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Get Started Free →Designs and outputs n8n workflow JSON with robust triggers, idempotency, error handling, logging, retries, and human-in-the-loop review queues. Use when you need an auditable automation that won’t silently fail.
.claude/skills/leoyeai-n8n-workflow-automation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 30% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 121% | 0% |
Designs and outputs n8n workflow JSON with robust triggers, idempotency, error handling, logging, retries, and human-in-the-loop review queues.
workflow.json (n8n importable JSON) + runbook.md (from template).Success = workflow is idempotent, logs every run, retries safely, and routes failures to a review queue.
run_id, log start/end, store status row and error details.If outputting n8n workflow JSON, conform to:
json{ "name": "<workflow name>", "nodes": [ { "name": "Trigger", "type": "n8n-nodes-base.cron", "parameters": {}, "position": [0,0] } ], "connections": {}, "settings": {}, "active": false }
Also output runbook.md using assets/runbook-template.md.
Output: Node map + workflow.json with Cron → Fetch → Aggregate → Email, plus error branches to review queue.
Output: Webhook → Validate → Process → Append status row; on error → log + notify + queue.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 9,897 | 18,126 | +83% | 1 | 1 | 0% | 1,970 | 5,033 | +155% | 0 | 0 | — |
case-10 | fail→pass | 21,551 | 28,479 | +32% | 1 | 1 | 0% | 4,213 | 6,814 | +62% | 0 | 0 | — |
case-11 | fail→fail | 20,208 | 19,075 | -6% | 1 | 1 | 0% | 3,711 | 4,998 | +35% | 0 | 0 | — |
case-01 | fail→pass | 27,195 | 26,845 | -1% | 1 | 1 | 0% | 5,658 | 6,055 | +7% | 0 | 0 | — |
case-02 | fail→fail | 28,663 | 27,835 | -3% | 1 | 1 | 0% | 6,221 | 7,186 | +16% | 0 | 0 | — |
case-03 | pass→pass | 17,415 | 16,511 | -5% | 1 | 1 | 0% | 2,811 | 4,052 | +44% | 0 | 0 | — |
case-05 | pass→pass | 16,459 | 10,414 | -37% | 1 | 1 | 0% | 3,505 | 3,280 | -6% | 0 | 0 | — |
case-06 | pass→pass | 8,425 | 9,533 | +13% | 1 | 1 | 0% | 1,675 | 2,793 | +67% | 0 | 0 | — |
case-07 | pass→pass | 11,305 | 8,569 | -24% | 1 | 1 | 0% | 2,485 | 2,864 | +15% | 0 | 0 | — |
case-08 | fail→pass | 10,517 | 11,594 | +10% | 1 | 1 | 0% | 1,757 | 2,762 | +57% | 0 | 0 | — |
case-09 | fail→fail | 16,469 | 13,033 | -21% | 1 | 1 | 0% | 2,624 | 3,212 | +22% | 0 | 0 | — |
case-12 | pass→pass | 17,961 | 19,921 | +11% | 1 | 1 | 0% | 3,205 | 4,469 | +39% | 0 | 0 | — |
case-13 | fail→pass | 10,824 | 8,507 | -21% | 1 | 1 | 0% | 1,822 | 2,375 | +30% | 0 | 0 | — |
case-14 | pass→pass | 14,963 | 17,560 | +17% | 1 | 1 | 0% | 2,372 | 3,801 | +60% | 0 | 0 | — |
case-15 | fail→pass | 16,244 | 27,851 | +71% | 1 | 1 | 0% | 3,230 | 7,144 | +121% | 0 | 0 | — |
case-16 | pass→pass | 9,945 | 11,584 | +16% | 1 | 1 | 0% | 1,691 | 2,791 | +65% | 0 | 0 | — |
case-17 | pass→pass | 10,400 | 8,872 | -15% | 1 | 1 | 0% | 1,716 | 2,498 | +46% | 0 | 0 | — |
case-18 | pass→pass | 14,759 | 11,959 | -19% | 1 | 1 | 0% | 2,834 | 3,352 | +18% | 0 | 0 | — |
case-19 | pass→pass | 19,947 | 24,512 | +23% | 1 | 1 | 0% | 4,422 | 6,235 | +41% | 0 | 0 | — |
case-20 | fail→pass | 13,528 | 10,840 | -20% | 1 | 1 | 0% | 2,360 | 2,676 | +13% | 0 | 0 | — |
case-21 | pass→pass | 9,106 | 7,463 | -18% | 1 | 1 | 0% | 1,685 | 2,150 | +28% | 0 | 0 | — |
case-22 | pass→pass | 14,422 | 12,824 | -11% | 1 | 1 | 0% | 2,791 | 3,348 | +20% | 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. 22 cases were attempted. The headline lift of +27 percentage points is the difference between those two pass rates over the 22 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.