icde-reproducibility
Use when strengthening reproducibility evidence for an IEEE ICDE data-engineering paper: pinning hardware, storage devices, software versions, datasets and workload generators, seeds, and variance protocol; ensuring baseline-tuning fairness; tracing figures to raw logs; and packaging supplemental material whose availability ICDE scores.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icde-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.
# ICDE Reproducibility Use this before submission and again before camera-ready. ICDE authors are expected to submit supplemental material, and its availability is weighed in the evaluation — so reproducibility is not optional polish, it is scored evidence. ## Evidence map - Map each performance claim to a **verifiable location**: a figure regenerated from logged runs, a workload script, or a documented measurement in the supplement. - **Pin the environment**: CPU, memory, storage device (the NVMe/SSD/HDD distinction changes results), OS and kernel, database/library versions, compiler flags, and any cluster topology. - **Pin the data**: dataset provenance, construction steps, scale factors, and for synthetic data the **generator with its seeds** — a workload nobody can regenerate is not reproducible. - **Pin the variance protocol**: how many runs, warm-up handling, how outliers are treated, and whether reported bars are standard deviations, confidence intervals, or percentiles. - **Document baseline tuning**: the configuration and tuning budget given to each competitor. Reproducibility here means a reader can re-run the *fair* comparison, not just your system. - **Trace figures to
- Evidence map
- Systems-reproducibility audit table
- Degrees of reproducibility
- Vignette: a throughput-plus-latency paper
- Single-blind note
- Output format
What does the icde-reproducibility skill do?
Use when strengthening reproducibility evidence for an IEEE ICDE data-engineering paper: pinning hardware, storage devices, software versions, datasets and workload generators, seeds, and variance protocol; ensuring baseline-tuning fairness; tracing figures to raw logs; and packaging supplemental material whose availability ICDE scores.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icde-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.