vis-experiments
Use when designing or auditing IEEE VIS evaluations, covering how to match evidence to the contribution type (perceptual study, controlled user study, algorithm benchmark, design-study validation, qualitative work), controlled experiment design with power and effect sizes, CVD-safe and perceptually grounded encoding choices, task taxonomies, and provenance so a TVCG reviewer trusts the result.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-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.
# VIS Experiments Use this before submission when the evaluation is not yet locked. IEEE VIS reviewers judge whether the **evidence matches the contribution type** — and visualization has several distinct contribution types, each with its own evidence standard. The organizing principle is **evaluate the claim you actually make**: a claim about *perception* needs a controlled study, a claim about *scale* needs a benchmark, a claim about *real-world usefulness* needs a design-study validation or a deployment. ## Evaluation audit - **Pick the evaluation to the contribution type**, not by habit (see the table). The classic VIS reject is a system paper "evaluated" only by an accuracy number, or a perceptual claim backed only by author intuition. - **Design controlled studies properly:** state hypotheses, a within/between design, a task from a recognized task taxonomy, a **power analysis** justifying N, and report **effect sizes with confidence intervals**, not just p-values. Consider preregistration for confirmatory studies (`vis-reproducibility`). - **Justify encodings perceptually:** color choices should be **CVD-safe** and appropriate to the data type (sequential/diverging/categorica
- Evaluation audit
- Contribution-type to evidence table
- Controlled-study design floor
- Perceptual and accessibility checks
- Vignette: evaluating a new time-series encoding
- Output format
What does the vis-experiments skill do?
Use when designing or auditing IEEE VIS evaluations, covering how to match evidence to the contribution type (perceptual study, controlled user study, algorithm benchmark, design-study validation, qualitative work), controlled experiment design with power and effect sizes, CVD-safe and perceptually grounded encoding choices, task taxonomies, and provenance so a TVCG reviewer trusts the result.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-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.