academic-plotting
Produces professional figures for ML papers using Gemini and Matplotlib.
Install
mkdir -p .claude/skills/academic-plotting && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13546" && unzip -o skill.zip -d .claude/skills/academic-plotting && rm skill.zipInstalls to .claude/skills/academic-plotting
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Generate architecture diagrams via Gemini from research context
- →Auto-select chart types for data-driven figures
- →Generate data-driven figures via Matplotlib/Seaborn
- →Extract system components and relationships for diagrams
- →Identify visual entities and relationships for diagrams
- →Determine layout patterns for diagrams
How it works
The skill generates publication-quality figures for ML papers by using Gemini for architecture diagrams from research context and Matplotlib/Seaborn for data-driven figures from experiment results or data.
Inputs & outputs
When to use academic-plotting
- →Creating ML system architecture diagrams
- →Plotting training loss curves
- →Generating experiment confusion matrices
- →Visualizing ablation results
About this skill
Academic Plotting for ML Papers
Generate publication-quality figures for ML/AI conference papers. Two distinct workflows:
- Diagram figures (architecture, system design, workflows, pipelines) — AI image generation via Gemini
- 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):
- Read the provided context — paper section, abstract, or description paragraph
- 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
- 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)
- 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
- Assign colors — One accent color per logical group/layer
- Write every label exactly — Extract exact terminology from the paper text
For data charts (results → figure):
- Read the provided data — table, paragraph with numbers, CSV, or JSON
- 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
- 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)
- Determine figure sizing — Single column vs full width based on data density
- 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)
- Small doodle-style icons inside boxes: a tiny gear ⚙ for processing, a lightbulb 💡 for ideas,
a magnifying glass 🔍 for search — rendered as simple line drawings, NOT emoji
- Overall feel: a carefully drawn whiteboard diagram, clean but with personality
- NO clip art, NO stock icons, NO photorealistic elements
Style B: "Modern Minimal" (Clean & Bold)
Confident, authoritative. Best for method figures where precision matters.
VISUAL STYLE — MODERN MINIMAL:
- Ultra-clean geometric shapes with crisp edges
- Bold color blocks as backgrounds for sections — NOT just accent bars, but full section fills
using desaturated tones: slate blue (#E8EDF2), warm sand (#F5F0E8), cool mint (#E8F2EE)
- Component boxes have ROUNDED CORNERS (12px radius), NO visible border — they float on
the section background using subtle shadow (1px, 4px blur, rgba(0,0,0,0.06))
- ONE accent color per section used sparingly on key elements: Deep blue (#2563EB),
Emerald (#059669), Amber (#D97706), Rose (#E11D48)
- Arrows are thin (1.5px), dark gray (#6B7280), with small filled circle at source
and clean arrowhead at target — NOT thick colored arrows
- Typography: Inter or system sans-serif, title 600 weight, body 400 weight
- Labels INSIDE boxes, not beside them
- Generous whitespace — at least 24px between elements
- NO decorative elements, NO icons — let the structure speak
Style C: "Illustrated Technical" (Icon-Rich)
Engaging, explanatory. Good for tutorial-style papers and figures that need to be self-explanatory.
VISUAL STYLE — ILLUSTRATED TECHNICAL:
- Each major component has a small MEANINGFUL ICON drawn in a consistent line-art style
(single color, 2px stroke, ~24x24px): brain icon for reasoning, database cylinder for storage,
arrow-loop for iteration, network nodes for communication
- Components sit inside soft rounded rectangles with a LEFT COLOR STRIP (4px wide)
- Background is pure white, but each logical group has a very faint colored region behind it
(#F8FAFC for blue group, #FFF8F0 for orange group)
- Connections use CURVED bezier paths (not straight lines), colored by SOURCE component
- Key data flows are THICKER (3px) than secondary flows (1px, dashed)
- Small annotation badges on arrows: "×N" for repeated operations, "optional" in italics
- Title labels are ABOVE each section in small caps, letter-spaced
- Overall: like a well-designed API documentation diagram
Style D: "Accent Bar" (Classic Academic)
The default academic style. Safe for any venue, works well in grayscale.
VISUAL STYLE — CLASSIC ACCENT BAR:
- Horizontal section bands stacked vertically, pale gray (#F7F7F5) fill
- Thick colored LEFT ACCENT BAR (8px) distinguishes each section
- Content boxes: white fill, thin #DDD border, 4px rounded corners
- Section palette: Blue #4A90D9, Teal #5BA58B, Amber #D4A252, Slate #7B8794
- Sans-serif typography (Helvetica/Arial), bold titles, regular body
- Colored arrows match their SOURCE section
- Clean, flat, zero decoration
Curated Color Palettes
"Ocean Dusk" (professional, calming — default recommendation):
#264653 deep teal, #2A9D8F teal, #E9C46A gold, #F4A261 sandy orange, #E76F51 burnt coral
"Ink & Wash" (for 简笔画 style):
#2C2C2C charcoal ink, #D6E4F0 washed blue, #F5DEB3 washed wheat, #D4E6D4 washed sage, #E6DFF0 washed lavender
"Nord" (for modern minimal):
#2E3440 polar night, #5E81AC frost blue, #A3BE8C aurora green, #EBCB8B aurora yellow, #BF616A aurora red
"Okabe-Ito" (universal colorblind-safe, required for data charts):
#E69F00 orange, #56B4E9 sky blue, #009E73 green, #F0E442 yellow, #0072B2 blue, #D55E00 vermillion, #CC79A7 pink
Checklist
- Extract from context: Read paper/description, identify entities and relationships
- Choose visual style (A/B/C/D) — match the paper's tone and venue
- Choose color palette — or use one consistent with existing paper figures
- Obtain Gemini API key (
GEMINI_API_KEYenv var) - Write a detailed prompt: style block + layout + connections + constraints
- Generate script at
figures/gen_fig_<name>.py, run for 3 attempts - Review, select best, save as
figures/fig_<name>.png
Prompt Structure (6 Sections)
Every Gemini prompt must include these sections in order:
1. FRAMING (5 lines): "Create a
---
*Content truncated.*
When not to use it
- →When the figure has numerical axes but Gemini is used
- →When the figure has boxes and arrows but Matplotlib is used
- →When generating interactive plots (use Plotly)
Prerequisites
Limitations
- →Gemini may misspell labels if not explicitly constrained
- →Gemini may ignore style if not specific enough
- →Requires specific font and sizing considerations for LaTeX integration
How it compares
This skill automates the creation of publication-quality figures tailored for ML papers, distinguishing between diagram and data figures and using specialized tools for each, which is more efficient and consistent than manual creation.
Compared to similar skills
academic-plotting side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| academic-plotting (this skill) | 0 | 3mo | Review | Advanced |
| umap-learn | 6 | 2mo | Review | Intermediate |
| hugging-face-trackio | 1 | 6mo | Review | Intermediate |
| omicverse-visualization-for-bulk-color-systems-and-single-cell-d | 1 | 4mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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