vldb-experiments
Use when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-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.
# VLDB Experiments Use this before the evaluation section hardens. At this venue the experiments *are* the argument: a PVLDB reviewer typically flips from the introduction straight to the plots and decides how skeptically to read everything else. ## What the evaluation must establish Four distinct burdens, each needing its own experiments: 1. **The problem exists** — measure the baseline failing on a credible workload before showing your fix. 2. **The mechanism causes the gain** — ablate your components; a monolithic "our system vs. theirs" plot proves selection, not mechanism. 3. **The gain survives scale** — curves along data size, cluster size, and concurrency, not one configuration chosen after the fact. 4. **The cost is known** — measure where your design loses and say so. ## Workload realism ladder | Rung | Example | Reviewer credit | |---|---|---| | Micro-benchmark | single operator, synthetic keys | Mechanism insight only | | Standard benchmark | TPC-style, YCSB, JCC-H-class | Comparable, but "benchmark-only" is a known flag | | Benchmark + skew/drift | standard suite with realistic distributions | Solid | | Trace or production-derived | replayed real workload | Strongest,
- What the evaluation must establish
- Workload realism ladder
- Baseline fairness protocol
- Reporting floor
- Ablation and sweep matrix
- Scale honesty
- Output format
What does the vldb-experiments skill do?
Use when designing or auditing the evaluation of a VLDB paper, covering workload and dataset realism at scale, competitor tuning fairness, scalability curves versus single points, tail-latency and throughput reporting, ablations that isolate the mechanism, and the loss-case disclosure PVLDB reviewers look for first.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-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.