Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Execute this skill enables AI assistant to create intelligent alerting rules for proactive performance monitoring. it is triggered when the user requests to "create alerts", "define monitoring rules", or "set up alerting". the skill helps define thresholds, rou... Use when generating or creating new content. Trigger with phrases like 'generate', 'create', or 'scaffold'.
.claude/skills/jeremylongshore-creating-alerting-rules/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-19 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-16 | ✓→✗ | ▼ Worse | 33% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 2% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 36% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 45% | 0% |
Create production-ready alerting rules for Prometheus, Grafana, and PagerDuty with intelligent thresholds, routing policies, and escalation procedures.
This skill automates the creation of comprehensive alerting rules, reducing the manual effort required for performance monitoring. It guides you through defining alert categories, setting intelligent thresholds, and configuring routing and escalation policies. The skill also helps generate runbooks and establish alert testing procedures.
This skill activates when you need to:
User request: "create latency alerts for the payment service"
The skill will:
User request: "set up alerting for error rate increases in the API gateway"
The skill will:
This skill can be integrated with other Claude Code plugins to automate incident response workflows. For example, it can trigger automated remediation actions or create tickets in an issue tracking system.
The skill produces structured output relevant to the task.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 13,956 | 10,211 | -27% | 1 | 1 | 0% | 2,580 | 2,637 | +2% | 0 | 0 | — |
case-02 | pass→pass | 16,627 | 18,089 | +9% | 1 | 1 | 0% | 3,106 | 4,218 | +36% | 0 | 0 | — |
case-03 | pass→pass | 12,440 | 12,668 | +2% | 1 | 1 | 0% | 2,151 | 3,109 | +45% | 0 | 0 | — |
case-04 | pass→pass | 14,586 | 14,491 | -1% | 1 | 1 | 0% | 2,733 | 3,569 | +31% | 0 | 0 | — |
case-05 | pass→pass | 14,521 | 12,940 | -11% | 1 | 1 | 0% | 2,689 | 3,022 | +12% | 0 | 0 | — |
case-06 | pass→pass | 11,304 | 12,220 | +8% | 1 | 1 | 0% | 2,001 | 2,737 | +37% | 0 | 0 | — |
case-07 | pass→pass | 15,829 | 19,323 | +22% | 1 | 1 | 0% | 3,288 | 4,939 | +50% | 0 | 0 | — |
case-08 | pass→pass | 13,562 | 14,181 | +5% | 1 | 1 | 0% | 2,704 | 3,408 | +26% | 0 | 0 | — |
case-09 | pass→pass | 16,876 | 19,027 | +13% | 1 | 1 | 0% | 3,206 | 4,435 | +38% | 0 | 0 | — |
case-10 | pass→pass | 12,409 | 13,872 | +12% | 1 | 1 | 0% | 2,200 | 3,292 | +50% | 0 | 0 | — |
case-11 | pass→pass | 14,051 | 11,556 | -18% | 1 | 1 | 0% | 2,616 | 2,823 | +8% | 0 | 0 | — |
case-12 | pass→pass | 11,009 | 13,656 | +24% | 1 | 1 | 0% | 2,037 | 3,163 | +55% | 0 | 0 | — |
case-13 | pass→pass | 12,944 | 15,745 | +22% | 1 | 1 | 0% | 2,475 | 3,732 | +51% | 0 | 0 | — |
case-14 | pass→pass | 13,075 | 11,863 | -9% | 1 | 1 | 0% | 2,285 | 2,753 | +20% | 0 | 0 | — |
case-15 | pass→pass | 12,692 | 11,481 | -10% | 1 | 1 | 0% | 2,276 | 2,631 | +16% | 0 | 0 | — |
case-16 | pass→fail | 10,818 | 11,591 | +7% | 1 | 1 | 0% | 2,112 | 2,813 | +33% | 0 | 0 | — |
case-17 | pass→pass | 17,948 | 16,022 | -11% | 1 | 1 | 0% | 3,799 | 4,045 | +6% | 0 | 0 | — |
case-18 | pass→pass | 12,634 | 15,852 | +25% | 1 | 1 | 0% | 2,721 | 4,159 | +53% | 0 | 0 | — |
case-19 | fail→pass | 26,232 | 19,580 | -25% | 1 | 1 | 0% | 2,387 | 3,696 | +55% | 0 | 0 | — |
case-20 | fail→fail | 17,544 | 22,478 | +28% | 1 | 1 | 0% | 3,229 | 3,766 | +17% | 0 | 0 | — |
case-21 | pass→pass | 17,321 | 14,628 | -16% | 1 | 1 | 0% | 2,468 | 3,058 | +24% | 0 | 0 | — |
case-22 | pass→pass | 21,736 | 14,263 | -34% | 1 | 1 | 0% | 2,375 | 3,030 | +28% | 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 0 percentage points is the difference between those two pass rates over the 22 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.