orchestra-research/ai-research-skillsMIT4 files

Academic Plotting

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.

Specification
Skill ID
orchestra-research/ai-research-skills/academic-plotting
Publisher
orchestra-research
Repository
ai-research-skills
Installs
0
Files
4
License
MIT
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

orchestra-research/ai-research-skills/academic-plottingInstalls these files
  • SKILL.md
  • references/data-visualization.md
  • references/diagram-generation.md
  • references/style-guide.md

What this skill tells the agent

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 TypeToolWhy
Architecture / system diagramGemini (Workflow 1)Complex spatial layouts with boxes, arrows, labels
Workflow / pipeline / lifecycleGemini (Workflow 1)Multi-step processes with connections
Bar chart, line plot, scattermatplotlib (Workflow 2)Precise numerical data, reproducible
Heatmap, confusion matrixmatplotlib/seaborn (Workflow 2)Structured grid data
Ablation table as chartmatplotlib (Workflow 2)Grouped bars or line comparisons
Pie / donut chartmatplotlib (Workflow 2)Proportional data (use sparingly in ML papers)
Training curvesmatplotlib (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 TypeExampleWhat 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 dataExperiment log filesVariables, 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
  1. 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)
  1. 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
  1. Assign colors — One accent color per logical group/layer
  2. 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
  1. 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)
  1. Determine figure sizing — Single column vs full width based on data density
  2. 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

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