icde-experiments
Use when designing or auditing IEEE ICDE experiments for a data-engineering paper: workload realism, tuned baselines, scale curves over single points, throughput and tail-latency with declared variance, mechanism-isolating ablations, cost/loss disclosure, and hardware reporting that satisfies a builder-heavy committee.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icde-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.
# ICDE Experiments Use this before submission when the evaluation is not yet locked. At ICDE, the experiments are where a systems paper is won or lost; a builder-heavy committee reads them adversarially. ## Experiment audit - Map each performance claim to a **figure, table, or measured crossover** — no orphan claims. - Run **tuned baselines**, not straw men. Document the tuning budget you gave each competitor; an untuned baseline is the single most common ICDE reject trigger. - Prefer **curves over points**: show behavior across scale factors, data sizes, or contention levels, not one operating point where you happen to win. - Report **throughput and tail latency with variance** — multiple runs, and captions that say whether bars are standard deviations, confidence intervals, or percentiles. A p99 that hides behind a median is a caught omission. - Add **ablations that isolate the mechanism**: turn off the one idea and show the gain disappears, so the improvement cannot be attributed to unrelated engineering. - Disclose the **cost** of every gain: memory, write amplification, CPU, or read-latency penalty. A loss map is a credibility signal, not a weakness. - Report **hardware, OS, s
- Experiment audit
- What experiments are for at this venue
- Evidence-burden table
- Vignette: evaluating a write-optimized index
- Reporting floor
- Output format
What does the icde-experiments skill do?
Use when designing or auditing IEEE ICDE experiments for a data-engineering paper: workload realism, tuned baselines, scale curves over single points, throughput and tail-latency with declared variance, mechanism-isolating ablations, cost/loss disclosure, and hardware reporting that satisfies a builder-heavy committee.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icde-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.