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Get Started Free →n8n workflow automation patterns and API integration. This skill should be used when creating n8n workflows, using webhooks, managing workflows via REST API, or integrating n8n with MCP servers. Covers workflow JSON structure, node patterns, and automation best practices.
.claude/skills/aiskillstore-n8n/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-16 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-23 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-11 | ✓→✓ | = Same ✓ | 408% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 209% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 92% | 0% |
This skill enables creating and managing n8n workflows for automation tasks.
n8n instance running with API access:
bashN8N_HOST=localhost N8N_PORT=5678 N8N_API_KEY=your-api-key
Every n8n workflow is JSON with this structure:
json{ "name": "Workflow Name", "nodes": [], "connections": {}, "settings": { "executionOrder": "v1" } }
Each node has:
json{ "id": "unique-id", "name": "Display Name", "type": "n8n-nodes-base.nodetype", "typeVersion": 1, "position": [x, y], "parameters": {}, "credentials": {} }
Connections define data flow between nodes:
json{ "Source Node": { "main": [ [{"node": "Target Node", "type": "main", "index": 0}] ] } }
Creates an HTTP endpoint that triggers workflow execution:
json{ "name": "Webhook Handler", "nodes": [ { "id": "webhook", "name": "Webhook", "type": "n8n-nodes-base.webhook", "typeVersion": 2, "position": [250, 300], "webhookId": "my-webhook", "parameters": { "path": "my-endpoint", "httpMethod": "POST", "responseMode": "responseNode" } }, { "id": "respond", "name": "Respond", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1.1, "position": [450, 300], "parameters": { "respondWith": "json", "responseBody": "={{ $json }}" } } ], "connections": { "Webhook": { "main": [[{"node": "Respond", "type": "main", "index": 0}]] } } }
Access at: http://localhost:5678/webhook/my-endpoint
Make external API calls:
json{ "id": "http", "name": "HTTP Request", "type": "n8n-nodes-base.httpRequest", "typeVersion": 4.2, "position": [450, 300], "parameters": { "method": "POST", "url": "https://api.example.com/endpoint", "authentication": "predefinedCredentialType", "nodeCredentialType": "myApiCredential", "sendBody": true, "specifyBody": "json", "jsonBody": "={{ JSON.stringify($json) }}" }, "credentials": { "myApiCredential": {"id": "cred-id", "name": "My Credential"} } }
Route data based on conditions:
json{ "id": "if", "name": "IF", "type": "n8n-nodes-base.if", "typeVersion": 2, "position": [450, 300], "parameters": { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict" }, "conditions": [ { "leftValue": "={{ $json.status }}", "rightValue": "success", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" } } }
Process items in batches:
json{ "id": "batch", "name": "Loop Over Items", "type": "n8n-nodes-base.splitInBatches", "typeVersion": 3, "position": [450, 300], "parameters": { "batchSize": 10, "options": {} } }
bashcurl -s "http://localhost:5678/api/v1/workflows" \ -H "X-N8N-API-KEY: $N8N_API_KEY"
bashcurl -s "http://localhost:5678/api/v1/workflows/{id}" \ -H "X-N8N-API-KEY: $N8N_API_KEY"
bashcurl -s -X POST "http://localhost:5678/api/v1/workflows" \ -H "X-N8N-API-KEY: $N8N_API_KEY" \ -H "Content-Type: application/json" \ -d '{"name": "New Workflow", "nodes": [...], "connections": {...}}'
bashcurl -s -X PUT "http://localhost:5678/api/v1/workflows/{id}" \ -H "X-N8N-API-KEY: $N8N_API_KEY" \ -H "Content-Type: application/json" \ -d '{"name": "Updated", "nodes": [...], "connections": {...}}'
bashcurl -s -X POST "http://localhost:5678/api/v1/workflows/{id}/activate" \ -H "X-N8N-API-KEY: $N8N_API_KEY" curl -s -X POST "http://localhost:5678/api/v1/workflows/{id}/deactivate" \ -H "X-N8N-API-KEY: $N8N_API_KEY"
bashcurl -s "http://localhost:5678/api/v1/executions?workflowId={id}&limit=10&includeData=true" \ -H "X-N8N-API-KEY: $N8N_API_KEY"
n8n uses expressions for dynamic values:
| Syntax | Description | |--------|-------------| | ={{ $json.field }} | Access current item field | | ={{ $json.body.param }} | Access nested field | | ={{ $('Node Name').item.json.field }} | Access output from specific node | | ={{ $input.first().json }} | First input item | | ={{ $input.all() }} | All input items | | ={{ JSON.stringify($json) }} | Convert to JSON string |
| Node | Type | Purpose | |------|------|---------| | Webhook | n8n-nodes-base.webhook | HTTP trigger | | HTTP Request | n8n-nodes-base.httpRequest | API calls | | Respond to Webhook | n8n-nodes-base.respondToWebhook | Return HTTP response | | IF | n8n-nodes-base.if | Conditional branching | | Switch | n8n-nodes-base.switch | Multi-way branching | | Set | n8n-nodes-base.set | Transform data | | Code | n8n-nodes-base.code | Custom JavaScript | | Split In Batches | n8n-nodes-base.splitInBatches | Loop processing | | Merge | n8n-nodes-base.merge | Combine branches |
n8n can expose workflows as MCP tools via the built-in MCP server:
"availableInMCP": truehttp://localhost:5678/mcp-server/httpresponseMode: "responseNode" with Respond node for control/webhook-test/ path during development| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | pass→pass | 2,824 | 1,920 | -32% | 1 | 1 | 0% | 478 | 2,428 | +408% | 0 | 0 | — |
case-01 | pass→pass | 4,406 | 3,946 | -10% | 1 | 1 | 0% | 917 | 2,832 | +209% | 0 | 0 | — |
case-02 | pass→pass | 6,983 | 2,993 | -57% | 1 | 1 | 0% | 1,410 | 2,711 | +92% | 0 | 0 | — |
case-03 | pass→pass | 4,791 | 3,401 | -29% | 1 | 1 | 0% | 949 | 2,763 | +191% | 0 | 0 | — |
case-04 | fail→fail | 3,619 | 7,373 | +104% | 1 | 1 | 0% | 570 | 3,372 | +492% | 0 | 0 | — |
case-05 | pass→pass | 4,871 | 3,020 | -38% | 1 | 1 | 0% | 804 | 2,664 | +231% | 0 | 0 | — |
case-06 | pass→pass | 6,265 | 4,858 | -22% | 1 | 1 | 0% | 1,181 | 3,020 | +156% | 0 | 0 | — |
case-12 | pass→pass | 5,232 | 8,516 | +63% | 1 | 1 | 0% | 874 | 2,685 | +207% | 0 | 0 | — |
case-07 | pass→pass | 5,640 | 4,336 | -23% | 1 | 1 | 0% | 1,106 | 2,983 | +170% | 0 | 0 | — |
case-08 | pass→pass | 4,330 | 3,169 | -27% | 1 | 1 | 0% | 762 | 2,692 | +253% | 0 | 0 | — |
case-09 | pass→pass | 4,355 | 2,528 | -42% | 1 | 1 | 0% | 618 | 2,596 | +320% | 0 | 0 | — |
case-10 | pass→pass | 3,365 | 2,384 | -29% | 1 | 1 | 0% | 524 | 2,498 | +377% | 0 | 0 | — |
case-13 | pass→pass | 4,465 | 3,972 | -11% | 1 | 1 | 0% | 747 | 2,774 | +271% | 0 | 0 | — |
case-14 | pass→pass | 3,842 | 2,216 | -42% | 1 | 1 | 0% | 662 | 2,521 | +281% | 0 | 0 | — |
case-15 | pass→pass | 6,418 | 1,623 | -75% | 1 | 1 | 0% | 1,120 | 2,363 | +111% | 0 | 0 | — |
case-16 | fail→pass | 14,913 | 1,948 | -87% | 1 | 1 | 0% | 2,737 | 2,389 | -13% | 0 | 0 | — |
case-17 | pass→pass | 4,865 | 4,320 | -11% | 1 | 1 | 0% | 891 | 2,864 | +221% | 0 | 0 | — |
case-18 | pass→pass | 3,016 | 2,166 | -28% | 1 | 1 | 0% | 462 | 2,492 | +439% | 0 | 0 | — |
case-19 | pass→pass | 6,312 | 2,768 | -56% | 1 | 1 | 0% | 1,248 | 2,641 | +112% | 0 | 0 | — |
case-20 | pass→pass | 8,710 | 3,851 | -56% | 1 | 1 | 0% | 1,394 | 2,790 | +100% | 0 | 0 | — |
case-21 | pass→pass | 2,494 | 2,510 | +1% | 1 | 1 | 0% | 396 | 2,466 | +523% | 0 | 0 | — |
case-22 | pass→pass | 4,262 | 6,606 | +55% | 1 | 1 | 0% | 682 | 2,455 | +260% | 0 | 0 | — |
case-23 | fail→pass | 17,575 | 9,775 | -44% | 1 | 1 | 0% | 3,103 | 3,807 | +23% | 0 | 0 | — |
case-24 | fail→fail | 19,424 | 20,311 | +5% | 1 | 1 | 0% | 3,888 | 6,423 | +65% | 0 | 0 | — |
case-25 | fail→fail | 17,117 | 13,014 | -24% | 1 | 1 | 0% | 3,364 | 4,669 | +39% | 0 | 0 | — |
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. 25 cases were attempted. The headline lift of +8 percentage points is the difference between those two pass rates over the 25 comparable cases.
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.
Other measured skills in the registry, with their headline benchmark lift.