scientific-schematics

Trigger phrases

Phrases that activate this skill when typed to Claude Code:

• create a scientific diagram
• draw this pathway
• generate a schematic
• make a flowchart for this method
• visualize this neural network

What it does

scientific-schematics is a Claude Code skill from K-Dense AI’s scientific-agent-skills repo. It turns Claude into a scientific diagram generator that produces publication-quality figures using AI image generation with iterative quality refinement — specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and other complex scientific visualizations that are hard to produce in standard plotting libraries.

A session produces a PNG or vector-format figure: the schematic is generated, reviewed against a quality threshold for the target document type (journal figure, poster, graphical abstract), and regenerated if quality is below threshold — only then is it returned.

When to use it

Reach for it when:

• You need a graphical abstract for a manuscript (the scientific-writing skill calls this automatically)
• You want a clean flowchart or biological pathway diagram that would take hours in Inkscape or PowerPoint
• You’re illustrating a novel neural network architecture or system design for a paper

When not to reach for it:

• Data plots (scatter, bar, histogram, heatmap) — use matplotlib, seaborn, or scientific-visualization
• General-purpose photos or illustrations — use generate-image

Install

Copy the SKILL.md from K-Dense AI’s scientific-schematics folder into .claude/skills/scientific-schematics/ in your project. Requires an OpenRouter API key for the AI image generation backend.

Trigger phrases: “create a scientific diagram”, “draw this pathway”, “generate a schematic”, “make a flowchart for this method”.

What a session looks like

A typical session has three phases:

1. Description and target. Describe the diagram content and the target context (journal figure, poster, graphical abstract). Claude selects the appropriate output dimensions and style parameters.
2. Generation and review. The diagram is generated and reviewed against a quality rubric. If it falls below threshold, Claude iterates with refined prompts — you don’t have to babysit the loop.
3. Output delivery. The accepted figure is saved to the specified output path with a recommended filename. For graphical abstracts, the aspect ratio (typically 1200×600px) is enforced automatically.

Receipts

Where it works well:

• Graphical abstracts for manuscripts — the workflow/result summary format is well-suited to AI image generation, and the quality loop catches obvious failures
• Conceptual diagrams where artistic precision matters less than communicating a relationship or process clearly

Where it backfires:

• Diagrams requiring exact molecular structures, precise circuit layouts, or dimensioned technical drawings — AI image generation produces plausible-looking but not verified structures
• Iterative refinement adds API cost; very complex diagrams may cycle through multiple generations before meeting the threshold

Pattern that works: write a dense, specific natural-language description of the diagram rather than a vague prompt; more specificity produces higher first-pass quality and fewer regeneration cycles.

Source and attribution

Originally authored by K-Dense Inc.. The canonical SKILL.md lives in the scientific-schematics folder of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner’s perspective. For the formal spec and any updates, defer to the source repo.