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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. - **State and test the modeling assumptions.** Fit the arrival/service distributions to the real workload (QQ-plots, goodness-of-fit); a theorem whose M/G/1 assumption is never checked against the target system invites the "unrealistic model" reject. - **Show analysis-vs-simulation agreement.** Overlay the analytic prediction on simulated measurements; if th
- Evaluation audit
- Claim-to-evidence design table
- Validation floor for analytic results
- Provenance floor for measurement studies
- Vignette: evaluating a scheduling policy
- Statistical reporting floor
- Output format
What does the sigmetrics-experiments skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.