Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› socc-writing-style
socc-writing-style
GitHub用于修订ACM SoCC论文，确保面向云规模问题，强调尾延迟与成本指标，结合系统与数据交叉贡献，提供真实部署证据，遵守匿名及12页篇幅限制。
Trigger Scenarios
修改ACM SoCC会议论文正文
优化系统性能论文的写作风格
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-writing-style -g -y
SKILL.md
Frontmatter
{
"name": "socc-writing-style",
"description": "Use when revising an ACM SoCC paper for a cloud-scale problem an operator recognizes on the first page, a contribution at the systems-and-data intersection, evidence measured on a real or realistic deployment with tail latency and cost as first-class, dual-anonymous wording, and disciplined use of the acmart 12-page budget."
}
SoCC Writing Style
Use this when revising the main paper. SoCC papers are read by a joint SIGMOD+SIGOPS audience, so they need a cloud-scale problem stated in operator terms on the first page and evidence a skeptic from either community trusts. The failure this skill prevents is a paper that reads like a pure-OS result with a cloud title glued on, or a benchmark with no systems contribution.
Revision rules
- Lead with the cloud problem, in cost and tail terms: the problem an operator or platform team recognizes, why current systems are inadequate at scale, the contribution (mechanism and/or measurement), the measured evidence, and what changes for cloud practice.
- Situate the contribution at the systems-and-data intersection. Say explicitly what is new: a mechanism, a measurement, a benchmark, a deployment lesson, or a cost model. SoCC's twin communities both want to see themselves in the paper.
- Make tail latency and cost first-class. Report p95/p99 (and p99.9 where it bites) and a concrete cost model, not just averages. A cloud paper that reports only the mean signals it has not been stress-tested the way an operator would.
- Pair every claim with measured evidence — a real or realistic deployment, a tuned baseline, a distribution rather than a point, a trace with stated provenance — not adjectives.
- State limitations honestly where the results live: single-provider traces, one region, tested regimes, the workloads you did not cover. A cloud paper's external validity is part of its claim.
- Respect the 12-page (full) / 6-page (short) budget as a design constraint; references are unlimited, but the argument and its decision-critical figures fit the body.
- Maintain dual anonymity in self-citations, system/cluster/trace names, provider hints, acknowledgements, and the availability wording.
Cloud-systems paper skeleton
| Section | Job it must do | Common failure |
|---|---|---|
| Intro | Operator problem, inadequacy at scale, contribution, evidence preview, cloud payoff — first page | Leads with a technology trend, not a problem |
| Background/Motivation | Why current cloud systems fall short here, grounded in scale | Motivation by assertion, no measured gap |
| Design / Study | The mechanism or the measurement setup, reproducibly, with the testbed/trace | Method described too thinly to re-deploy |
| Evaluation | Throughput, tail, and cost vs. tuned baselines on a real/realistic system | Mean-only metrics; simulation standing in for deployment |
| Limitations | The external-validity limits that bite (provider, region, regimes) | Generic or absent |
| Related work | Delta-first positioning across systems and data venues | Catalog of citations with no contrast |
Sentence-level rewrites
| Draft pattern | SoCC-safe rewrite |
|---|---|
| "Our system significantly improves performance." | "raises throughput X% and holds p99 within the SLO on a 200-node testbed vs. |
| "We evaluate on a large workload." | "We replay a production trace of N invocations across M functions (provenance in §3)" |
| "Results show our approach works well." | "cuts provisioned instance-seconds Y% at equal p99 (Fig. 3); limits in §6" |
| "State-of-the-art performance." | Claim scoped to the workloads, scale, and cost model actually tested |
| "It scales." | "sustains the SLO from 10 to 200 nodes; beyond that, |
Tail-and-cost discipline
[Tail] report p95/p99 (p99.9 where it bites), not just the mean; show the distribution
[Cost] state the pricing model; report instance-seconds or $ so the saving is checkable
[Baseline] tune every baseline with a documented, equal budget; an untuned baseline is a weakness
[Scale] show behavior across sizes; name the bottleneck where it stops scaling
[Limits] single-provider / one-region / tested-regime limits stated next to the results
Vignette: compressing an over-scoped systems paper
A draft with three mechanisms, twelve figures, and a sprawling background: keep the one mechanism that carries the contribution, the throughput/p99/cost figures that decide it, tuned baselines, and a limitations paragraph; move secondary regimes and full config sweeps to the artifact with forward references; cut background to the measured gap the paper closes. The test of a good cut: a reviewer should be able to answer "what did it improve, at what tail and cost, versus what baseline, and where does it stop working?" from the body alone.
Output format
[Writing diagnosis] clear / under-motivated / mean-only / over-claimed / over-scoped
[First-page fix] <new framing leading with the operator problem in cost/tail terms>
[Evidence audit] <claim -> deployment/trace -> tail+cost reported -> tuned baseline? yes/no>
[Limitations fix] <external-validity limit that bites -> where to state it>
[Anonymity edits] <system/cluster/trace names / self-citations / links to rewrite>
Version History
- 9f86f09 Current 2026-07-19 17:42
