scientific-schematics
Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.
Best use case
scientific-schematics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.
Teams using scientific-schematics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scientific-schematics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scientific-schematics Compares
| Feature / Agent | scientific-schematics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills) # Scientific Schematics Skill ## When to Use - Creating **journal-ready** figures (clean typography, consistent styling, high resolution) from a short textual description. - Producing **poster-friendly** diagrams that prioritize readability at distance (larger labels, stronger contrast). - Drafting **neural network architecture** schematics (e.g., Transformer blocks, attention modules) for papers or slides. - Generating **biological pathway** visuals (e.g., Krebs cycle) with iterative quality review. - Rapidly iterating on a diagram concept when you need **AI-assisted refinement loops** instead of manual redraws. ## Key Features - **Text-to-diagram automation**: Converts a natural-language prompt into a publication-quality schematic. - **Iterative generate → review → refine loop**: Automatically improves the figure until a quality threshold is met. - **Document-type aware critique**: Reviewer feedback adapts to `journal` vs `poster` requirements. - **Model-configurable pipeline**: Choose separate LLMs for generation and vision-based review. - **Output validation**: Performs final checks (e.g., resolution/accessibility considerations) before saving to `figures/`. - Reference guidance: - Best practices: `references/best_practices.md` - Supported diagram categories: `references/diagram_types.md` ## Dependencies - Python 3.10+ (recommended) - Python packages: - `pillow` (PIL) - `matplotlib` - `requests` - Environment: - `OPENROUTER_API_KEY` (required) ## Example Usage ### 1) Set the OpenRouter API key **Windows (PowerShell)** ```powershell $env:OPENROUTER_API_KEY="your_key_here" ``` **Linux/macOS** ```bash export OPENROUTER_API_KEY="your_key_here" ``` ### 2) Run the generator (journal/poster) ```bash python scripts/generate_schematic.py "Transformer architecture with attention mechanism" --doc-type journal ``` ### 3) Override the generation model ```bash python scripts/generate_schematic.py "Krebs cycle" --doc-type journal --generator anthropic/claude-3.5-sonnet ``` ### 4) (Optional) Override both generator and reviewer ```bash python scripts/generate_schematic.py "Flowchart of a clinical trial enrollment pipeline" \ --doc-type poster \ --generator google/gemini-2.0-flash-001 \ --reviewer google/gemini-2.0-flash-001 ``` ## Implementation Details ### Pipeline Stages 1. **Generation** - A code-capable LLM converts the prompt into a diagram image. - Default generator model: `google/gemini-2.0-flash-001`. 2. **Review** - A vision-capable LLM evaluates the generated image against the target `--doc-type`. - Default reviewer model: `google/gemini-2.0-flash-001`. - The reviewer returns actionable critique and a numeric quality score. 3. **Refinement Loop** - If the score is below the acceptance threshold (e.g., **8.5/10**), the system re-enters generation using the reviewer’s feedback as constraints. - This repeats until the threshold is met or the run terminates by internal stopping conditions. 4. **Finalization** - Performs final checks such as **resolution suitability** and **accessibility-oriented considerations** (e.g., legibility). - Saves the final artifact to the `figures/` directory. ### Key Parameters - `--doc-type <journal|poster>`: Controls review criteria (e.g., density/precision for journals vs readability/scale for posters). - `--generator <model_id>`: Model used to produce the diagram. - `--reviewer <model_id>`: Model used to critique the diagram. - **Quality threshold**: A numeric cutoff (example: `8.5/10`) that determines whether refinement continues.
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