Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when strengthening CAV (Computer Aided Verification) reproducibility, covering benchmark provenance (SV-COMP/SMT-COMP/HWMCC/VNN-COMP set revisions), pinned tool and baseline…
Install Cav Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-reproducibility --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/CAV-Skills/skills/cav-reproducibility .claude/skills/cav-reproducibility && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cav-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-reproducibility into .claude/skills/cav-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-reproducibility", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-reproducibilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-reproducibility --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/CAV-Skills/skills/cav-reproducibility .agents/skills/cav-reproducibility && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cav-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-reproducibility into .agents/skills/cav-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-reproducibility", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-reproducibility --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/CAV-Skills/skills/cav-reproducibility .cursor/skills/cav-reproducibility && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cav-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-reproducibility into .cursor/skills/cav-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-reproducibility", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path CAV-Skills/skills/cav-reproducibility--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-reproducibility --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/CAV-Skills/skills/cav-reproducibility .gemini/skills/cav-reproducibility && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cav-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-reproducibility into .gemini/skills/cav-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-reproducibility", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-reproducibilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/CAV-Skills/skills/cav-reproducibility .github/skills/cav-reproducibility && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cav-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-reproducibility into .github/skills/cav-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-reproducibility", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cav-reproducibility --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/CAV-Skills/skills/cav-reproducibility .opencode/skills/cav-reproducibility && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cav-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CAV-Skills/skills/cav-reproducibility into .opencode/skills/cav-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cav-reproducibility", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
cav-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 588 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening CAV (Computer Aided Verification) reproducibility, covering benchmark provenance (SV-COMP/SMT-COMP/HWMCC/VNN-COMP set revisions), pinned tool and baseline…
- Strengthening CAV (Computer Aided Verification) reproducibility
- SKILL.md covers Evidence map, The reproducibility failure…, Provenance and configuration… and Degrees of reproducibility…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering benchmark provenance (SV-COMP/SMT-COMP/HWMCC/VNN-COMP set revisions)
What it does
Cav Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- 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
Example prompts
- “/cav-reproducibility”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Cav Reproducibility loads about 1.4k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 588 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 588 words, ~1,406 tokens.
Download SKILL.mdSave it as
.claude/skills/cav-reproducibility/SKILL.md (or your agent's skills folder).- name
- cav-reproducibility
- description
- 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.
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 bare "verified" verdict is not reproducible evidence.
- Keep the paper and the artifact consistent: a number in the PDF that no script regenerates is the contradiction reviewers read as carelessness.
The reproducibility failure modes CAV reviewers know
| Claim in the paper | Weak answer | CAV-ready answer |
|---|---|---|
| "Faster than solver X" | "X was slower in our tests" | X vA.B, its documented config, same time/memory limit, same hardware; per-instance data in the artifact |
| "Solves N hard instances" | "on standard benchmarks" | The named division of set <revision R>, the exact instance list, the fetch/pin in the artifact |
| "Our result is sound/UNSAT" | asserted | The unsat proof + a bundled independent checker that accepts it |
| "Randomized search finds it" | one lucky run | Fixed seed(s), number of runs, variance reported |
| "Scales to large designs" | "large" | The size metric and the largest instance run, with the timeout that bounds it |
Provenance and configuration pinning
[Benchmarks] pin the set + revision (SV-COMP/SMT-COMP/HWMCC/VNN-COMP subset); archive the instance
list and the fetch script, not just "the standard benchmarks"
[Tools] record exact versions (yours and every baseline), build flags, and the commit/tag
[Limits] state per-instance wall-clock and memory limits, core count, and the hardware/CPU
[Randomness] log seeds for portfolio/stochastic components; say what is and is not deterministic
[Witnesses] ship proof certificates + an independent checker for every soundness/UNSAT claim
[Runs] state the number of repetitions and how variance/timeouts were handledShow full SKILL.md (270 more words)Show less
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command reruns a benchmark subset and regenerates a table/figure from logged data (and re-checks the witnesses).
- Scripted: scripts exist but require documented manual steps, a large external benchmark download, or a long (multi-day) full run.
- Descriptive: prose detailed enough that a competent reader could rebuild the evaluation.
For CAV, aim turnkey for anything a reviewer might rerun quickly (a solver on a small bundled subset, a witness check) and scripted-with-clear-instructions for a full multi-day benchmark sweep. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine.
Vignette: a portfolio-solver evaluation
Consider a paper claiming a portfolio SMT technique is faster and stays sound. Its reproducibility spine: the solver pinned to a commit with build flags; each baseline pinned to a released version and its documented configuration; the benchmark division pinned to a revision with an archived instance list; a uniform per-instance time/memory limit and stated hardware; logged seeds and repetition count; a differential check against a trusted solver on all verdicts (no disagreements) plus unsat proofs with a bundled checker; and analysis scripts that turn the logs into the paper's tables — with one honest sentence about the parts (a proprietary hardware benchmark, say) that cannot be shared and why.
Consistency and camera-ready pass
- Before submission: every benchmark number traces to a logged run in the artifact; every soundness claim has a checkable witness; the artifact is anonymized for Regular/Application categories.
- Before camera-ready: swap anonymized links for a permanent, DOI-issuing archive, and align the
artifact with the AEC badges you are pursuing (
cav-artifact-evaluation).
Output format
[Claim inventory] <claim -> proof/witness or logged benchmark run>
[Benchmark provenance] set+revision / baseline versions / limits / hardware — pinned? yes/no
[Soundness evidence] witness + independent checker present? yes/no
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Artifact fixes] <additions before upload>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in CAV-Skills/skills/cav-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cav Reproducibility next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cav Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
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Compute Environment Setup
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Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
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Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
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Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Cav Reproducibility
What does Cav Reproducibility do?
A skill your agent uses when strengthening CAV (Computer Aided Verification) reproducibility, covering benchmark provenance (SV-COMP/SMT-COMP/HWMCC/VNN-COMP set revisions), pinned tool and baseline…. Cav Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Cav Reproducibility?
Cav Reproducibility fits situations like: 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.
How do I install Cav Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a claude-code`. Or copy the skill folder (CAV-Skills/skills/cav-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cav-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Cav Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a codex`. Or copy the skill folder (CAV-Skills/skills/cav-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cav-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Cav Reproducibility in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cav-reproducibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cav-reproducibility, .gemini/skills/cav-reproducibility, .github/skills/cav-reproducibility and .opencode/skills/cav-reproducibility in your project.
What does Cav Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Cav Reproducibility is instructions for the agent only.
Does Cav Reproducibility access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Cav Reproducibility safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Cav Reproducibility use?
Cav Reproducibility is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Cav Reproducibility use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Cav Reproducibility?
Skills that share tags, products or a category with Cav Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cav Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.