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sigmetrics-experiments
Use when designing or auditing ACM SIGMETRICS evaluations, covering theorem-plus-validation rigor, stating and testing modeling assumptions, analysis-vs-simulation agreement, real workloads and traces, fairly tuned baselines, statistics and confidence intervals for stochastic systems, learning guarantees, measurement provenance, and matching evidence to the shape of each performance claim.
SIGMETRICS-Skills/skills/sigmetrics-experiments/SKILL.md
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# SIGMETRICS Experiments
Use this before submission when the evidence is not yet locked. SIGMETRICS reviewers are
performance-evaluation specialists; the evaluation is where a good model or measurement is won or
lost. The organizing principle is **evidence proportional to the claim** — an analytic claim needs a
proof *and* validation, a measurement claim needs a methodology a skeptic accepts, and a learning
claim needs a guarantee, not only a benchmark score.
## Evaluation audit
- **Match evidence to the claim shape.** A claim about a **bound** needs a proof plus a simulation
that shows the analytic curve is right; a claim about a **real system** needs measured data and a
documented methodology; a claim about a **learner** needs a regret/convergence guarantee; a claim
about **tail** behavior needs tail metrics (p99), not means.