edbt-experiments
Use when designing or auditing EDBT empirical evaluations for database-systems work, covering real workloads and datasets, fair and tuned baselines, honest measurement across realistic scales, reproducible harnesses, and the higher bar of the Experiments & Analysis paper where the measurement itself is the contribution.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill edbt-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.
# EDBT Experiments Use this before submission when the evaluation is not yet locked. EDBT reviewers are database-systems empiricists; the evaluation is where a good idea is won or lost. The organizing principle is **evidence proportional to the claim** — the study must measure the thing the paper actually asserts, on workloads, datasets, and scales a skeptic would accept, against baselines a skept
What does the edbt-experiments skill do?
Use when designing or auditing EDBT empirical evaluations for database-systems work, covering real workloads and datasets, fair and tuned baselines, honest measurement across realistic scales, reproducible harnesses, and the higher bar of the Experiments & Analysis paper where the measurement itself is the contribution.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill edbt-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.