	6,005	5,363	-11%	1	1	0%	946	4,250	+349%	0	0	—
case-14 We need execution statistics over the last 7 days for workflow wf_77123 to calculate run counts and success rate.	fail→pass	7,436	5,666	-24%	1	1	0%	1,312	4,278	+226%	0	0	—
case-15 I am writing a Python script to audit active n8n workflows programmatically using our API helper module. How do I instantiate the client and fetch active workflows?	fail→pass	11,969	4,510	-62%	1	1	0%	2,324	4,975	+114%	0	0	—
case-16 In Python, I want to use our testing module to validate workflow wf_101 and then run a dry run with input {"email": "user@test.com"}. Show the Python code using the helper classes.	fail→pass	8,436	4,579	-46%	1	1	0%	1,536	4,919	+220%	0	0	—
case-17 How do I programmatically generate a performance score and health metrics for workflow wf_202 using our Python optimization module?	fail→pass	9,640	4,608	-52%	1	1	0%	1,735	4,863	+180%	0	0	—
case-18 An n8n workflow (wf_909) failed in production. Outline the sequence of troubleshooting commands using our helper scripts to inspect execution logs, validate structure, and generate a test report.	fail→pass	10,065	5,373	-47%	1	1	0%	1,869	4,968	+166%	0	0	—
case-19 How is the performance score (0-100) calculated for an n8n workflow in our optimization analysis? What exact percentage weight is assigned to the success rate factor?	fail→pass	8,887	4,433	-50%	1	1	0%	1,392	4,805	+245%	0	0	—
case-20 In the 0-100 workflow performance scoring model, what exact percentage weight is given to the workflow complexity metric?	fail→pass	6,632	2,068	-69%	1	1	0%	1,004	4,223	+321%	0	0	—
case-21 How do I configure Docker Compose to deploy an n8n instance using PostgreSQL for storage and Redis for queue-based scaling?	pass→pass	12,888	12,043	-7%	1	1	0%	2,635	6,394	+143%	0	0	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 19 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +65 percentage points is the difference between those two pass rates over the 19 comparable cases.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 17.4%
Pass rate with the skill: 82.6%
Cases attempted: 23
Cases counted toward lift: 19
Cases that regressed: 0
Skill version measured: v1
Run completed: 8/8/2026, 19:03 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-04

Prompt

I am setting up environment credentials for connecting our automation scripts to our self-hosted n8n instance at https://n8n.example.com. Where should these secrets be persisted securely, and what environment variable names are used for the key and host URL?

Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.

Show both answers

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run83

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Efficiency · separate axis (at equal correctness)+0% turns+107% tokens

Shown for transparency; not credited unless a verified run shows no correctness regression.

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 50 days ago

Other measured skills in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

leoyeai/n8n

SkillSafety

Source

n8n Workflow Management

⚠️ CRITICAL: Workflow Creation Rules

Setup

Quick Reference

Workflow Management

List Workflows

Get Workflow Details

Create Workflows

Activate/Deactivate

Testing & Validation

Validate Workflow Structure

Dry Run Testing

Test Suite

Execution Monitoring

List Executions

Get Execution Details

Manual Execution

Performance Optimization

Analyze Performance

Get Optimization Suggestions

Generate Optimization Report

Get Workflow Statistics

Python API

Basic Usage

Validation & Testing

Performance Optimization

Common Workflows

1. Validate and Test Workflow

2. Debug Failed Workflow

3. Optimize Workflow Performance

4. Monitor Workflow Health

Validation Checks

Structure Validation

Configuration Validation

Flow Validation

Optimization Analysis

Execution Metrics

Performance Metrics

Bottleneck Detection

Optimization Opportunities

Performance Scoring

Best Practices

Development

Testing

Deployment

Optimization

Maintenance

Troubleshooting

Authentication Error

Connection Error

Validation Errors

Execution Timeout

Rate Limiting

Missing Credentials

File Structure

API Reference

Support

How this skill was measured

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-04

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