sigmod-reproducibility
Use when hardening the reproducibility story of a SIGMOD submission, covering PACMMOD's expectation that code, data, scripts, and notebooks be shared, experiment provenance from config to figure, dataset and workload disclosure, variance reporting for systems numbers, and alignment with later ARI badging.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-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.
# SIGMOD Reproducibility PACMMOD's author guidelines state the culture plainly: sharing research artifacts should be the norm, and papers are expected to make code, data, scripts, and notebooks available where possible — encouraged rather than mandatory for acceptance, but reviewers read availability as a credibility signal. This skill covers reproducibility *as engineered into the paper*; the post-acceptance badge process lives in `sigmod-artifact-evaluation`. ## Provenance chain, not vibes A database paper's result is a function of code version, configuration, dataset, workload, and hardware. Reproducibility means the paper pins all five for every number it prints. | Layer | Must be recoverable from paper + artifact | Where it usually hides | |---|---|---| | Code version | Commit or tag behind each experiment | "latest" at submission time | | Configuration | Buffer sizes, thread counts, compaction/GC settings, flags | Defaults nobody recorded | | Dataset | Source, version, generator seed, scale factor | "standard benchmark data" | | Workload | Query mix, arrival pattern, skew parameters, warm/cold state | The harness script | | Hardware | CPU model, cores, RAM, storage class, net
- Provenance chain, not vibes
- Systems numbers need distributions
- Fairness to baselines is reproducibility too
- Repro debt ledger
- Pinning, mechanically
- Data you cannot publish
- Multi-round consistency
- What reviewers can check without running anything
- Output format
stamped into every experiment's log directory by the harness git -C "$ENGINE_DIR" rev-parse HEAD > meta/engine.commit sha256sum data/*.bin > meta/datasets.sha256 echo "$WORKLOAD_SEED $QUERY_MIX $SKEW" > meta/workload.params
What does the sigmod-reproducibility skill do?
Use when hardening the reproducibility story of a SIGMOD submission, covering PACMMOD's expectation that code, data, scripts, and notebooks be shared, experiment provenance from config to figure, dataset and workload disclosure, variance reporting for systems numbers, and alignment with later ARI badging.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-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 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.