Agent skill

cav-reproducibility

Use when strengthening CAV (Computer Aided Verification) reproducibility, covering benchmark provenance (SV-COMP/SMT-COMP/HWMCC/VNN-COMP set revisions), pinned tool and baseline versions, resource limits and hardware, seeds for randomized/portfolio solvers, checkable proof witnesses/certificates for soundness claims, and consistency between the paper's tables and the artifact.

brycew6m4,252★ · +31/wk · 3 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 5 KB
Bundled scripts: none
Path: CAV-Skills/skills/cav-reproducibility/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# CAV Reproducibility Use this before submission and again before camera-ready. In computer-aided verification, reproducibility is not a courtesy — a benchmark result is only meaningful relative to a **fixed benchmark set, pinned tool versions, and a stated resource budget**, and a soundness claim is only credible if it ships a **checkable witness**. The goal is that a competent reader could rerun your evaluation and re-check your correctness claims and reach your conclusions. ## Evidence map - Map each theorem, technique claim, and reported benchmark number to a **verifiable location** — a proof (body or appendix), a script in the artifact, or a table regenerated from logged runs. - For techniques, give enough of the algorithm, parameters, and encoding that a reader could re-implement or rerun it. - For benchmark evaluations, report the exact **benchmark set and revision**, the **baseline tools and versions**, the **resource limits** (per-instance time and memory), the **hardware and core count**, and the **number of runs**. - For soundness claims, emit a **certificate/witness** (unsat proof, DRAT, an SV-COMP-style witness, an Isabelle/Coq script) and an independent checker — a ba

What's inside
Steps it walks through
  1. Evidence map
  2. The reproducibility failure modes CAV reviewers know
  3. Provenance and configuration pinning
  4. Degrees of reproducibility (state the one you achieved)
  5. Vignette: a portfolio-solver evaluation
  6. Consistency and camera-ready pass
  7. Output format
More from Awesome-Journal-Skills
All skills →
About this skill
What does the cav-reproducibility skill do?

Use when strengthening CAV (Computer Aided Verification) reproducibility, covering benchmark provenance (SV-COMP/SMT-COMP/HWMCC/VNN-COMP set revisions), pinned tool and baseline versions, resource limits and hardware, seeds for randomized/portfolio solvers, checkable proof witnesses/certificates for soundness claims, and consistency between the paper's tables and the artifact.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-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.

Keep going