figure-style
SolidPublication-grade figure correctness and legibility rules. Load before drawing any plot and call `apply_figure_style()` — sets a role-mapped font-size ladder, outward ticks, frameless legends, and 300-dpi output. The skill is a checklist, not a house look: data fidelity (claim-titles tested against every row, excluded data never enters summaries), label economy (floor and ceiling), colour threading, chart-choice-by-data-shape, layout, and a render-then-verify QA loop (bbox collision + per-panel perceptual check). Ships helpers: focal_palette, bar_with_points, strip_with_median, end_of_line_labels, panel_letter, set_frame, panel_crops. For multi-panel figures load `figure-composer`; for whole-paper figure arc load `paper-narrative`.
Install
Quality Score: 79/100
Skill Content
Details
- Author
- emaballarin
- Repository
- emaballarin/ccplugins
- Created
- 3 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
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make-figures
Generate publication-ready figures and visual abstracts for medical research papers. Supports ROC curves, forest plots, CONSORT/STARD/PRISMA flow diagrams, calibration plots, Kaplan-Meier curves, Bland-Altman plots, confusion matrices, pipeline diagrams, and journal-specific visual/graphical abstracts (python-pptx template-based).
figure-composer
Compose one publication-grade multi-panel figure. Entry from a one-line claim + data refs, OR from an existing figure via `derive_outline_task(png)`. Runs a per-figure loop: outline (12-col grid, per-panel ask + label_budget) → fan-out one Task subagent per panel (each loads `figure-style`) → tile + stamp letters → adversarial composite review with two-tier feedback (Tier-1 outline_revisions / Tier-2 per-panel violations) → regen affected panels, ≤3 rounds. Kernel exposes panel_task / compose_figure / compose_crops / composite_review_task / derive_outline_task (import by absolute path). For one standalone plot use `figure-style`; for whole-paper figure ordering use `paper-narrative`.
food-figure
Comprehensive figure system for food & nutrition manuscripts: analyzes the user's data, recommends the best figure(s) to make, then produces submission-grade graphics in Python or R at the target journal's spec. Handles all common scientific figure types (bar/box/violin, line/kinetic, scatter/regression, Bland–Altman, radar/sensory, chromatograms, TPA/rheology, dose–response, survival, PCA/PLS-DA, heatmaps/clustering, forest, microscopy plates, multi-panel). Use to make, create, design, revise, audit, or recommend figures/charts/plots for a food-science paper, or to work out what to plot from a dataset. If Python or R isn't chosen, ask once and remember it. Triggers: make a figure, create a figure, design a figure, what figure should I make, recommend a chart, plot my data, analyze my data and plot it, chart my results, food science figure, journal figure, scientific plotting, data visualization for a manuscript.