socc-reproducibility
Use when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing tail-latency and cost (not just the mean), claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper reports and what the artifact regenerates.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-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.
# SoCC Reproducibility Use this before submission and again before camera-ready. SoCC — the joint SIGMOD+SIGOPS cloud symposium — is read by reviewers who expect an inspectable measurement trail: the SIGOPS half wants to believe the system runs, and the SIGMOD half wants to believe the numbers. The goal is that a competent reader with a comparable testbed could rebuild your evidence and reach your conclusions — including the **tail latency and cost**, not just the average. ## Evidence map - Map each cloud claim and reported number to a **verifiable location** — a paper section, a figure generated from logged runs, or a script in the artifact. - For systems, give enough of the mechanism, configuration, and **testbed** (node counts, instance types, OS/kernel, network) that a reader could re-deploy and re-measure. - For measurement/trace studies, report the trace provenance, extraction date, filtering, the workload replay, metrics, and the analysis scripts. - Reproduce **tail and cost**: percentiles (p95/p99/p99.9), the pricing model behind any cost claim, and the number of runs and variance — the mean alone is not a cloud result. - Keep the paper and the artifact **consistent**: a nu
- Evidence map
- Reproducibility statement audit
- Provenance pinning
- Degrees of reproducibility (state the one you achieved)
- Vignette: a scheduling measurement paper
- Consistency and camera-ready pass
- Output format
What does the socc-reproducibility skill do?
Use when strengthening ACM SoCC reproducibility, covering the testbed and workload description, released code and traces, provenance pinning for measurement studies, reproducing tail-latency and cost (not just the mean), claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper reports and what the artifact regenerates.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill socc-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.