scientific-schematics
by K-Dense-AIscientific-schematics turns natural-language prompts into publication-quality scientific diagrams with smart iterative refinement. It uses Nano Banana 2 for generation and Gemini 3.1 Pro Preview for review, regenerating only when output falls below the threshold for your document type. Built for neural network architectures, system diagrams, flowcharts, biological pathways, and other complex scientific visuals.
This skill scores 79/100, which means it is a solid listing candidate for directory users who want a specialized scientific diagram workflow rather than a generic prompt. The repository shows enough operational detail to install with confidence: it explains what to ask for, how the iterative review loop works, and when the skill will regenerate instead of stopping at a first draft.
- Strong workflow specificity: natural-language diagram requests, iterative refinement, and quality review are clearly described in SKILL.md and references/README.md.
- Good agent leverage: it targets publication-quality scientific schematics for neural nets, flowcharts, biological pathways, and similar visuals, reducing guesswork for common scientific use cases.
- Useful install decision signals: frontmatter is valid, the body is substantial, and the docs include concrete example commands plus quality thresholds by document type.
- Execution depends on external tooling and API setup (for example OpenRouter API key and a referenced script), so users may need environment configuration beyond the skill text.
- The repository evidence shows no bundled scripts or assets in the skill folder, so some implementation details are only described in prose rather than directly inspectable here.
Overview of scientific-schematics skill
What scientific-schematics does
The scientific-schematics skill turns short natural-language prompts into publication-style scientific diagrams, then checks the result with a review loop before deciding whether to regenerate. It is built for users who want a fast path to figures that look suitable for papers, talks, posters, or technical docs without drawing everything by hand.
Who it fits best
Use the scientific-schematics skill if you need neural network architecture diagrams, flowcharts, biological pathways, system schematics, or other dense scientific visuals where clarity matters more than artistic style. It is especially useful when you already know the concept you want to show, but not the exact layout or visual wording.
What makes it different
The main value is not just image generation; it is controlled iteration. The skill uses Nano Banana 2 for generation and Gemini 3.1 Pro Preview for quality review, with regeneration only when the output falls below the threshold for the document type. That makes the scientific-schematics skill more decision-oriented than a generic prompt: it tries to stop once the figure is good enough for the intended use.
How to Use scientific-schematics skill
Install and inspect the skill
For scientific-schematics install, add the skill from the repo and then read the skill file before trying your own prompt:
npx skills add K-Dense-AI/claude-scientific-skills --skill scientific-schematics
Start with scientific-skills/scientific-schematics/SKILL.md, then check references/README.md for the fastest operational examples. This repo has a small support footprint, so those two files carry most of the practical guidance.
Turn a rough idea into a usable prompt
The scientific-schematics usage pattern works best when you specify the diagram type, the audience, and the purpose. A weak prompt says “make a diagram of my workflow.” A stronger one says: “Create a conference-ready system diagram of an RNA-seq pipeline with inputs, QC, alignment, quantification, differential expression, and output interpretation, optimized for a white slide background.”
Include the details that affect layout:
- diagram category: flowchart, pathway, architecture, sequence, system map
- subject entities and their order
- label preferences: short labels, full names, acronyms, or both
- audience: paper, poster, thesis, presentation, grant
- any “must show” relationships or exclusions
Suggested workflow for better output
A practical scientific-schematics guide is to draft the figure as content first, then let the skill handle styling and refinement. First define the core nodes and connections. Then ask for a version with a document type threshold that matches the destination, such as journal, thesis, poster, or presentation. Finally, review whether the figure is too crowded, too abstract, or too literal before asking for a rerun.
Files to read first
If you want to understand the scientific-schematics for Image Generation flow quickly, read in this order:
scientific-skills/scientific-schematics/SKILL.mdreferences/README.md
That will show you the main generation pattern, the iterative review loop, and the built-in quality expectations before you commit to a larger workflow.
scientific-schematics skill FAQ
Is this better than a normal image prompt?
Usually yes, if you care about repeatable scientific clarity. A normal prompt may generate a plausible image once, but the scientific-schematics skill adds a review-and-regenerate loop, document-type thresholds, and a stronger bias toward publication-ready diagrams.
Does scientific-schematics work for beginners?
Yes, if you can describe your subject in plain English. You do not need to know design tools or diagram syntax. The main beginner mistake is under-specifying the process, which leads to vague figures instead of a diagram with the right structure.
When should I not use it?
Do not use it when you need highly branded marketing art, photorealistic scenes, or a diagram that must match an exact compliance template pixel-for-pixel. It is optimized for scientific communication, not for arbitrary graphic design.
What should I expect from scientific-schematics install?
The install is lightweight, but the quality still depends on the prompt and your target format. Expect the best results when you provide the final use case up front, because the skill chooses its acceptance threshold based on document type and intended precision.
How to Improve scientific-schematics skill
Give the skill the right level of structure
The biggest quality gain comes from clearer source content. Instead of “draw a signaling pathway,” provide the pathway name, the main steps, key molecules, and whether the emphasis should be causal flow, mechanism, or comparison. This helps the skill place labels and arrows correctly instead of inventing structure.
Match the threshold to the real destination
A common failure mode is asking for “publication quality” when the real need is a slide figure, or asking for a poster figure while expecting journal-level density. In the scientific-schematics skill, document type matters because it changes how much detail the review loop tolerates before regeneration. Pick the least ambiguous target: journal, thesis, poster, presentation, or report.
Iterate with specific critique
If the first output is close but not usable, improve it with concrete feedback:
- “reduce label crowding in the center”
- “make the input-output direction clearer”
- “group the preprocessing and model steps separately”
- “use shorter labels and stronger contrast”
- “emphasize the membrane-bound steps”
These instructions help more than generic praise or rejection because they tell the skill what to preserve and what to change.
Watch for the usual mismatch points
The skill can struggle when the source concept is overloaded with too many elements, when acronyms are unexplained, or when the desired hierarchy is unclear. If you need a better second pass, simplify the prompt to the essential components first, then ask for one refinement at a time. That usually produces cleaner scientific schematics than trying to solve every issue in one revision.