cav-experiments
Use when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers with pinned versions and equal resource limits, timeout-dominated comparisons, soundness cross-checks and proof witnesses, cactus/scatter reporting, and matching evidence to the shape of each verification claim.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-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.
# CAV Experiments Use this before submission when the evaluation is not yet locked. CAV reviewers are verification researchers; the empirical section is where a good technique is won or lost. The organizing principle is **evidence proportional to the claim** — the evaluation must test what the paper asserts, on benchmarks and baselines a skeptic accepts, under a resource budget that makes the comparison fair. ## Evaluation audit - **Match evidence to the claim shape.** A **soundness** claim needs a proof and/or a checkable witness, not a benchmark score. A **performance** claim needs a fair comparison on standard benchmarks under equal limits. A **capability** claim ("solves instances prior tools cannot") needs those instances and the prior tools actually run. - **Use standard benchmark sets** at a **pinned revision** (SV-COMP, SMT-COMP, HWMCC, VNN-COMP, or a documented domain set). State the subset you ran and why; a hand-picked set invites the "cherry-picked" reject. - **Choose fair baselines:** the strongest relevant prior tool(s), at their **latest released version**, run with a **documented, equal resource limit** (per-instance time and memory) on the same hardware. An outdate
- Evaluation audit
- Claim-to-evidence design table
- Fair-comparison checklist (the reviewer's first objections)
- Reporting floor
- Vignette: evaluating a model-checking technique
- Output format
What does the cav-experiments skill do?
Use when designing or auditing a CAV (Computer Aided Verification) empirical evaluation, covering standard benchmark sets (SV-COMP/SMT-COMP/HWMCC/VNN-COMP), fair baseline solvers with pinned versions and equal resource limits, timeout-dominated comparisons, soundness cross-checks and proof witnesses, cactus/scatter reporting, and matching evidence to the shape of each verification claim.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-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.