vis-reproducibility
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-reproducibility --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.
# 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
- Evidence map
- Open-materials statement audit
- Preregistration for studies (a distinctly VIS-valued move)
- Provenance pinning
- Degrees of reproducibility (state the one you achieved)
- Vignette: a technique-plus-study paper
- Consistency and camera-ready pass
- Output format
What does the vis-reproducibility skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-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 984 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.