sigmetrics-reproducibility
Use when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the analysis, measurement/trace provenance, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper proves/measures and what the artifact contains.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-reproducibility --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 Reproducibility Use this before submission and again before the POMACS camera-ready. SIGMETRICS reproducibility has a distinctive shape: the "artifact" is often a **proof plus a simulator plus a trace**, not only running code. The goal is that a competent reader could re-derive your bound, re-run your simulation to the same curves, and re-analyze your measurement to the same conclusions. ## Evidence map - Map each theorem, bound, and reported number to a **verifiable location** — a proof in an appendix, a figure regenerated from a seeded simulation, or a script that turns the trace into the table. - For analytic results, give the **full derivation and every assumption**; a reader should be able to check the proof and see which assumptions each step uses. - For simulations, ship a **seeded simulator** whose scripts regenerate each figure *and* overlay the analytic prediction, so a reviewer sees model and measurement agree. - For measurement studies, document the trace source, collection window, sanitization, and the processing scripts; archive the processed dataset or document access. - Keep the paper and the artifact **consistent**: a p99 number in the PDF that no simu
- Evidence map
- Reproducibility-claim audit
- Provenance and determinism pinning
- Degrees of reproducibility (state the one you achieved)
- Vignette: a queueing-theory-plus-measurement paper
- Consistency and camera-ready pass
- Output format
What does the sigmetrics-reproducibility skill do?
Use when strengthening ACM SIGMETRICS reproducibility, covering proofs and their assumptions as reproducible artifacts, seeded simulators whose figures regenerate and match the analysis, measurement/trace provenance, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper proves/measures and what the artifact contains.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmetrics-reproducibility --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.