sigmod-experiments
Use when designing or auditing the evaluation of a SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency methodology, ablations that isolate the mechanism, and the setup disclosure a data-systems PC demands before trusting any speedup.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-experiments --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 Experiments The evaluation section decides most SIGMOD verdicts. A data-systems PC does not ask whether your system is fast; it asks whether the experiments would convince the person who built the baseline you beat. Design the evaluation to survive that specific reader. ## The setup table comes first Before any result, the paper owes a complete experimental contract: hardware (CPU, memory, storage class, network), software versions, datasets with scale, workloads with skew and mix parameters, baseline versions and tuning provenance, repetition counts, and warm-up policy. At SIGMOD this is not appendix material — reviewers skim to it before reading the design. ## Workload realism ladder | Rung | Example | Evidentiary weight | |---|---|---| | Microbenchmark | Single-operator stress loop | Explains mechanisms; proves little alone | | Standard benchmark | TPC-style, YCSB-style suites at stated scale | Comparable across papers; known blind spots | | Trace-derived | Public or characterized production traces | High, if provenance is disclosed | | End-to-end application | Full query mix on realistic schema | Highest, rarely achieved | A SIGMOD-strong evaluation climbs the ladder:
- The setup table comes first
- Workload realism ladder
- Baseline fairness protocol
- Curves, not points
- Ablations that isolate the mechanism
- Run hygiene
- Matching metrics to contribution type
- Negative-result honesty
- Output format
What does the sigmod-experiments skill do?
Use when designing or auditing the evaluation of a SIGMOD paper, covering workload realism and standard benchmark usage, baseline tuning fairness, scalability and tail-latency methodology, ablations that isolate the mechanism, and the setup disclosure a data-systems PC demands before trusting any speedup.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigmod-experiments --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.