academic-plotting

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• SKILL.md
• data-visualization.md · references/
• diagram-generation.md · references/
• style-guide.md · references/

SKILL.md (first lines)

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  来源仓库: https://github.com/Orchestra-Research/AI-Research-SKILLs
  项目名称: AI-Research-SKILLs
  开源协议: Apache License 2.0
  收录日期: 2026-04-02

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# Academic Plotting for ML Papers

Generate publication-quality figures for ML/AI conference papers. Two distinct workflows:

1. **Diagram figures** (architecture, system design, workflows, pipelines) — AI image generation via Gemini
2. **Data figures** (line charts, bar charts, scatter plots, heatmaps, ablations) — matplotlib/seaborn

## When to Use Which Workflow

| Figure Type | Tool | Why |
|-------------|------|-----|
| Architecture / system diagram | Gemini (Workflow 1) | Complex spatial layouts with boxes, arrows, labels |
| Workflow / pipeline / lifecycle | Gemini (Workflow 1) | Multi-step processes with connections |
| Bar chart, line plot, scatter | matplotlib (Workflow 2) | Precise numerical data, reproducible |
| Heatmap, confusion matrix | matplotlib/seaborn (Workflow 2) | Structured grid data |
| Ablation table as chart | matplotlib (Workflow 2) | Grouped bars or line comparisons |
| Pie / donut chart | matplotlib (Workflow 2) | Proportional data (use sparingly in ML papers) |
| Training curves | matplotlib (Workflow 2) | Loss/accuracy over steps/epochs |

**Rule of thumb**: If the figure has numerical axes, use matplotlib. If the figure has boxes and arrows, use Gemini.

---

## Step 0: Context Analysis & Extraction

The user will typically provide one of these inputs — not a ready-made specification:

| Input Type | Example | What to Extract |
|-----------|---------|-----------------|
| Full paper / section draft | "Here's our method section..." | System components, their relationships, data flow |
| Description paragraph | "Our system has three layers that..." | Key entities, hierarchy, connections |
| Raw results / data table | "MMLU: 85.2, HumanEval: 72.1..." | Metrics, methods, comparison structure |
| CSV / JSON data | Experiment log files | Variables, trends, grouping dimensions |
| Vague request | "Make a figure for the overview" | Read surrounding paper context to infer content |

### Extraction Workflow

**For diagrams** (research context → architecture figure):

1. **Read the provided context** — paper section, abstract, or description paragraph
2. **Identify visual entities** — What are the main components/modules/stages?
   - Look for: nouns that represent system parts, named modules, layers, stages
   - Count them: if >8 top-level entities, consider grouping into sections
3. **Identify relationships** — How do components connect?
   - Look for: verbs describing data flow ("sends to", "queries", "feeds into")
   - Classify: data flow (solid arrow), control flow (gray), error path (dashed red)
4. **Determine layout pattern**:
   - Sequential pipeline → left-to-right flow
   - Layered architecture → horizontal bands stacked vertically
   - Hub-and-spoke → central node with radiating connections
   - Hierarchical → top-down tree
5. **Assign colors** — One accent color per logical group/layer
6. **Write every label exactly** — Extract exact terminology from the paper text

**For data charts** (results → figure):

1. **Read the provided data** — table, paragraph with numbers, CSV, or JSON
2. **Identify dimensions**:
   - What is being compared? (methods, models, configurations) → categorical axis
   - What is the metric? (accuracy, loss, latency, F1) → value axis
   - Is there a time/step dimension? → line plot
   - Are there multiple metrics? → multi-panel or grouped bars
3. **Choose chart type** automatically using this priority:
   - Has a step/time axis → **line plot**
   - Comparing N methods on M benchmarks → **grouped bar chart**
   - Single ranking → **horizontal bar** (leaderboard)
   - Correlation between two continuous variables → **scatter plot**
   - Square matrix of values → **heatmap**
   - Proportional breakdown → **stacked bar** (avoid pie charts)
4. **Determine figure sizing** — Single column vs full width based on data density
5. **Highlight "our method"** — Identify which entry is the paper's contribution and give it a distinct color

### Auto-Detection Examples

**Context → Diagram**: "Our system has a Planner, Executor, and Verifier. Planner sends plans to Executor, Executor returns results to Verifier, Verifier feeds back to Planner on failure."
→ 3 entities, cycle layout, dashed feedback arrow → **Workflow 1 (Gemini)**

**Data → Chart**: "GPT-4: MMLU 86.4, HumanEval 67.0. Ours: 88.1, 71.2. Llama-3: 79.3, 62.1."
→ 3 methods × 2 benchmarks → **Workflow 2 (grouped bar)**, highlight "Ours" in coral

---

## Workflow 1: Architecture & System Diagrams (AI Image Generation)

Use Gemini 3 Pro Image Preview to generate diagrams. **Choose a visual style first** — this is the single biggest factor in whether the figure looks professional or generic.

### Visual Styles

Pick one style per paper (all figures should be consistent):

#### Style A: "Sketch / 简笔画" (Hand-Drawn)

Warm, approachable, memorable. Ideal for overview figures and system introductions. Looks like a whiteboard sketch refined by a designer.

```
VISUAL STYLE — HAND-DRAWN SKETCH:
- Slightly irregular, hand-drawn line quality — lines wobble gently, not perfectly straight
- Rounded, soft shapes with visible pen strokes (like drawn with a thick felt-tip marker)
- Warm off-white background (#FAFAF7), NOT pure white
- Fill colors are soft watercolor-like washes: muted blue (#D6E4F0), soft peach (#F5DEB3),
  light sage (#D4E6D4), pale lavender (#E6DFF0)
- Borders are dark charcoal (#2C2C2C) with 2-3px line weight, slightly uneven
- Arrows are hand-drawn with slight curves, ending in simple open arrowheads (not filled triangles)
- Text uses a rounded sans-serif font (like Comic Neue or Architects Daughter feel)

From brycewang-stanford/Auto-Empirical-Research-Skills, MIT.

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