add-figure

分类: 开发与编程 | 上传者: Runchuan-BU | 下载: 0 | 版本: v1.0（最新）
将仅 Python 的图形参考技能添加到 BioClaw 安装中。当用户希望在代理容器内提供出版质量的绘图指导而不添加源代码功能时使用。使用仅限 Python 的“SKILL.md”和根级“seaborn_reference.md”创建“container/skills/figure/”。
更新日志: Source: GitHub https://github.com/Runchuan-BU/BioClaw
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当前层级: tree/main/.claude/skills/add-figure/
📄 SKILL.md 10.0 KB
SKILL.md

---
name: add-figure
description: Add a Python-only figure reference skill to a BioClaw installation. Use when the user wants publication-quality plotting guidance inside agent containers without adding source-code features. Creates `container/skills/figure/` with a Python-only `SKILL.md` and a root-level `seaborn_reference.md`.
disable-model-invocation: true
---

# Add Figure Skill

This skill adds a new runtime skill at `container/skills/figure/` for publication-quality plotting inside BioClaw containers.

## What This Adds

- A new container runtime skill: `container/skills/figure/SKILL.md`
- A flattened seaborn reference: `container/skills/figure/seaborn_reference.md`
- Python-only plotting guidance for `matplotlib`, `seaborn`, `scanpy`, and related Python tools
- Journal palette constants translated to Python hex lists and dicts

## What This Must Not Change

- Do not modify any source files in `src/`, `container/agent-runner/`, or other app code
- Do not modify `Dockerfile`
- Do not modify sync logic in `src/container-runner.ts`
- Do not add any R dependency or R code examples

The feature is entirely delivered as a skill, not as application code.

## Why This Shape

BioClaw already syncs `container/skills/<skill>/` into `/home/node/.claude/skills/<skill>/` inside the container.

The sync only copies one directory level, so any reference material needed at runtime must live directly under `container/skills/figure/`, not under nested `references/`.

## Implementation Steps

Run all steps directly. Only pause if the repo already contains a user-modified `container/skills/figure/` and you need conflict resolution.

### 1. Verify Current State

Check:

```bash
pwd
ls -la container/skills
test -e container/skills/figure && echo "figure skill already exists" || echo "no figure skill yet"
```

If `container/skills/figure/` already exists, inspect it before changing anything.

### 2. Create `container/skills/figure/`

Create:

```text
container/skills/figure/
├── SKILL.md
└── seaborn_reference.md
```

Do not add nested directories.

### 3. Write `container/skills/figure/SKILL.md`

Create a Python-only runtime skill with frontmatter like:

```yaml
---
name: figure
description: Python-only reference for publication-quality academic figures inside BioClaw containers. Covers matplotlib, seaborn, scanpy-style workflows, journal palette constants, bioinformatics plot standards, anti-patterns, and a pre-flight quality checklist.
---
```

The file must include all of the following sections.

#### Required Section: Purpose and Rules

State clearly:

- Python only
- Save outputs to `/workspace/group/`
- Prefer `.pdf` or `.svg` for final figures
- Use `.png` only when raster is necessary, at `dpi >= 300`
- Root-level seaborn reference lives at `/home/node/.claude/skills/figure/seaborn_reference.md`

#### Required Section: Tool Routing

Include a short table routing these tasks to Python tools:

- General plots -> `seaborn` + `matplotlib`
- Annotation-heavy custom figures -> `matplotlib`
- Single-cell embeddings -> `scanpy`
- Clustered heatmaps -> `seaborn.clustermap`
- Set intersections -> `upsetplot`
- Survival curves -> `lifelines` + `matplotlib`
- Enrichment bubble plots -> `seaborn.scatterplot`

#### Required Section: Chart Type Selection

Include a Python-only chart selection table for:

- Histogram / density / violin
- Group comparison
- Scatter / hexbin
- Line / area
- Heatmap / clustered heatmap
- Volcano plot
- UMAP / t-SNE
- Relative composition
- Alpha diversity
- Beta diversity / ordination
- Enrichment bubble plot
- Kaplan-Meier curve
- Multi-panel layout

Every row must reference Python functions only.

#### Required Section: Publication-Quality Defaults

Include a code block defining:

- `BIO_STYLE`
- `SAVEFIG_KWARGS`
- `finish_axis(ax)`

The style must:

- Use sans-serif fonts with Arial/Helvetica fallbacks
- Remove top and right spines
- Set figure and save DPI to 300
- Set `savefig.bbox` to `tight`
- Keep visible text at publication-safe sizes
- Preserve editable SVG text with `svg.fonttype = "none"`

#### Required Section: Universal Standards

Document:

- Vector first
- Raster at 300+ DPI
- No on-image title
- Labeled axes with units
- Legend placement
- Panel labels for multi-panel figures
- Tight margins
- Minimum text size

#### Required Section: Color Standards

Include:

- Discrete palette usage guidance
- Continuous palette usage guidance
- Colorblind and consistency rules
- Explicit ban on `jet` and `rainbow`

#### Required Code Block: Python Palette Constants

Define a `JOURNAL_PALETTES` dict containing these keys:

- `NPG`
- `Lancet`
- `AAAS`
- `JCO`
- `NEJM`
- `D3_CATEGORY10`
- `D3_CATEGORY20`

Define a `MATERIAL_PALETTES` dict containing named color families such as:

- `red`
- `pink`
- `purple`
- `deep_purple`
- `indigo`
- `blue`
- `light_blue`
- `cyan`
- `teal`
- `green`
- `light_green`
- `lime`
- `yellow`
- `amber`
- `orange`
- `deep_orange`
- `brown`
- `gray`
- `blue_gray`

Also define:

```python
def apply_palette(name, n=None):
    colors = JOURNAL_PALETTES[name]
    return colors if n is None else colors[:n]
```

Use these exact journal palette hex values:

```python
JOURNAL_PALETTES = {
    "NPG": [
        "#E64B35", "#4DBBD5", "#00A087", "#3C5488", "#F39B7F",
        "#8491B4", "#91D1C2", "#DC0000", "#7E6148", "#B09C85",
    ],
    "Lancet": [
        "#00468B", "#ED0000", "#42B540", "#0099B4",
        "#925E9F", "#FDAF91", "#AD002A", "#ADB6B6",
    ],
    "AAAS": [
        "#3B4992", "#EE0000", "#008B45", "#631879", "#008280",
        "#BB0021", "#5F559B", "#A20056", "#808180", "#1B1919",
    ],
    "JCO": [
        "#0073C2", "#EFC000", "#868686", "#CD534C", "#7AA6DC",
        "#003C67", "#8F7700", "#3B3B3B", "#A73030", "#4A6990",
    ],
    "NEJM": [
        "#BC3C29", "#0072B5", "#E18727", "#20854E",
        "#7876B1", "#6F99AD", "#FFDC91", "#EE4C97",
    ],
    "D3_CATEGORY10": [
        "#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD",
        "#8C564B", "#E377C2", "#7F7F7F", "#BCBD22", "#17BECF",
    ],
    "D3_CATEGORY20": [
        "#1F77B4", "#AEC7E8", "#FF7F0E", "#FFBB78", "#2CA02C",
        "#98DF8A", "#D62728", "#FF9896", "#9467BD", "#C5B0D5",
        "#8C564B", "#C49C94", "#E377C2", "#F7B6D2", "#7F7F7F",
        "#C7C7C7", "#BCBD22", "#DBDB8D", "#17BECF", "#9EDAE5",
    ],
}
```

Translate the Material palette into Python hex lists under `MATERIAL_PALETTES`. Keep it as a dict of family name -> 10-step tonal list.

#### Required Section: Bioinformatics-Specific Plot Standards

Include Python examples and standards for:

- UMAP / t-SNE
- Heatmap
- Volcano plot
- Relative composition bar plot
- Alpha diversity
- Beta diversity / ordination
- Enrichment bubble plot
- Survival curve

The examples must use Python syntax only.

The section must explicitly require:

- UMAP/t-SNE axes without ticks
- Heatmaps with visible color bars
- Volcano plots with cutoff lines at `abs(log2FC) >= 1` and `padj <= 0.05`
- Relative abundance y-axis constrained appropriately
- Alpha diversity shown with both summary and individual points
- PCoA axes labeled with explained variance when available
- Survival plots labeled with time unit

#### Required Section: Quality Checklist

Include a pre-flight checklist covering:

- Output format / DPI
- Axis labels and units
- No in-figure title
- Legend placement
- Panel labels
- Color consistency
- Colorblind-safe interpretation
- Statistical annotation correctness
- Minimum font size
- No chartjunk

#### Required Section: Anti-Patterns

Include a table forbidding at least:

- Default unstyled plots
- Too many cluster colors in one panel
- UMAP/t-SNE with ticks
- Heatmap without labeled color bar
- Volcano plot without thresholds
- Bar plots that hide variability
- Overlapping labels

#### Required Section: Working Rules

End with rules such as:

- Prefer reusable plotting code over one-off snippets
- Keep filenames stable and descriptive
- Keep figure code reproducible in the workspace
- Fall back to plain `matplotlib` if a preferred library is unavailable

### 4. Write `container/skills/figure/seaborn_reference.md`

Create a root-level seaborn reference file for shallow-copy sync. It should be concise but directly usable.

It must include:

- Title: `# Seaborn API Reference`
- Source note pointing to https://seaborn.pydata.org/
- Objects interface example using `seaborn.objects as so`
- Short tables for marks and stats
- Sections for:
  - Relational plots
  - Distribution plots
  - Categorical plots
  - Regression plots
  - Matrix plots
  - Multi-plot grids
  - Style and context
- A final `## Common Patterns for Bio` section with examples for:
  - Volcano plot
  - Expression heatmap with clustering
  - Violin + strip
  - UMAP embedding

Keep the file at the skill root. Do not place it under `references/`.

### 5. Explicit Removals and Exclusions

The generated `container/skills/figure/SKILL.md` must not contain:

- `AutoFigure-Edit`
- `ggplot2`
- `ComplexHeatmap`
- `clusterProfiler`
- `BioCircos`
- `circlize`
- `R Graph Gallery`
- `r4ds`
- Any `geom_` examples
- Any R code fences

This skill is intentionally Python-only because the BioClaw container does not include R.

### 6. Validation

Run:

```bash
ls -la container/skills/figure

rg -n "AutoFigure|ggplot2|ComplexHeatmap|clusterProfiler|BioCircos|circlize|R Graph Gallery|r4ds|geom_" \
  container/skills/figure

rg -n "JOURNAL_PALETTES|MATERIAL_PALETTES|NPG|Lancet|AAAS|JCO|NEJM|D3_CATEGORY10|D3_CATEGORY20" \
  container/skills/figure/SKILL.md
```

If Docker is available, also verify in-container readability:

```bash
docker run --rm --entrypoint sh \
  -v "$PWD/container/skills/figure:/home/node/.claude/skills/figure:ro" \
  bioclaw-agent:latest \
  -lc 'ls -1 /home/node/.claude/skills/figure && sed -n "1,10p" /home/node/.claude/skills/figure/SKILL.md'
```

### 7. Final Output to the User

Report:

- Which files were created
- That no source files were modified
- That the skill is Python-only
- Validation results

If preparing for a PR, remind the user that the PR should only include the new contributed skill under `.claude/skills/add-figure/` when contributing back to the BioClaw repo.
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add-figure

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