paper-figures

Paper Figures

A structured approach to producing publication-ready chart figures (PNG) from tabular data plus a natural-language description, using matplotlib.

When to Use This Skill

• User provides a CSV / dataframe / inline data and asks for a chart
• User describes a target figure in words and wants it rendered
• User mentions "figure", "plot", "chart", "visualize", "render" for a paper or experiment

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Inputs and Output

Inputs the agent will receive:

• A data source: CSV file path, JSON, or inline table.
• A description: natural-language text specifying chart type, axes, title, colors, annotations, legend, scenarios, etc. Sometimes terse, sometimes a full paragraph. The description is the full specification — no reference image is provided.

Output (always):

• A standalone Python script plot.py that:
  • Loads the data from the provided source
  • Renders the figure with matplotlib
  • Saves a PNG via plt.savefig(..., dpi=300, bbox_inches="tight")
• The rendered plot.png next to it (the script is run and the PNG produced — do not stop at the script).

Verification artifacts (write when filesystem access is available):

• figure-spec.md — the compact figure specification extracted before coding.
• audit.md — the post-render audit checklist and any repairs made.
• final-status.md — one visible status label: PASSED, PASSED_WITH_WARNINGS, REPAIRED, or FAILED_NEEDS_HANDOFF.

Output directory:

• If the user specifies an output directory (e.g. "save to path/to/dir/"), write plot.py and plot.png inside that directory. Create the directory if it does not exist.
• If no directory is given, write to the current working directory.
• The two filenames are always plot.py and plot.png. Repeated runs on different inputs go to different directories, not different filenames — this keeps the script reference inside the PNG's neighbourhood stable and makes batch comparison easy.

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Core Workflow

Step 1: Plan Figure        -> verify: description/data ambiguity handled
Step 2: Extract Spec       -> verify: figure-spec.md has all required fields
Step 3: Implement          -> verify: plot.py runs and plot.png exists
Step 4: Audit Figure       -> verify: chart matches spec, data, and description
Step 5: Repair or Finalize -> verify: final-status.md is honest