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Design observability strategies: SLI/SLO frameworks, alerting, and dashboards.
Passed 17 of 21 test cases with the skill, and 9 of 21 without it.
and 7 more in the full report
9 fixed9 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have an existing alerts.json file containing dozens of legacy Alertmanager definitions. What exact CLI command analyzes this file for duplicates and noise without making modifications?
| Checked | Without skill | With skill |
|---|---|---|
| Executes `python scripts/alert_optimizer.py --input alerts.json --analyze-only`. | Missed | Passed |
The instructions your model reads when the skill loads.
Design production-ready observability strategies that combine the three pillars (metrics, logs, traces) with SLI/SLO frameworks, golden-signals monitoring, multi-window burn-rate alerting, and alert-noise optimization.
Before designing the observability strategy, confirm these inputs. If any is unknown or vague, ASK — do not assume:
slo_designer flags)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command |
|---|---|---|
slo_designer.py | Generate SLI/SLO framework, error budgets, and burn-rate alerts from a service definition | python scripts/slo_designer.py --service-type api --criticality high --user-facing true |
alert_optimizer.py | Analyze alert configs for noise, coverage gaps, and duplicates; emit an optimization report | python scripts/alert_optimizer.py --input alerts.json --analyze-only |
dashboard_generator.py | Produce Grafana-compatible dashboard JSON with golden signals and role-based views | python scripts/dashboard_generator.py --service-type api --name "Payment Service" |
test cases passed, vs 9 / 21 without
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