Data Scientist
magnus919/hermes-profiles
PhD-level expertise in data science, statistics, and machine learning.
A skill your agent uses when designing or auditing IEEE VIS evaluations, covering how to match evidence to the contribution type (perceptual study, controlled user study, algorithm benchmark…
Install Vis Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-experiments --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-experiments .claude/skills/vis-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-experiments into .claude/skills/vis-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-experiments", 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-experimentsType 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-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-experiments --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-experiments .agents/skills/vis-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-experiments into .agents/skills/vis-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-experiments", 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-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-experiments --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-experiments .cursor/skills/vis-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-experiments into .cursor/skills/vis-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-experiments", 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-experiments--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-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vis-experiments --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-experiments .gemini/skills/vis-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-experiments into .gemini/skills/vis-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-experiments", 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-experimentsInstalls 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-experiments -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-experiments .github/skills/vis-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-experiments into .github/skills/vis-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-experiments", 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-experiments -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-experiments --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-experiments .opencode/skills/vis-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VIS-Skills/skills/vis-experiments into .opencode/skills/vis-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vis-experiments", 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-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 532 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing IEEE VIS evaluations, covering how to match evidence to the contribution type (perceptual study, controlled user study, algorithm benchmark…
- Auditing IEEE VIS evaluations
- SKILL.md covers Evaluation audit, Contribution-type to evidence…, Controlled-study design floor and Perceptual and accessibility…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering how to match evidence to the contribution type (perceptual study
What it does
Vis Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
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 Experimental design. 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
- Auditing IEEE VIS evaluations
- Covering how to match evidence to the contribution type (perceptual study
- Controlled user study
- Algorithm benchmark
Example prompts
- “/vis-experiments”
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 Experiments loads about 1.4k tokens when it runs. Until then it costs about 103 tokens; SKILL.md has 532 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). 532 words, ~1,390 tokens.
Download SKILL.mdSave it as
.claude/skills/vis-experiments/SKILL.md (or your agent's skills folder).- name
- vis-experiments
- description
- 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.
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/categorical); channel choices should follow known effectiveness rankings for the task. Reviewers check this explicitly.
- Benchmark techniques fairly: compare against the strongest existing technique and a reasonable baseline on realistic data sizes, with runtime/quality reported and the code available.
- Hold qualitative and design-study work to method: coding schemes, multiple coders, reflection across abstraction levels, and an audit trail — design studies are a first-class VIS contribution, not a weak substitute for a controlled study.
- Pin provenance for datasets, stimuli, and rendering so the evaluation reproduces rather than re-samples.
Contribution-type to evidence table
| Contribution type | Matching evidence | Reject pattern avoided |
|---|---|---|
| Perceptual/cognitive claim | Controlled experiment: real stimuli, power analysis, effect sizes + CIs | "Author intuition stands in for a perception result" |
| New encoding/interaction technique | Controlled study and/or task-based comparison vs. the conventional design | "Prettier, but no evidence it helps a task" |
| System / tool | Demonstration of real use, expert feedback, or a usage study | "Feature list with no evaluation of use" |
| Design study | Reflection + validation across data/task/encoding/algorithm levels | "A one-off tool with no transferable lesson" |
| Algorithm (layout/rendering) | Benchmark: quality + runtime vs. strong baselines on realistic sizes | "Toy inputs only; no comparison" |
| Data/model contribution | Characterization + a task the data enables, with the data shared | "Dataset dumped with no analysis or task" |
Show full SKILL.md (164 more words)Show less
Controlled-study design floor
[Hypotheses] stated before analysis; confirmatory vs. exploratory labeled
[Design] within/between justified; counterbalancing; the task from a known taxonomy
[Power] an a-priori power analysis justifies N; do not stop at "we recruited 20"
[Stimuli] real or realistic; the exact stimuli archived
[Measures] accuracy AND time AND (where relevant) preference/confidence; define each
[Statistics] effect sizes + CIs; appropriate tests; corrections for multiple comparisons
[Reporting] report what you found, including null and exploratory results, honestlyPerceptual and accessibility checks
- Color: use CVD-safe palettes; match palette type to data (sequential for ordered, diverging for a meaningful midpoint, categorical for nominal); never encode magnitude on hue alone.
- Channel effectiveness: prefer position/length for quantitative comparison; justify any use of area, angle, or color for a precise task.
- Legibility: ensure figures read in grayscale and at print size; a result a reviewer cannot see is a result you cannot claim.
Vignette: evaluating a new time-series encoding
Suppose the paper claims a new encoding reads trends faster than a line chart. The matching plan: a controlled within-subjects study; trend-reading tasks drawn from a task taxonomy; real time-series stimuli, archived; an a-priori power analysis fixing N; accuracy and completion-time as measures; effect sizes with CIs comparing the new encoding to a tuned line-chart baseline; a CVD-safe palette justified against the task; and honest reporting of any task where the line chart won — every number traceable to the archived analysis notebook.
Output format
[Evaluation readiness] strong / adequate / weak
[Contribution type] perceptual / technique / system / design-study / algorithm / data
[Evidence match] <contribution type -> evidence chosen -> appropriate? yes/no>
[Study rigor] <hypotheses? power analysis? effect sizes + CIs? preregistered?>
[Encoding validity] <CVD-safe? channel matched to task? grayscale-legible?>
[Provenance] <stimuli/data/rendering archived and reproducible? yes/no>
[Decision-critical next run] <one study or benchmark to add>© 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-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Vis Experiments 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 Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Data Scientistmagnus919/hermes-profiles | 281 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Scenario Experiment Benchmark MiningDrchronx/ai-agent-research-starter-kit | 135 | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Data Scientistmagnus919/agent-skills | 113 | — | ~4.1k | Automated safety check: Pass | MIT | |
| Light Experiment CodingLight0305/Light-skills | 641 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Experiment DesignGRIND-Lab-Core/night_owl_research_agent | 106 | — | ~3.3k | Automated safety check: Warn | None |
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Mine and synthesize real top-journal scenario/vignette experiment patterns for behavioral research.
Data Scientist
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A skill your agent uses for PhD-level expertise in data science, statistics, and machine learning: rigorous statistical analysis, experimental design, causal inference, advanced modeling, research…
Light Experiment Coding
Light0305/Light-skills
Builds the code for a frozen research experiment test-first, with leakage controls, seed handling and saved evidence so results can be rerun and audited.
Experiment Design
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Turn a refined GIScience / remote sensing / spatial data science proposal into a detailed, claim-driven experiment roadmap.
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Calculates sample sizes and statistical power for study planning.
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Questions about Vis Experiments
What does Vis Experiments do?
A skill your agent uses when designing or auditing IEEE VIS evaluations, covering how to match evidence to the contribution type (perceptual study, controlled user study, algorithm benchmark…. Vis Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Vis Experiments?
Vis Experiments fits situations like: auditing IEEE VIS evaluations; covering how to match evidence to the contribution type (perceptual study; controlled user study; algorithm benchmark.
How do I install Vis Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-experiments -a claude-code`. Or copy the skill folder (VIS-Skills/skills/vis-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/vis-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Vis Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vis-experiments -a codex`. Or copy the skill folder (VIS-Skills/skills/vis-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/vis-experiments in your project. Codex loads it when a task matches its description.
Can I use Vis Experiments 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-experiments -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-experiments, .gemini/skills/vis-experiments, .github/skills/vis-experiments and .opencode/skills/vis-experiments in your project.
What does Vis Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Vis Experiments is instructions for the agent only.
Does Vis Experiments 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 Experiments 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 Experiments use?
Vis Experiments 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 Experiments 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 Vis Experiments?
Skills that share tags, products or a category with Vis Experiments: Data Scientist (magnus919/hermes-profiles, 281 stars), Scenario Experiment Benchmark Mining (Drchronx/ai-agent-research-starter-kit, 135 stars), Data Scientist (magnus919/agent-skills, 113 stars) and Light Experiment Coding (Light0305/Light-skills, 641 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Vis Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.