hinna0818/ukbanalytica_v2/ukbsci-plot

Name: ukbsci-plot
Author: hinna0818

Claude Code and agent skill by hinna0818/ukbanalytica_v2

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By hinna0818
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hinna0818/ukbanalytica_v2
inst/skills/UKBAnalytica_skills/ukbsci-plot/SKILL.md
Updated 20 Sep 2026
Version 1.0.01 version recorded

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Publication-grade figure production for UK Biobank Research Analysis Platform (RAP) analyses built with UKBAnalytica. Wraps the package's built-in plotters — plot_forest (subgroup / regression forest), plot_calibration (clinical-model calibration), plot_regression_volcano (multi-exposure / multi-protein volcano), plot_heatmap, plot_stacked_bar, plot_violin, and plot_scatter for lightweight exploratory/manuscript panels, plus the family of survival, ML, SHAP, mediation, propensity, imputation, correlation, and enrichment plots from sibling skills — and adds a shared neutral theme (ukbsci_clinical / ukbsci_diverging / ukbsci_sequential palettes), a dual-format save helper, and a figure-contract checklist. Use this skill when the user asks for any manuscript figure derived from UKBAnalytica outputs: forest plots, volcano plots, KM curves, calibration plots, Love plots, SHAP summaries, multi-panel composites, or PDF / SVG high-resolution export. Triggers: UKB plotting, forest plot, volcano, calibration, manuscript figure, ggplot helper, ukbsci 画图, 论文级图, /ukbsci-plot. Hard rule: local agents must not read or inspect real UKB RAP participant-level data; generate scripts for RAP execution and interpret aggregate outputs only.

Install

npx vskill@latest install hinna0818/ukbanalytica_v2/ukbsci-plot

SKILL.md

ukbsci-plot — Manuscript figure production for UKBAnalytica

0. RAP guardrails

Core rule: do not export UK Biobank participant-level raw data, direct identifiers (eid, exact dates, raw RAP fields), or row-level source tables that can be linked back to participants.

Strict local-agent boundary: this skill is for script generation, workflow planning, package guidance, and interpretation of aggregate outputs. The agent must not read, inspect, summarize, or process real UK Biobank RAP participant-level data, including de-identified row-level tables, raw RAP fields, exact dates, per-row predictions, row-level SHAP matrices, screenshots, tracebacks, or logs containing row-level values. Generate scripts for the user to run inside RAP; only aggregate results or rendered figures may be shared back with the agent. See ../references/agent-privacy-boundary.md.

Output	Sharing rule
Rendered PDF / SVG / PNG figure	Shareable if it does not expose identifiers, exact dates, raw RAP fields, row labels, or data previews.
`05-figs/data/Fig*.csv` source CSV	Shareable only if aggregate (per-bin, per-strata, per-feature means/CIs). Never share a source CSV that contains raw per-participant values or identifiers.
Coefficient / metric table from `04-results/*.csv`	Shareable — aggregate only.
Per-participant raw values used to build a figure	Do not export. Keep intermediate data on RAP.
Individual-level interpretation figure (e.g. SHAP force plot)	Do not use a real participant row with the local agent. Generate synthetic or representative prototype-row figures inside RAP if needed.

1. When to load

Build the manuscript-ready forest plot of the main Cox / subgroup result.
Produce calibration / DCA / ROC for a clinical model (paired with

ukbsci-ml).

Volcano of a protein / metabolite / batch regression screen.
Polished multi-panel figures combining outputs from multiple skills.
Standardized save (.pdf + .png + .svg) at 300 dpi.

2. When NOT to load

The user wants a KM curve only → ukbsci-survival::plot_km_curve (this

skill defers to it).

The user wants Love / PS distribution → ukbsci-propensity plots.
The user wants mediation / MI / SHAP plots → use the originating skill's

plotter; this skill provides the shared theme and save helper.

3. Figure contract (always run first)

For any figure the user asks for, restate three things before plotting:

Claim — the one-sentence conclusion the figure must support.
Evidence panel(s) — which underlying CSV(s) from 04-results/ feed

the figure.

Export contract — final size (mm), single-column / double-column,

font family, palette family.

A figure that cannot be re-rendered from 05-figs/data/Fig*.csv is not acceptable.

4. Shared theme & palettes (define once, reuse everywhere)

UKBAnalytica does not ship custom themes. This skill defines the following neutral helpers — copy verbatim into each plotting script:

library(ggplot2)
 
ukbsci_theme <- function(base_size = 7, base_family = "Arial") {
  theme_classic(base_size = base_size, base_family = base_family) +
    theme(
      axis.line       = element_line(linewidth = 0.35, colour = "black"),
      axis.ticks      = element_line(linewidth = 0.35, colour = "black"),
      legend.title    = element_text(size = base_size * 0.95),
      legend.text     = element_text(size = base_size * 0.85),
      strip.text      = element_text(size = base_size * 0.95, face = "bold"),
      plot.title      = element_text(size = base_size * 1.1, face = "bold"),
      panel.grid      = element_blank()
    )
}
 
# Categorical palette — clinical / two-arm + accent
ukbsci_clinical <- c(
  baseline   = "#2F6FA3",
  exposure   = "#C74732",
  control    = "#7F7F7F",
  accent     = "#F2A93B",
  highlight1 = "#3D8B66",
  highlight2 = "#7A4E9B"
)
 
# Diverging palette — for heatmaps / SMD / signed effects
ukbsci_diverging <- c("#2166AC", "#67A9CF", "#D1E5F0",
                      "#F7F7F7",
                      "#FDDBC7", "#EF8A62", "#B2182B")
 
# Sequential palette — for density / continuous gradients
ukbsci_sequential <- c("#FFF7EC", "#FEE8C8", "#FDD49E",
                       "#FDBB84", "#FC8D59", "#EF6548",
                       "#D7301F", "#990000")
 
# Apply the theme once, globally, at the top of a script
theme_set(ukbsci_theme())

These names are intentionally generic — they do not reference any specific journal brand.

5. Standardized save helper

save_ukbsci_figure <- function(plot, name,
                                width_mm = 180, height_mm = 120,
                                dpi = 300,
                                outdir = "/mnt/project/<area>/05-figs") {
  dir.create(outdir, showWarnings = FALSE, recursive = TRUE)
  w <- width_mm / 25.4; h <- height_mm / 25.4
 
  # SVG (editable)
  svglite::svglite(file.path(outdir, paste0(name, ".svg")), width = w, height = h)
  print(plot); dev.off()
 
  # PDF (vector, cairo for fonts)
  grDevices::cairo_pdf(file.path(outdir, paste0(name, ".pdf")),
                       width = w, height = h, family = "Arial")
  print(plot); dev.off()
 
  # PNG (raster, 300 dpi)
  ragg::agg_png(file.path(outdir, paste0(name, ".png")),
                width = w, height = h, units = "in", res = dpi)
  print(plot); dev.off()
}

Manuscript single-column ≈ width_mm = 89; double-column ≈ 180. Use a height that yields golden-ratio-ish aspect unless the figure dictates otherwise.

6. UKBAnalytica plot families (where each one lives)

Plot	Provided by	Notes
Forest (subgroup / batch reg)	`UKBAnalytica::plot_forest`	This skill — see §7
Calibration	`UKBAnalytica::plot_calibration`	This skill — see §8
Volcano	`UKBAnalytica::plot_regression_volcano`	This skill — see §9
Heatmap	`UKBAnalytica::plot_heatmap`	Long-format matrix-style data
Stacked bar	`UKBAnalytica::plot_stacked_bar`	Categorical composition
Violin	`UKBAnalytica::plot_violin`	Grouped continuous distributions
Scatter	`UKBAnalytica::plot_scatter`	Exposure-response or feature-feature trends
KM survival	`plot_km_curve`	`ukbsci-survival`
Love / PS distribution	`plot_balance`, `plot_ps_distribution`	`ukbsci-propensity`
Mediation effects	`plot_mediation`, `plot_mediation_forest`	`ukbsci-mediation`
MI diagnostics	`plot_mi_pooled`, `plot_mi_diagnostics`	`ukbsci-imputation`
GO bar / lollipop	`plot_go_ora_bar`, `plot_enrichment_lollipop`	`ukbsci-proteomics`
Correlation heatmap	`plot_correlation`	this skill (re-export-friendly)
ML evaluation	`plot_ml_roc`, `plot_ml_calibration`, `plot_ml_dca`, …	`ukbsci-ml`
SHAP	`plot_shap_summary`, `plot_shap_beeswarm`, `plot_shap_dependence`, `plot_shap_force`	`ukbsci-ml`

When mixing plots from sibling skills, apply the same theme via theme_set(ukbsci_theme()) at the top of the orchestrating script and override colors using the ukbsci_* palettes.

7. Forest plot — `plot_forest()`

plot_forest(results,
            estimate_col      = "estimate",
            lower_col         = "lower95",
            upper_col         = "upper95",
            label_col         = "subgroup",
            pvalue_col        = "pvalue",
            p_interaction_col = "p_interaction",
            null_value        = 1,
            log_scale         = TRUE,
            colors            = NULL,
            title             = "Subgroup Analysis",
            xlab              = "Hazard Ratio (95% CI)",
            show_n            = TRUE,
            show_events       = TRUE)

Input: any data.frame with at least estimate, lower95, upper95, label. Naturally consumes outputs of runmulti_cox / run_subgroup_analysis / run_multi_subgroup / runmulti_competing / pool_mi_models$pooled / tidy().

8. Calibration — `plot_calibration()`

plot_calibration(data, predicted, observed,
                 n_bins   = 10,
                 smooth   = TRUE,
                 conf_int = TRUE)

Input: per-prediction rows with predicted probabilities and binary outcome. Note: this is participant-level so the data argument should come from flow$final_test_predictions (which stays on RAP); the figure itself summarises into n_bins bins, so the rendered output is aggregate.

9. Volcano — `plot_regression_volcano()`

plot_regression_volcano(data,
                        effect_col          = NULL,
                        p_col               = "pvalue",
                        adjusted_p_col      = NULL,
                        label_col           = NULL,
                        significance_cutoff = 0.05,
                        top_n_label_each    = 5,
                        null_effect         = 1,
                        x_lab               = NULL,
                        y_lab               = NULL,
                        x_limits            = NULL,
                        y_limits            = NULL,
                        point_size          = 1.05,
                        label_size          = 2,
                        colors              = c(neutral = "#D8D8D8",
                                                 lower   = "#2F6FA3",
                                                 higher  = "#C74732"),
                        show_cutoff         = TRUE)

Auto-detects effect_col (HR → OR → estimate) and label_col (gene_symbol → protein_clean → variable). The output's attr(., "plot_data") and attr(., "label_data") give the aggregate underlying tables — write those to 05-figs/data/.

10. Lightweight plot helpers

Use these for compact exploratory or supplementary panels when the user has an aggregate or de-identified plotting table.

plot_heatmap(cor_df, x = "trait", y = "protein", fill = "rho")
plot_stacked_bar(tab_df, x = "risk_group", fill = "case_status", position = "fill")
plot_violin(plot_df, x = "group", y = "marker", fill = "group")
plot_scatter(plot_df, x = "exposure", y = "outcome", color = "group")

Inputs must use column names as strings. Do not pass eid, exact dates, or row-level raw RAP fields into local-agent-visible source CSVs.

11. Multi-panel composition

patchwork is the recommended layout engine:

library(patchwork)
top  <- p_forest + p_volcano + plot_layout(widths = c(1, 1.2))
mid  <- p_calib  + p_dca     + plot_layout(widths = c(1, 1))
fig  <- top / mid +
        plot_annotation(tag_levels = "a") &
        ukbsci_theme()
save_ukbsci_figure(fig, "Fig01-composite",
                   width_mm = 180, height_mm = 200)

plot_annotation(tag_levels = "a") gives lowercase a, b, c, … panel labels — common across observational epidemiology.

12. Common pitfalls

This is the start of the file. Read the full SKILL.md on GitHub.

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Source

SKILL.md in the repositoryCheckinginst/skills/UKBAnalytica_skills/ukbsci-plot/SKILL.md

Popularity

Repository stars: 35
7-day trend score: 18

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 2 Sep 2026, 2 months ago.

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