/academic-plotting

name: academic-plotting
description: Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Academic Writing, Visualization, Matplotlib, Seaborn, Plotting, Figures, Diagrams, NeurIPS, ICML, ICLR, LaTeX]
dependencies: [matplotlib>=3.8.0, seaborn>=0.13.0, numpy, google-genai>=1.0.0]

<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档，收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝

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

声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权，请联系删除。 -->

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.

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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

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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