sigmetrics-related-work
GitHub用于审计SIGMETRICS投稿的新颖性与合规性。指导作者按性能评估、系统、测量等文献赛道定位工作，采用“差异优先”而非罗列式写作，强调与最接近先验工作的对比，并处理匿名引用及版本重叠声明。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-related-work -g -y
SKILL.md
Frontmatter
{
"name": "sigmetrics-related-work",
"description": "Use when positioning an ACM SIGMETRICS submission against the performance-evaluation literature across SIGMETRICS\/POMACS, Performance Evaluation, QUESTA, TON, and the systems\/learning\/measurement neighbors (NSDI\/OSDI, IMC, NeurIPS\/ICML), writing delta-first contrast rather than a citation catalog, keeping self-citations double-anonymous, and handling concurrent and prior-version overlap."
}
SIGMETRICS Related Work
Use this to audit novelty and eligibility. SIGMETRICS reviewers are close to the performance-evaluation literature and expect to see where your paper sits relative to the nearest prior model, bound, or measurement — stated as a delta, not a list. Reopen the current call for the simultaneous-submission and prior-publication rules (a paper under one-shot revision counts as under submission) before advising authors.
Positioning checks
- Separate the analytic/measurement novelty from the engineering effort. What is new: a tighter bound, a more general model, a policy that provably beats a known one, a measurement of a system nobody had characterized, or a learning algorithm with a new guarantee?
- Cover the performance-evaluation lanes (see the table), not just the papers nearest your method. A bibliography missing the obvious queueing-theory predecessor or the prior measurement of the same system reads as unaware.
- Write delta-first. Each closely related paper gets one sentence naming what it did and one naming what you do differently — a tighter bound, a weaker assumption, a broader policy class, a larger/newer measurement — not a summary.
- Preserve double-anonymity. Cite your own prior work in the third person and never link reviewers to an identity-revealing preprint, system page, or repository (Operational Systems Track excepted).
- Declare overlap with any prior conference/workshop version or concurrent submission; do not re-submit archival work as new.
Performance-evaluation literature lanes
| Lane | Typical venues | What SIGMETRICS reviewers check |
|---|---|---|
| Core performance evaluation | SIGMETRICS/POMACS, Performance Evaluation, QUESTA | Whether the nearest model/bound/measurement is compared or distinguished |
| Systems (when you claim a systems payoff) | NSDI, OSDI, SIGCOMM, ATC | Whether the system you improve/measure is credited and fairly baselined |
| Measurement | IMC, PAM, INFOCOM | Whether prior measurements of the same system/workload are engaged |
| Learning (Learning track) | NeurIPS, ICML, COLT | Whether the learning-theoretic predecessor (regret bounds, algorithms) is cited to its origin |
| Networking/queueing journals | IEEE/ACM TON, QUESTA, Stochastic Models | Whether deeper journal-length analyses of the model are engaged |
A bibliography that cites only your own subarea tells a reviewer the delta may be smaller than claimed; one that reaches the neighboring theory, systems, and measurement venues signals command of the field.
Delta-first positioning vignette
Suppose the paper proves a tail-latency bound for a rank-based scheduler. Its nearest neighbors: a prior analysis of a single age-based policy (one policy, mean latency), a general scheduling framework (broad class, but no tail bound), and a measurement study of the target system (data, no policy analysis). The novelty sentence should name all three contrasts — a tail bound where the single-policy analysis gave only mean, a provable tail guarantee where the framework gave none, and a policy with analysis where the measurement gave only characterization.
Concurrent and prior-version judgment calls
[Concurrent arXiv work] cite neutrally, state the technical difference (tighter bound? weaker
assumption? newer measurement?), avoid unverifiable priority claims;
keep the citation double-anonymous
[Your workshop version] usually non-archival and citable, but confirm against the current call
wording and phrase so anonymity survives
[Prior short version] declare the overlap and state what the full paper adds (proofs, validation)
[Paper under one-shot revision] it is under submission to SIGMETRICS -- do not submit it elsewhere
before withdrawing
Eligibility red flags
- Substantial text/result overlap with a published paper by the same authors (self-plagiarism risk).
- A "new" analysis that re-derives a known bound without a tighter result or weaker assumption.
- Citations exclusively to non-performance-evaluation venues, signaling the paper may be a systems or learning paper rerouted without reframing.
Output format
[Eligibility] clear / needs declaration / risky
[Lanes covered] <performance-eval / systems / measurement / learning / journals>
[Nearest 3 works] <work -> one-line delta (tighter bound / weaker assumption / broader class / newer data)>
[Archival-overlap risk] <none / declare: what>
[Novelty sentence] <SIGMETRICS-ready contribution contrast against the nearest prior work>
Version History
- 9f86f09 Current 2026-07-19 17:37
