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Get Started Free →Configure use when you need to work with auto-scaling. This skill provides auto-scaling configuration with comprehensive guidance and automation. Trigger with phrases like "configure auto-scaling", "set up elastic scaling", or "implement scaling".
.claude/skills/jeremylongshore-configuring-auto-scaling-policies/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-19 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 179% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 196% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 40% | 0% |
Configure auto-scaling policies for cloud workloads across AWS Auto Scaling Groups, GCP Managed Instance Groups, Azure VMSS, and Kubernetes Horizontal Pod Autoscaler (HPA). Generate scaling configurations based on CPU, memory, request rate, or custom metrics with appropriate thresholds, cooldown periods, and scale-in protection.
aws, gcloud, or az)kubectl configured with cluster access and metrics-server deployed| Error | Cause | Solution | |-------|-------|---------| | No scaling activity despite high load | Metric not reaching threshold or cooldown active | Verify metric source in CloudWatch/Stackdriver; check cooldown timer with describe-scaling-activities | | Scaling too aggressively (flapping) | Cooldown too short or threshold too sensitive | Increase cooldown period and widen the gap between scale-out and scale-in thresholds | | Max capacity reached | Instance/pod limit hit during traffic spike | Raise max_size or implement request queuing as a backpressure mechanism | | HPA unable to compute replica count | Metrics server not deployed or metric unavailable | Install metrics-server and verify kubectl top pods returns data | | FailedScaleUp: insufficient capacity | Cloud provider out of capacity in selected AZ/region | Add multiple AZs to the ASG or use mixed instance types with allocation strategy |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 2,316 | 2,473 | +7% | 1 | 1 | 0% | 472 | 1,318 | +179% | 0 | 0 | — |
case-01 | pass→pass | 2,505 | 3,001 | +20% | 1 | 1 | 0% | 481 | 1,426 | +196% | 0 | 0 | — |
case-02 | pass→pass | 11,288 | 11,518 | +2% | 1 | 1 | 0% | 2,127 | 2,984 | +40% | 0 | 0 | — |
case-03 | pass→pass | 5,009 | 4,578 | -9% | 1 | 1 | 0% | 934 | 1,876 | +101% | 0 | 0 | — |
case-05 | pass→pass | 7,621 | 9,121 | +20% | 1 | 1 | 0% | 1,443 | 2,560 | +77% | 0 | 0 | — |
case-06 | pass→pass | 5,246 | 6,137 | +17% | 1 | 1 | 0% | 1,201 | 2,202 | +83% | 0 | 0 | — |
case-07 | pass→pass | 6,129 | 6,458 | +5% | 1 | 1 | 0% | 973 | 2,186 | +125% | 0 | 0 | — |
case-08 | pass→pass | 8,996 | 8,773 | -2% | 1 | 1 | 0% | 1,464 | 2,747 | +88% | 0 | 0 | — |
case-09 | pass→pass | 5,315 | 5,116 | -4% | 1 | 1 | 0% | 961 | 1,789 | +86% | 0 | 0 | — |
case-10 | pass→pass | 6,161 | 7,925 | +29% | 1 | 1 | 0% | 1,235 | 2,462 | +99% | 0 | 0 | — |
case-11 | pass→pass | 3,563 | 3,678 | +3% | 1 | 1 | 0% | 623 | 1,520 | +144% | 0 | 0 | — |
case-12 | pass→pass | 11,849 | 13,047 | +10% | 1 | 1 | 0% | 2,478 | 3,523 | +42% | 0 | 0 | — |
case-13 | pass→pass | 8,456 | 9,220 | +9% | 1 | 1 | 0% | 1,583 | 2,402 | +52% | 0 | 0 | — |
case-14 | pass→pass | 10,373 | 10,960 | +6% | 1 | 1 | 0% | 1,636 | 2,781 | +70% | 0 | 0 | — |
case-15 | pass→pass | 3,549 | 3,411 | -4% | 1 | 1 | 0% | 633 | 1,572 | +148% | 0 | 0 | — |
case-16 | pass→pass | 3,721 | 3,703 | -0% | 1 | 1 | 0% | 838 | 1,657 | +98% | 0 | 0 | — |
case-17 | pass→pass | 3,966 | 4,027 | +2% | 1 | 1 | 0% | 747 | 1,684 | +125% | 0 | 0 | — |
case-18 | pass→pass | 9,345 | 9,179 | -2% | 1 | 1 | 0% | 1,705 | 2,631 | +54% | 0 | 0 | — |
case-19 | fail→pass | 7,672 | 5,866 | -24% | 1 | 1 | 0% | 1,490 | 1,957 | +31% | 0 | 0 | — |
case-20 | pass→pass | 8,094 | 3,654 | -55% | 1 | 1 | 0% | 1,435 | 1,507 | +5% | 0 | 0 | — |
case-21 | pass→pass | 5,231 | 3,918 | -25% | 1 | 1 | 0% | 859 | 1,420 | +65% | 0 | 0 | — |
case-22 | fail→pass | 10,098 | 7,589 | -25% | 1 | 1 | 0% | 1,660 | 2,198 | +32% | 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 +9 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.