viz
Skill48 starsChanged 8 days agoMITView on GitHub
Use when creating or revising any chart, plot, figure, or data visualization, including requests to graph, visualize, make a figure, or improve an existing chart.
8 other projects use a version of this.
Adopted by
Most recently taken up by
- matthewdigiuseppe/MStacktheir own version, first seen Nov 1, 2026
- tizonai/hyperuitheir own version, first seen Oct 9, 2026
- AidenSbVevo/claude-code-startertheir own version, first seen Oct 8, 2026
- sandptel/viztheir own version, first seen Sep 22, 2026
- SNL-UCSB/data-visualization-skilltheir own version, first seen Jul 27, 2026
- unsafe9/agent-skillstheir own version, first seen Jun 16, 2026
Other versions8 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 8
- matthewdigiuseppe/MStack★ 17same name
- SNL-UCSB/data-visualization-skill★ 4same name
- FelipeAngeli/skills★ 2same name
- tizonai/hyperui★ 2same name
- AidenSbVevo/claude-code-starter★ 1same name
- uhyunpark/cc-plugin-viz★ 1same name
- unsafe9/agent-skills★ 1same name
- sandptel/viz★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository9 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agySkill
- claudeSkill
- codexSkill
- datacheckSkill
- hallucinatorSkill
- napkinSkill
- plain-writingSkill
The file
We split this skill into the rules it gives an agent
9 rules. 0 are common, 6 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 8 edited versions we read keep these.
6
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 13
Run datacheck first if the data is new.
- Distinctive: line 14
Write down the sample, the N, and the quantity each panel shows.
- Distinctive: line 15
When you revise a figure, keep the user's chart type.
- Distinctive: line 32
Use the survey item's wording or a plain label, never a raw variable name.
- Distinctive: line 33
Use one shared theme and one shared scale for each paper, so each group keeps the same color and shape in every panel and figure.
- Distinctive: line 37
Avoid 3D effects, dual axes, and decorative backgrounds unless the user asks for them.
- Very specific: line 43
Use a device that works without a display, such as ragg or cairo, with a white background, not a transparent one.
- Distinctive: line 44
Use plain ASCII hyphens and minus signs in labels unless you have checked that the font renders the others.
- Very specific: line 50
Run pdftotext on a PDF and search it for raw variable names or other internal labels.
“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Data visualization
Build a figure that answers a clear question, matches the text it supports, and survives inspection of the exported file.
Before plotting
- Confirm the audience, the comparison, the output format, and any journal or slide constraints. Ask once about all the visual choices you cannot infer, e.g., layout, interval type, and size, instead of fixing them one draft at a time.
- Run
datacheckfirst if the data is new. - Write down the sample, the N, and the quantity each panel shows. Check that they match what the text and caption claim, e.g., one study arm rather than all arms pooled.
- When you revise a figure, keep the user's chart type. Offer a different design as a separate option file and let the user choose.
Choose the simplest chart that matches the task:
| Question | Good default |
| Compare categories | Ordered point or bar chart |
| Show a distribution | Histogram, density, violin, or box plot |
| Show change over time | Line chart |
| Show association | Scatterplot with an appropriate fit |
| Show estimates | Dot and whisker or forest plot, with small multiples for many groups |
| Show composition | Stacked bars only when part-to-whole comparison is clear |
Build
- Make every figure from a script in the project that saves it to disk. Do not leave a figure only in a notebook or plot pane.
- Make each axis title and annotation name the statistic actually plotted, e.g., odds ratios on an odds ratio axis, and r rather than R². State the direction of any difference and use it in every figure.
- Use the survey item's wording or a plain label, never a raw variable name.
- Use one shared theme and one shared scale for each paper, so each group keeps the same color and shape in every panel and figure. Set category order once, on the full data.
- Show every category in every figure. Mark sparse cells instead of dropping them.
- Show uncertainty when the estimate has uncertainty, and say which interval it is. When significance matters, show it with fill or line type as well as the interval.
- Make the figure readable in grayscale and for colorblind readers. Pair color with shape or line type.
- Avoid 3D effects, dual axes, and decorative backgrounds unless the user asks for them.
Unless the project sets other defaults, use R with ggplot2 and theme_minimal(base_size = 12). Save a vector PDF and a 300 dpi PNG preview at the final size, e.g., one journal column wide.
Export
- Use a device that works without a display, such as
raggorcairo, with a white background, not a transparent one. Embed fonts in PDFs, e.g., withcairo_pdf. - Use plain ASCII hyphens and minus signs in labels unless you have checked that the font renders the others.
- Long labels become short names, and the full wording goes in the caption. Keep legends to a few breaks.
Verify after every change
- Open the exported file at its final size, not the plot pane. Check for clipped or overlapping text, crowded legends, wrong category order, empty groups, and missing fonts.
- Run
pdftotexton a PDF and search it for raw variable names or other internal labels. - Compare a few plotted values with the summary table they come from.
- If a document embeds the figure, rebuild the figure, then render the document again. Check that the document shows the new version, that every saved figure is used, and that captions are not duplicated. In pandoc, leave the image alt text empty when a separate caption exists.
Report the source data, the script, the output files, and the checks you ran. Use journal-style, from the journal-style repository, when a finished figure must meet a journal's submission rules.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

