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Get Started Free →Incident response runbook for CoreWeave GPU workload failures. Use when inference services are down, GPUs are unavailable, or responding to production incidents on CoreWeave. Trigger with phrases like "coreweave incident", "coreweave outage", "coreweave runbook", "coreweave service down".
.claude/skills/jeremylongshore-coreweave-incident-runbook/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -45% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -30% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 18% | 0% |
> Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
bash# 1. Check pod status kubectl get pods -l app=inference -o wide # 2. Check recent events kubectl get events --sort-by=.lastTimestamp | tail -20 # 3. Check node status kubectl get nodes -l gpu.nvidia.com/class -o wide # 4. Check GPU health kubectl exec -it $(kubectl get pod -l app=inference -o name | head -1) -- nvidia-smi
bashkubectl rollout undo deployment/inference
For data handling, see coreweave-data-handling.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 23,430 | 15,116 | -35% | 1 | 1 | 0% | 4,094 | 3,198 | -22% | 0 | 0 | — |
case-02 | fail→pass | 14,596 | 9,769 | -33% | 1 | 1 | 0% | 2,714 | 2,021 | -26% | 0 | 0 | — |
case-03 | fail→pass | 13,237 | 5,210 | -61% | 1 | 1 | 0% | 2,427 | 1,340 | -45% | 0 | 0 | — |
case-04 | pass→pass | 15,169 | 7,187 | -53% | 1 | 1 | 0% | 2,955 | 1,749 | -41% | 0 | 0 | — |
case-05 | pass→pass | 10,063 | 5,975 | -41% | 1 | 1 | 0% | 1,801 | 1,594 | -11% | 0 | 0 | — |
case-11 | pass→pass | 6,666 | 2,009 | -70% | 1 | 1 | 0% | 1,387 | 667 | -52% | 0 | 0 | — |
case-06 | pass→pass | 13,393 | 9,316 | -30% | 1 | 1 | 0% | 2,564 | 2,200 | -14% | 0 | 0 | — |
case-07 | fail→pass | 13,905 | 7,810 | -44% | 1 | 1 | 0% | 2,358 | 1,656 | -30% | 0 | 0 | — |
case-08 | pass→pass | 12,291 | 3,781 | -69% | 1 | 1 | 0% | 2,102 | 840 | -60% | 0 | 0 | — |
case-09 | pass→fail | 15,688 | 11,838 | -25% | 1 | 1 | 0% | 2,870 | 2,520 | -12% | 0 | 0 | — |
case-10 | fail→pass | 3,551 | 1,990 | -44% | 1 | 1 | 0% | 599 | 705 | +18% | 0 | 0 | — |
case-12 | fail→pass | 9,688 | 4,057 | -58% | 1 | 1 | 0% | 1,826 | 1,069 | -41% | 0 | 0 | — |
case-13 | fail→pass | 9,288 | 3,655 | -61% | 1 | 1 | 0% | 1,785 | 1,027 | -42% | 0 | 0 | — |
case-14 | pass→pass | 8,242 | 1,860 | -77% | 1 | 1 | 0% | 1,326 | 712 | -46% | 0 | 0 | — |
case-15 | pass→pass | 4,349 | 1,491 | -66% | 1 | 1 | 0% | 309 | 577 | +87% | 0 | 0 | — |
case-21 | pass→pass | 5,950 | 1,883 | -68% | 1 | 1 | 0% | 845 | 655 | -22% | 0 | 0 | — |
case-16 | pass→pass | 14,059 | 2,478 | -82% | 1 | 1 | 0% | 2,363 | 660 | -72% | 0 | 0 | — |
case-17 | pass→pass | 11,130 | 1,830 | -84% | 1 | 1 | 0% | 1,761 | 650 | -63% | 0 | 0 | — |
case-18 | pass→pass | 8,916 | 3,054 | -66% | 1 | 1 | 0% | 1,459 | 637 | -56% | 0 | 0 | — |
case-19 | pass→pass | 7,560 | 1,930 | -74% | 1 | 1 | 0% | 1,305 | 615 | -53% | 0 | 0 | — |
case-20 | pass→pass | 9,503 | 2,077 | -78% | 1 | 1 | 0% | 1,531 | 708 | -54% | 0 | 0 | — |
case-22 | pass→pass | 12,811 | 1,600 | -88% | 1 | 1 | 0% | 2,197 | 578 | -74% | 0 | 0 | — |
case-23 | fail→pass | 10,411 | 1,790 | -83% | 1 | 1 | 0% | 1,817 | 631 | -65% | 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. 23 cases were attempted. The headline lift of +30 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.