Generate a publication-quality statistical plot from a data file using PaperBanana.
Instructions
Read the data file at $ARGUMENTS[0].
Prepare the data for the MCP tool:
If the file is CSV: parse it and convert to a column-keyed dictionary (keys = column names, values = arrays of column values), then serialize with json.dumps() to produce a JSON string.
If the file is JSON: use the raw file content as-is (it is already a JSON string).
If $ARGUMENTS[1] is provided, use it as the plot intent. Otherwise, ask the user for a description of the desired plot (e.g., "Bar chart comparing model accuracy across benchmarks").
Call the MCP tool generate_plot with:
data_json: the JSON string (not a parsed object)
intent: the plot description
iterations: 3 (default)
Present the generated plot to the user.
CLI Fallback
If the MCP tool is not available, fall back to the CLI:
/generate-plot results.csv "Bar chart comparing model accuracy"
1---2name: generate-plot3description: Generate Plot4---56# Generate Plot78Generate a publication-quality statistical plot from a data file using PaperBanana.910## Instructions11121. Read the data file at `$ARGUMENTS[0]`.132. Prepare the data for the MCP tool:14 - If the file is **CSV**: parse it and convert to a column-keyed dictionary (keys = column names, values = arrays of column values), then serialize with `json.dumps()` to produce a JSON string.15 - If the file is **JSON**: use the raw file content as-is (it is already a JSON string).163. If `$ARGUMENTS[1]` is provided, use it as the plot intent. Otherwise, ask the user for a description of the desired plot (e.g., "Bar chart comparing model accuracy across benchmarks").174. Call the MCP tool `generate_plot` with:18 - `data_json`: the JSON string (not a parsed object)19 - `intent`: the plot description20 - `iterations`: 3 (default)215. Present the generated plot to the user.2223## CLI Fallback2425If the MCP tool is not available, fall back to the CLI:2627```bash28paperbanana plot --data <file> --intent "<intent>"29```3031## Example3233```34/generate-plot results.csv "Bar chart comparing model accuracy"35```
Run npx skillmds@latest add llmsresearch/generate-plot in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.
Generate Plot It is listed under Coding & Dev Tools on SkillMD.
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llmsresearch (@llmsresearch) published this skill. Their other Agent Skills are listed on their SkillMD profile.