paper-plot

name: paper-plot description: Use when structured numeric data, arrays, or CSV-like measurements should be turned into a publication-quality figure by adapting a bundled paper-style plotting template instead of improvising a new chart from scratch. skill_role: companion

Paper Plot

Use this skill when the job is to turn measured data into a paper-quality figure quickly and consistently. This companion skill is adapted from Trae1ounG/paper-plot-skills/tree/main/plot-from-data.

Interaction discipline

• Follow the shared interaction contract injected by the system prompt.
• If chart semantics, units, grouping, or the intended comparison are ambiguous, ask the user a focused follow-up question instead of guessing.
• When the first durable render is ready, send a concise progress update that says which style was chosen, what data source was used, and where the output was written.

Use when

• the user provides measured values, arrays, tables, or CSV-like data and wants a publication-quality figure
• the chart can be expressed as a bar, line, scatter, or radar plot using one of the bundled styles
• write, analysis-campaign, or experiment needs a first-pass paper-facing figure from structured results

Do not use when

• the job is only final visual QA or last-mile refinement of an already rendered figure; use figure-polish
• the figure is a disposable debug plot with no durable value
• the figure requires a custom multi-panel composition that clearly does not fit any bundled template

All bundled templates emit a dpi=300 PNG first. If a paper-facing final export needs vector output or further visual refinement, hand the result to figure-polish after the first-pass render.

Available Styles

Workflow

1. Confirm the chart question, units, grouping, and preferred output location.
2. Choose the closest bundled style; if two or more styles fit, ask the user or state the rationale.
3. Read `references/<style_name>.md` for the exact layout, color, and rcParams expectations.
4. Copy `scripts/<script>.py` into a quest-local figure workspace such as `paper/figures/scripts/<figure_id>.py`.
5. Replace only the clearly marked data and label section in the copied script; keep the bundled template immutable.
6. Run the copied script and inspect the rendered output.
7. If the figure is durable or paper-facing, hand the result to `figure-polish` before treating it as final.

Data Substitution Tips

Each template script keeps the editable data block near the top, usually as np.array(...) declarations or a small dictionary.

• Keep array rank and basic types stable unless you intentionally refactor the plotting logic.
• If the number of categories changes, update width calculations, color lists, tick labels, and legend labels together.
• Replace labels and legends directly in the copied script instead of post-editing the exported figure.
• Keep the source data path and generated script path next to the figure output so the figure remains reproducible.

Detailed Style Parameters

Read the corresponding file in references/ for exact rcParams, colors, font sizes, spine settings, and tick directions before generating:

• Bar: references/bar_paired_delta.md, references/bar_grouped_hatch.md
• Line: references/line_confidence_band.md, references/line_training_curve.md, references/line_loss_with_inset.md
• Scatter: references/scatter_tsne_cluster.md, references/scatter_broken_axis.md
• Radar: references/radar_dual_series.md

Relationship to other skills

• Use paper-plot for first-pass figure generation from structured data, especially for standard bar, line, scatter, and radar figure families.
• Use figure-polish for final render-inspect-revise work on durable milestone or paper-facing figures.
• In write, prefer this skill before inventing a new plotting stack for standard bar, line, scatter, or radar figures.