figure-style

Solid

Publication-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`.

Testing & QA 2 stars 0 forks Updated 3 days ago MIT

Install

View on GitHub

Quality Score: 78/100

Stars 20%
16
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Publication-Grade Figure Rules _A checklist for correct, legible, internally-consistent scientific figures. This skill does not impose a visual house style — frame, font, and palette are parameters. Load it and call `apply_figure_style()` before any plot._ ## §0 Scope §1–§3, §8, and §9 are **correctness** — they apply to every plot, in every context, and have no aesthetic content. §4–§7 are **guidance** — defaults that produce a clean result but that a deliberate alternative can override (individual rules inside §4–§7 that state a factual/perceptual invariant — e.g. §4.4 semantic-zero centring, §4.5 CVD, §6.9 leader anchoring — still bind). This skill is the inner tier (make one plot good); `figure-composer` assembles these panels into one multi-panel figure and `paper-narrative` reviews a whole paper's figure arc — the outer two tiers of the stack. --- ## §1 Data fidelity & self-consistency **1.1 Excluded rows.** A row marked excluded or flagged in the source data is either omitted entirely or drawn with a visually distinct open/hatched marker and named in the key. It **never** enters a summary statistic plotted alongside the included rows. **1.2 Comparable conditions only.** Arms measured under non-comparable conditions (different N, epoch budget, initialisation, protocol) are not plotted as visual peers. Separate them with a facet break or a marker on the label, and state the difference once in the caption. **1.3 Self-consistency.** Every key, threshold, and titl...

Details

Author
emaballarin
Repository
emaballarin/ccplugins
Created
3 months ago
Last Updated
3 days ago
Language
Python
License
MIT

Similar Skills

Semantically similar based on skill content — not just same category

Data & Documents Featured

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).

220 Updated today
Aperivue
AI & Automation Solid

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`.

2 Updated 3 days ago
emaballarin
AI & Automation Solid

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.

17 Updated 4 days ago
PangenomeAI