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name: figures4papers description: "Create, polish, and troubleshoot figures4papers-style publication-ready matplotlib figures, including grouped bars, trends, radar charts, heatmaps, concept diagrams, manifolds, and high-DPI/vector export workflows." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

figures4papers

Use this repo skill when the user wants publication-quality Python/matplotlib figures in the figures4papers house style: polished paper plots, wide comparison panels, print-safe encodings, radar or heatmap layouts, conceptual manifolds, clean typography, and reproducible PNG/PDF/SVG export.

This repository is a script-and-skill collection, not an installable Python package. Future agents can use this generated skill without the original checkout by following the bundled references and scripts below.

Load this skill when

• The user asks for a paper, slide, or report figure rather than exploratory data analysis.
• The figure should use a consistent publication look: sans-serif typography, blue/green/red/neutral palette roles, clean spines, high DPI, and vector text.
• The request mentions grouped bars, ablations, wide metric comparisons, legend-only axes, trend lines, event timelines, radar/polar comparisons, heatmaps, matrix panels, concept distributions, manifolds, Swiss-roll diffusion visuals, shaded spheres, or geodesic/arrow diagrams.
• The user needs to adapt a safe template, validate data shapes, create output directories, or fix matplotlib font, TeX, backend, or export errors.
• The task names figures4papers or asks for the same style as a figures4papers example.

Do not load for

• Interactive dashboards or web visualization with Plotly, Altair, Bokeh, or dashboard frameworks.
• GIS/geographic mapping, Figma/Illustrator-first infographic work, or purely exploratory plots without a publication target.
• Model training, data analysis, evaluation, or paper reproduction where plotting is incidental rather than the requested deliverable.
• Exact historical figure reproduction that would require unbundled data or source files. Use the distilled recipes and ask for user-provided data instead.

Minimal environment

Install the plotting packages needed by the selected route:

python -m pip install numpy matplotlib scipy seaborn python-dateutil

numpy and matplotlib cover the core templates. scipy supports KDE, diffusion, and manifold helpers; seaborn supports seaborn-style heatmaps; python-dateutil helps date/month timelines. System LaTeX is optional and only needed when exact text.usetex=True rendering is required. Portable bundled scripts keep TeX disabled by default.

Run the environment checker when import or export behavior is uncertain:

python scripts/check_figure_env.py --output figure_env_smoke --formats png

Route map

Task	Open
Grouped bars, horizontal ablations, log-speed bars, multi-metric comparison rows, direct annotations, legend-only axes	sub-skills/bar-comparison-figures/SKILL.md
Trend/line panels, cumulative timelines, event arrows, radar/polar benchmark comparisons, heatmaps, colorbars, matrix panels	sub-skills/trend-radar-heatmap-figures/SKILL.md
Concept distributions, manifold clouds, KDE contours, Swiss-roll/diffusion matrices, shaded spheres, geodesic arrows, 3D-style geometry	sub-skills/concept-manifold-diagrams/SKILL.md
Shared style, palette, rcParams, export formats, and helper functions	references/style-and-export.md
Natural-language pattern routing across all figure families	references/project-patterns.md
Cross-cutting install/import, font, TeX, backend, shape, and output failures	references/troubleshooting.md
Repository snapshot and refresh baseline	references/repo-provenance.md

Shared scripts

• scripts/figure_style_helpers.py provides reusable palette constants, FigureStyle, apply_publication_style, create_subplots, finalize_figure, grouped bar, trend, heatmap, scatter, and shaded-sphere helpers.
• scripts/check_figure_env.py verifies required plotting imports, optional dependencies, LaTeX availability, and headless PNG export.

Operating procedure

1. Classify the figure family from the route map before writing code.
2. Read the nearest sub-skill and its recipe reference; use the bundled template when the user has not provided a mature plotting script.
3. Normalize user data into the sub-skill's data contract and validate shapes, label counts, finite values, metric directions, and missing-value policy.
4. Apply the shared style: semantic palette roles, clean spines, readable fonts, frameless legends, print-safe edges/hatches where needed, and TeX off unless explicitly required.
5. Save deterministic outputs in the requested formats, create parent directories, close figures, and verify nonzero file sizes.
6. If a failure appears, use root troubleshooting first, then the owning sub-skill's troubleshooting reference for workflow-specific recovery.

Ask before finalizing when

• The metric direction, missing-value policy, or synthetic-vs-measured status is unclear.
• A tight axis range could be misleading or a zero baseline may be required.
• The user did not specify mandatory figure dimensions, formats, DPI, or venue constraints and those choices affect layout.
• Exact TeX rendering or a specific font is required but not available.
• A requested composite figure spans several sub-skills and the panel order or story should be user-approved.

Refresh and import notes

Read references/repo-provenance.md before refreshing this skill against a changed repository. This candidate was generated for DisCo's managed repo-skill format, but this construction run intentionally does not import it into the live repo-skills library.