Plot
SkillDocs & knowledgePlot gives your AI a data visualization expert to consult. Once added, your AI can pick the right chart type for a dataset, plan exploratory data analysis, and review charts for problems before you share them.
Use Plot in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Plot and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Plot skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
After adding Plot, ask your AI to design a visualization for a dataset, draft a dashboard spec, or review the charts in a notebook.
What your AI can do with it
- Select the right chart type and encoding for a dataset
- Design exploratory data analysis workflows
- Build dashboard specifications
- Audit existing charts for misleading visuals
- Catch accessibility failures in visualizations
- Flag wrong library choices for charting
What this skill tells your AI
The instructions your AI receives, as published by a554b554/reactant in skills/plot/SKILL.md and read by Ahel’s review.
Activation Boundary
This is a tag-only worker skill. Use it only after execute or execute-one
selects an exact <@plot...> annotation in a target document and hands off its
context. Never activate it from an ordinary chat chart request, semantic
similarity, or any skill other than those two routers. Without that handoff,
do not read data, run code, or create artifacts.
Produce a data visualization grounded in a real data file by generating and executing a Python script, then replace the tag with a figure reference.
Tag
<@plot: chart description, use ``data/results.csv``, save to ``figures/out.png``>
Input
- File path — the document being processed.
- Surrounding block — the maximal run of non-blank lines containing the tag; blank lines bound it.
- Prompt — the desired visualization: chart type, styling, formatting. Its double-backtick context references name the data file and, optionally, the output path.
Workflow
- Identify the data file from the prompt's context references and read it first to learn its actual structure (columns, types, row count). Never assume column names. If the prompt has no data reference, fail cleanly and ask the user to add one.
- Determine the output path: the output context reference if given,
otherwise a
figures/directory next to the document (create it if needed) with a sanitized name derived from the prompt (lowercase, underscores; for examplefigures/completion_time.png). - Write a complete, self-contained Python script that reads the data file,
produces the requested visualization (matplotlib and/or seaborn; pandas
for tabular loading), applies the prompt's styling, and saves the figure
with
dpi=300andbbox_inches='tight'. Default to clean, publication-ready styling: white background, legible fonts, labeled axes. If the prompt gives no chart type, infer the most appropriate one from the data structure and surrounding context. - Save the script next to the artifact with the same basename and a
.pyextension (for examplefigures/completion_time.py), then run it once with the host's Python. Keep the script file so the plot can be regenerated and audited. - Confirm the image file now exists, then replace the
<@plot: ...>tag with a format-appropriate reference using a path relative to the document:- Markdown:
 - LaTeX:
\includegraphics{figures/out.png}
- Markdown:
Rules
- Consume only this tag; do not rewrite surrounding prose. Preserve every
unrelated tag,
<@comment: ...>tag,<@chat-model>block, and escaped literal byte-for-byte. - Resolve reference paths relative to the document's directory unless absolute.
- If the data file cannot be found or read, if plotting libraries are unavailable, or if the script errors, fail cleanly: leave the tag, report the error, and include the generated script so the user can debug.
- The rendered chart is accepted as produced; Reactant runs no quality check or second model pass on it.
Signals
- GitHub stars
- 54
- Forks
- 11
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
plot- Source
- github.com/a554b554/reactant
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