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scientific-schematics

Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.

Published by aipoch ↗aipoch/medical-research-skills

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scientific-schematics

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Use scientific-schematics to create a publication-ready [journal or poster] schematic of [scientific concept], using a configured OPENROUTER_API_KEY and prioritizing legibility and accessibility.

Required inputs

  • [journal or poster]
  • [scientific concept]

User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗

SKILL.md

name: scientific-schematicsdescription: Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.license: MITauthor: AIPOCH

Source: 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)

$env:OPENROUTER_API_KEY="your_key_here"

Linux/macOS

export OPENROUTER_API_KEY="your_key_here"

2) Run the generator (journal/poster)

python scripts/generate_schematic.py "Transformer architecture with attention mechanism" --doc-type journal

3) Override the generation model

python scripts/generate_schematic.py "Krebs cycle" --doc-type journal --generator anthropic/claude-3.5-sonnet

4) (Optional) Override both generator and reviewer

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.