Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› sigmetrics-experiments
sigmetrics-experiments
GitHub专为ACM SIGMETRICS论文设计，用于实验审计与评估。确保证据强度匹配声明类型（如定理需证明+验证，测量需真实数据），检查建模假设、基线公平性及统计显著性，避免常见拒稿陷阱。
Trigger Scenarios
SIGMETRICS论文提交前审查
需要验证性能声明的证据充分性
检查模型假设与真实工作负载的一致性
评估统计分析方法是否严谨
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-experiments -g -y
SKILL.md
Frontmatter
{
"name": "sigmetrics-experiments",
"description": "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 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 they disagree, the model or the proof is wrong, and it is better to find that yourself.
- Use real workloads/traces, sampled or selected by a stated criterion, and describe their provenance. Toy inputs invite the "does this hold on real systems?" reject.
- Choose fair baselines, including the strongest prior policy/algorithm and a simple-but-reasonable alternative, tuned with a documented, equal budget. An untuned baseline is a scored weakness.
- Report statistics for stochastic systems: confidence intervals over independent runs, the number of runs and the source of variance, warm-up/steady-state handling for simulations, and effect sizes for comparisons. A single run with no interval is not evidence.
- For learning contributions, report the guarantee (regret/convergence/sample complexity) and validate it empirically; leaderboard numbers alone route to an ML venue.
Claim-to-evidence design table
| Performance claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Our policy bounds the tail" | Proof under stated assumptions + simulation matching the analytic p99 with CIs | "A p99 plot with no analytic comparison" |
| "The model captures the system" | Distribution fit to a real trace + goodness-of-fit | "Assumed M/G/1, never checked against the workload" |
| "We beat the prior policy" | Both tuned with equal budget; effect sizes + CIs on real workloads | "Untuned baseline; toy inputs" |
| "The algorithm has low regret" | Regret bound (proof) + empirical regret curve vs. the bound | "Benchmark score with no guarantee" |
| "Scales to realistic load" | Metrics across realistic arrival rates/sizes with variance reported | "Only light load tested" |
Validation floor for analytic results
[Proof] full derivation (appendix within the reviewed pages); every case and assumption stated
[Simulation] seeded, steady-state-aware; overlay the analytic prediction; report CIs and #runs
[Agreement] quantify the gap between analysis and simulation; explain any discrepancy
[Robustness] stress an assumption (heavy tails, estimation error) and bound the degradation
Provenance floor for measurement studies
- Record the trace/data source, the collection window, sanitization/anonymization, and access terms; archive the processed dataset (or document access), not just the collection script.
- State inclusion/exclusion criteria and the resulting sample, with the filtering script in the artifact.
- Report how outliers, gaps, and measurement artifacts were handled — silent inclusion skews every downstream metric.
Vignette: evaluating a scheduling policy
Suppose the paper claims a new policy provably reduces p99 latency versus a size-aware optimal. The matching plan: prove the bound under stated assumptions; fit the service-time distribution to a real trace and show the fit; simulate both policies with logged seeds and steady-state handling, overlay the analytic p99 on the simulated p99 with confidence intervals; run a trace-driven evaluation with both policies tuned equally, reporting effect sizes; and quantify the degradation under fetch-size estimation error — every number traceable to a logged run in the artifact.
Statistical reporting floor
- Confidence intervals and the number of independent runs for every stochastic measurement.
- Steady-state / warm-up handling stated for simulations.
- The compute and workload scale actually used, not vague feasibility language.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: proof? / validation? / measurement? / guarantee?>
[Assumption check] <assumption -> fit to real workload? goodness-of-fit shown?>
[Analysis-vs-measurement] <agreement shown with CIs? discrepancy explained? yes/no>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Decision-critical next run] <one experiment, proof case, or validation to add>
Version History
- 9f86f09 Current 2026-07-19 17:36
