Agent skill · Code Review & Quality

sigmetrics-review-process

Use when reasoning about how an ACM SIGMETRICS submission is evaluated, covering the hybrid conference-journal model, double-anonymous review, the three first-round outcomes (Accept-with-shepherding / One-Shot Revision / Reject), the one-shot revision resubmitted to a subsequent rolling deadline, the 12-month resubmission bar, and how SIGMETRICS differs from IMC, SIGCOMM, and NSDI.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-review-process --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: SIGMETRICS-Skills/skills/sigmetrics-review-process/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# SIGMETRICS Review Process Model the pipeline before interpreting any single review. SIGMETRICS's process is a **hybrid of the conference and journal models**: papers are POMACS articles, and the first-round decision is one of **three** outcomes, not a binary accept/reject. The most consequential mental shift for authors arriving from a plain conference is that **One-Shot Revision** is a real revise-and-resubmit round — but, unlike an open-ended journal R&R, it is **single-shot** and re-reviewed against an explicit list of required changes. ## Process model - Submission and review run on **HotCRP**, one site per rolling deadline, with **double-anonymous** review (the Operational Systems Track may reveal the deploying org/system). - Reviewers weigh the **rigor of the model or measurement**, the **correctness of proofs**, the **validity of assumptions**, the fairness and soundness of any empirical/simulation evidence, novelty, and reproducibility. SIGMETRICS reviewers check theorems, not just plots. - **Three first-round outcomes:** - **Accept** — every accepted paper is **shepherded**, so reviewer-required changes are incorporated into the final POMACS version. - **One-Shot Revisio

What's inside
Steps it walks through
  1. Process model
  2. Reading a decision against the categories
  3. The one-shot-revision mechanics (the distinctive SIGMETRICS move)
  4. How SIGMETRICS differs from its siblings
  5. Who reads you
  6. Where author leverage actually exists
  7. Misreadings to avoid
  8. Output format
More from Awesome-Journal-Skills
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About this skill
What does the sigmetrics-review-process skill do?

Use when reasoning about how an ACM SIGMETRICS submission is evaluated, covering the hybrid conference-journal model, double-anonymous review, the three first-round outcomes (Accept-with-shepherding / One-Shot Revision / Reject), the one-shot revision resubmitted to a subsequent rolling deadline, the 12-month resubmission bar, and how SIGMETRICS differs from IMC, SIGCOMM, and NSDI.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-review-process --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 909 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.

Keep going