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 IEEE VIS reproducibility and open-practices evidence, covering the open-materials statement, anonymized-but-runnable code and stimuli, preregistration of…
Install Vis Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-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/VIS-Skills/skills/vis-reproducibility .claude/skills/vis-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 "vis-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-reproducibility into .claude/skills/vis-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-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/VIS-Skills/skills/vis-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 vis-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-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/VIS-Skills/skills/vis-reproducibility .agents/skills/vis-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 "vis-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-reproducibility into .agents/skills/vis-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-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 vis-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-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/VIS-Skills/skills/vis-reproducibility .cursor/skills/vis-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 "vis-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-reproducibility into .cursor/skills/vis-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-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 VIS-Skills/skills/vis-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 vis-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-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/VIS-Skills/skills/vis-reproducibility .gemini/skills/vis-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 "vis-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-reproducibility into .gemini/skills/vis-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-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 vis-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 vis-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/VIS-Skills/skills/vis-reproducibility .github/skills/vis-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 "vis-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-reproducibility into .github/skills/vis-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-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 vis-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 vis-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/VIS-Skills/skills/vis-reproducibility .opencode/skills/vis-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 "vis-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-reproducibility into .opencode/skills/vis-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-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
vis-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 572 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening IEEE VIS reproducibility and open-practices evidence, covering the open-materials statement, anonymized-but-runnable code and stimuli, preregistration of…
- Strengthening IEEE VIS reproducibility and open-practices evidence
- SKILL.md covers Evidence map, Open-materials statement audit, Preregistration for studies (a… and Provenance pinning, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the open-materials statement
What it does
Vis Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening IEEE VIS reproducibility and open-practices evidence, covering the open-materials statement, anonymized-but-runnable code and stimuli, preregistration of perceptual and user studies, provenance for datasets and rendering pipelines, claim-to-figure mapping, honest degrees of reproducibility, and consistency between what the TVCG paper says and what the supplemental archive contains.
Its SKILL.md is about 1.5k 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 IEEE VIS reproducibility and open-practices evidence
- Covering the open-materials statement
- Anonymized-but-runnable code and stimuli
- Preregistration of perceptual and user studies
Example prompts
- “/vis-reproducibility”
Requirements
- Docker
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
Vis Reproducibility loads about 1.5k tokens when it runs. Until then it costs about 107 tokens; SKILL.md has 572 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). 572 words, ~1,499 tokens.
Download SKILL.mdSave it as
.claude/skills/vis-reproducibility/SKILL.md (or your agent's skills folder).- name
- vis-reproducibility
- description
- Use when strengthening IEEE VIS reproducibility and open-practices evidence, covering the open-materials statement, anonymized-but-runnable code and stimuli, preregistration of perceptual and user studies, provenance for datasets and rendering pipelines, claim-to-figure mapping, honest degrees of reproducibility, and consistency between what the TVCG paper says and what the supplemental archive contains.
VIS Reproducibility
Use this before submission and again before camera-ready. IEEE VIS's Open Practices posture and the Graphics Replicability Stamp make reproducibility a visible dimension, not a courtesy: reviewers routinely open the supplemental code, data, and video, and the TVCG camera-ready collects open-practices disclosures. The goal is that a competent reader could rebuild your figures, rerun your study analysis, and reach your conclusions.
Evidence map
- Map each figure, quantitative result, and study finding to a verifiable location — a section, a figure generated from logged data, or a script in the supplemental archive.
- For techniques and rendering, give enough of the algorithm, parameters, and environment (including GPU/driver assumptions and tolerances) that a reader could re-implement or re-run.
- For empirical and perceptual studies, report participants and recruitment, apparatus/stimuli, the task, the design (within/between), measures, statistics, and the analysis scripts.
- Keep the open-materials statement truthful and specific: what is shared, where it lives, and — if something cannot be shared — exactly why.
- Keep the paper and the archive consistent: a number in the PDF that no script reproduces is the contradiction reviewers read as carelessness.
Open-materials statement audit
| Claim in the paper | Weak availability answer | VIS-ready answer |
|---|---|---|
| "We render/lay out X" | "Code available on request" | Public, licensed repo with a build path that regenerates the teaser figure |
| "Our system supports task Y" | "Demo will be released" | Runnable build (or Docker) with bundled sample data and a demo script |
| "N participants judged Z" | Nothing (privacy cited vaguely) | Anonymized responses, stimuli, the analysis notebook, and the ethics/consent note |
| "We evaluated on dataset D" | Named but not shared | The dataset or documented access + the preprocessing scripts |
"Available on request" reads as not available; convert every such line into a concrete, anonymized archive or an explicit, justified exception.
Preregistration for studies (a distinctly VIS-valued move)
Perceptual experiments and controlled user studies benefit from preregistration (e.g., on OSF): locking hypotheses, design, sample size, and the analysis plan before data collection separates confirmatory from exploratory findings and blunts the "you fished for that result" objection.
[Preregister] hypotheses, conditions, planned N + power analysis, primary DV, analysis plan
[Cite it] reference the (anonymized) preregistration in the paper; report deviations honestly
[Separate] label confirmatory vs. exploratory results; do not present post-hoc as plannedProvenance pinning
[Datasets] record source and version; archive the actual data or stimuli, not just a query/URL;
document any cleaning/filtering with the script
[Rendering] pin toolchain and library versions; provide reference images and a comparison
tolerance for GPU-dependent or non-deterministic output
[Studies] store raw per-participant responses (anonymized), the exact stimuli, and timing
[Compute] state hardware and runtime so a reader can size a reproduction
[Randomness] log seeds; say what is and is not deterministicShow full SKILL.md (235 more words)Show less
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each figure/result from logged data.
- Scripted: scripts exist but need documented manual steps, large data, or specific hardware.
- Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.
For VIS, aim turnkey for anything an evaluator might rerun quickly (a figure from logged benchmark data, a study's statistics from anonymized responses); a large rendering benchmark or a proprietary dataset may stay scripted with access clearly documented. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine — the GRSI stamp is decided exactly there.
Vignette: a technique-plus-study paper
A paper contributing a new encoding and a controlled study evaluating it. Its reproducibility spine: the encoding code with a script that regenerates each figure; the study's stimuli and anonymized per-participant responses; the preregistration for the confirmatory hypotheses; the analysis notebook that turns responses into the reported effect sizes and CIs; and one honest sentence about anything (identifiable video, proprietary data) that cannot be shared and why.
Consistency and camera-ready pass
- Before submission: every scored number and figure traces to the archive; the open-materials statement matches reality; if double-blind, the archive is anonymized (no owner strings, lab names, or institutional URLs).
- Before camera-ready: swap anonymized links for permanent, DOI-issuing archives, complete the Open
Practices form, and align the package with what you submit to GRSI (
vis-artifact-evaluation).
Output format
[Claim inventory] <figure/result/finding -> evidence location>
[Open materials] concrete / vague / missing
[Preregistration] present / not applicable / should have (for studies)
[Provenance gaps] <dataset versions / rendering references / study raw data / seeds>
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Archive 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 VIS-Skills/skills/vis-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Vis 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 |
|---|---|---|---|---|---|---|
| Vis Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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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Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
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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 Vis Reproducibility
What does Vis Reproducibility do?
A skill your agent uses when strengthening IEEE VIS reproducibility and open-practices evidence, covering the open-materials statement, anonymized-but-runnable code and stimuli, preregistration of…. Vis Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening IEEE VIS reproducibility and open-practices evidence, covering the open-materials statement, anonymized-but-runnable code and stimuli, preregistration of perceptual and user studies, provenance for datasets and rendering pipelines, claim-to-figure mapping, honest degrees of reproducibility, and consistency between what the TVCG paper says and what the supplemental archive contains.
When should I use Vis Reproducibility?
Vis Reproducibility fits situations like: strengthening IEEE VIS reproducibility and open-practices evidence; covering the open-materials statement; anonymized-but-runnable code and stimuli; preregistration of perceptual and user studies.
How do I install Vis Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-reproducibility -a claude-code`. Or copy the skill folder (VIS-Skills/skills/vis-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/vis-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Vis Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-reproducibility -a codex`. Or copy the skill folder (VIS-Skills/skills/vis-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/vis-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Vis 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 vis-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/vis-reproducibility, .gemini/skills/vis-reproducibility, .github/skills/vis-reproducibility and .opencode/skills/vis-reproducibility in your project.
What does Vis Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Vis Reproducibility is instructions for the agent only. Our summary lists: Docker.
Does Vis 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 Vis 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 Vis Reproducibility use?
Vis 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 Vis Reproducibility use?
About 1.5k tokens (SKILL.md is roughly 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 Vis Reproducibility?
Skills that share tags, products or a category with Vis 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 Vis 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.