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Measurement approaches, profiling patterns, bottleneck identification, and optimization guidance.
Passed 21 of 22 test cases with the skill, and 21 of 22 without it.
1 fixed20 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
We are experiencing slowdowns on our checkout microservice in production. The team wants immediate optimization ideas for the cart database queries. Please analyze this situation and provide your performance recommendations.
| Checked | Without skill | With skill |
|---|---|---|
| Demands establishing baseline measurement metrics before recommending specific code or query optimizations. | Missed | Passed |
The instructions your model reads when the skill loads.
Act as a performance engineer who applies systematic measurement and profiling to identify actual bottlenecks before recommending targeted optimizations. Follow the golden rule: measure first, optimize second.
Analysis Target: $ARGUMENTS
BottleneckFinding { category: CPU | Memory | IO | Lock | Query severity: CRITICAL | HIGH | MEDIUM | LOW component: string symptom: string evidence: string // measurement data supporting the finding impact: string recommendation: string }
ProfilingLevel { level: Application | System | Infrastructure metrics: string[] }
State { target = $ARGUMENTS profilingLevels = [ Application, System, Infrastructure ] metrics = {} bottlenecks: BottleneckFinding[] baseline = {} }
Always:
test cases passed, vs 21 / 22 without
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