# Dr Cook:data Visualizer

> Generate publication-quality scientific figures and visualizations for academic manuscripts. Use when creating data visualization, plot, figure, chart, graph, heatmap, volcano plot, boxplot, violin plot, PCA plot, UMAP, survival curve, Kaplan-Meier, forest plot, scientific figure, publication figure, ggplot, matplotlib, R plot, Python plot, enrichment bubble chart, PPI network visualization, network figure, 数据可视化, 画图, 图表, 可视化. Do NOT trigger for: bioinformatics-assistant (analysis pipelines and computational methods), method-designer (experimental design and study planning).

- Skill: `wen-chen/dr-cook-data-visualizer` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add wen-chen/dr-cook-data-visualizer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/wen-chen/dr-cook-data-visualizer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: wen-chen (https://skillmd.com/u/wen-chen)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/wen-chen/dr-cook-data-visualizer

---


# data-visualizer

## 1. Overview

data-visualizer produces publication-quality figures for academic manuscripts, generating complete, runnable code in ggplot2 or matplotlib with correct resolution, font sizes, and color palettes. Three figure categories are supported.

**Omics figures:** Volcano plots, heatmaps, PCA/UMAP scatter plots, and enrichment bubble charts — produced from bioinformatics analysis outputs such as DESeq2 results or clusterProfiler enrichment tables. Typically downstream from bioinformatics-assistant.

**Clinical and statistical figures:** Boxplots, violin plots, Kaplan-Meier survival curves, and forest plots for meta-analyses. These communicate group comparisons, patient outcomes, and pooled effect sizes.

**Network figures:** PPI network visualization and Compound-Target-Pathway figures for TCM network pharmacology. Output is Cytoscape-ready edge/node tables or R igraph code.

## 2. Parameters

### Required

| Parameter | Values | Description |
|---|---|---|
| `figure_type` | `volcano` \| `heatmap` \| `pca` \| `umap` \| `boxplot` \| `violin` \| `survival` \| `forest` \| `ppi_network` \| `enrichment_bubble` \| `custom` | Which plot type to generate |
| `data_description` | string | Description of input data — pasted data table, column names, or description of R object |

### Optional

| Parameter | Values | Description |
|---|---|---|
| `tool` | `ggplot2` \| `matplotlib` \| `base_R` \| `cytoscape` (default: `ggplot2`) | Plotting library or tool |
| `color_scheme` | `default` \| `colorblind_safe` \| `tcm_palette` \| `journal_bw` (default: `default`) | Color palette to apply |
| `output_format` | `pdf` \| `png` \| `svg` (default: `pdf`) | File format for the saved figure |
| `domain` | `tcm` \| `bioinformatics` \| `clinical` \| `pharmacology` | Research domain; loaded from context_output if available |
| `target_journal` | string | Journal name; affects figure dimensions and resolution requirements |
| `language` | `en` \| `zh` (default: `en`) | Language for axis labels and legend text |

## 3. Workflow

**Step 1 — Check upstream context_output.**
Inherit `parameters.domain`, `parameters.target_journal`, and `parameters.language` without asking. If `raw_text` contains tabular data (e.g., DESeq2 columns `log2FoldChange`, `padj`, `gene`), treat it as `data_description`.

**Step 2 — Collect figure_type and data_description.**
If `figure_type` is not specified by the user, present the figure menu:

> "Which type of figure would you like?
> (1) Volcano plot — DEG results
> (2) Heatmap — expression matrix
> (3) PCA / UMAP — dimensionality reduction
> (4) Boxplot / Violin plot — group comparison
> (5) Survival curve (Kaplan-Meier) — time-to-event data
> (6) Forest plot — meta-analysis effect sizes
> (7) Network figure — PPI or Compound-Target-Pathway
> (8) Enrichment bubble chart — GO/KEGG results
> (9) Describe your own figure"

After `figure_type` is known, confirm `data_description` if not already provided. Accept a pasted data sample, column name list, or reference to an upstream R object (e.g., "DESeq2 `res` with gene, log2FoldChange, padj columns").

**Step 3 — Confirm tool and color scheme.**
Default `tool` to `ggplot2`. If `target_journal` is known, auto-suggest color scheme:
- Nature, Science, Cell → `journal_bw`; note "Color figures incur additional fees in print."
- Chinese journals → `default` (full color acceptable).
- All others → `default`.

Apply the suggested or default palette without asking if `color_scheme` is unspecified.

**Step 4 — Load plot-templates.md.**
Load `references/plot-templates.md` and retrieve the template matching `figure_type`. Use it as the structural foundation for Step 6. For `custom`: ask "Please describe your figure in 1–2 sentences," then select the closest template:
- Expression/omics data → volcano or heatmap
- Group comparisons/distributions → boxplot or violin
- Time-to-event data → survival
- Odds ratios/effect sizes → forest plot
- None apply → volcano scaffold; note which geoms and axes to replace

**Step 5 — Load color-palettes.md.**
Load `references/color-palettes.md` and extract hex values for the requested `color_scheme`. Confirm palette compatibility — `journal_bw` requires shape/pattern differentiation in addition to grayscale fills.

**Step 6 — Generate figure code.**
Produce complete, runnable R or Python code from the Step 4 template and Step 5 palette. Every code block must include:

- **Data format note** before the code: "Your data should be formatted as: [column names and types]."
- **Palette application** via `scale_color_manual()`, `scale_fill_manual()`, or `ggsci` helpers.
- **Publication settings**: `theme_classic(base_size = 10)`; axis font 10pt; title 12pt; legend 8pt; 300 dpi minimum.
- **Save call**: `ggsave()` or `plt.savefig()` with explicit `width`, `height`, `dpi`, `units = "mm"` per journal spec.
- **Chinese font note** when `language: zh`: include `library(showtext); showtext_auto()` and replace all label strings with Chinese text.

**Step 7 — Offer iteration.**
End every response with:

> "Would you like to adjust colors, resize for a specific journal format, add statistical annotations (e.g., significance brackets, p-value labels), switch to a different tool, or modify any other aspect of the figure?"

## 4. Output Format

Begin every response with the figure header:

```
[Figure: <Figure Type> | Tool: <tool> | Color: <color_scheme>]
```

Example: `[Figure: Volcano Plot | Tool: ggplot2 | Color: Colorblind-safe]`

Follow the header with:

1. **Data format requirement block** — a short prose or bulleted list stating exactly what columns/structure the input data must have.
2. **Complete code block** — language-tagged fence (` ```r ` or ` ```python `), complete and runnable.
3. **"Save as:" line** — shows the `ggsave()` or `plt.savefig()` call with explicit `width`, `height`, `dpi`, and `units = "mm"` arguments.
4. **Journal-specific note** — only when `target_journal` is set. Example: "Nature figures: max 180 mm wide (double-column) or 89 mm (single-column), 300 dpi minimum, TIFF or PDF preferred."

## 5. context_output

### Reads from upstream

| Field | Source | Usage |
|---|---|---|
| `parameters.domain` | any upstream module | Avoids re-asking for domain |
| `parameters.target_journal` | any upstream module | Auto-selects figure dimensions and color scheme |
| `parameters.language` | any upstream module | Sets axis label and legend language |
| `raw_text` | bioinformatics-assistant, paper-writer | Used as data input when it contains tabular results |

### Writes to output

```json
{
  "module": "data-visualizer",
  "summary": "<e.g., 'Volcano plot code generated for human DEG data, ggplot2, colorblind-safe palette'>",
  "artifacts": ["volcano_plot.pdf"],
  "parameters": {
    "figure_type": "<volcano | heatmap | pca | umap | boxplot | violin | survival | forest | ppi_network | enrichment_bubble | custom>",
    "tool": "<ggplot2 | matplotlib | base_R | cytoscape>",
    "color_scheme": "<default | colorblind_safe | tcm_palette | journal_bw>"
  },
  "status": "success | partial | failed",
  "error_message": "<string | null>"
}
```

`artifacts` is always a list of strings, each being an expected output file name (e.g., `["heatmap.pdf"]`). `status = partial` when `data_description` is insufficient to generate runnable code. `status = failed` if `figure_type` could not be determined after two attempts.

## 6. References

See `references/` for:
- `plot-templates.md` — Complete ggplot2/R code templates for volcano plot, heatmap, Kaplan-Meier survival curve, forest plot, and publication size guidelines
- `color-palettes.md` — NPG default, colorblind-safe (Wong 2011), TCM earth tones, journal grayscale palettes with ggplot2 application code and accessibility rules