| name | imagegenskill |
| description | Generate renderable, scientific-style SVG graphics directly from natural-language requirements (no image models). Use when users ask for an image/picture/scientific diagram/visualization poster or explicitly request SVG output for web-embeddable vector graphics. |
| license | MIT |
| author | AIPOCH |
Source: https://github.com/aipoch/medical-research-skills
When to Use
- You need scientific-looking diagrams/posters (laboratory poster aesthetic) generated from a short natural-language brief.
- The user requests SVG output specifically (e.g., “output SVG”, “vector graphic”, “embeddable in a web page”).
- You want language-to-image results without using diffusion/LLM image models, prioritizing interpretable structure over photorealism.
- You need repeatable, parameter-controlled visuals (seed/palette/structure) for research notes, slides, or documentation.
- You want a structured visualization (grids, networks, waveforms, symbol rings) rather than an illustrative drawing.
Key Features
- Converts a natural-language brief into a renderable SVG with a scientific, restrained visual style.
- Multiple built-in styles via
STYLE:
lab-atlas (default): calm, stable, laboratory map feel
signal-loom: denser spectral waveforms, stronger texture
lattice-field: prominent lattice grids, denser nodes
- Produces SVG + JSON metadata (e.g.,
prompt, seed, palette) for traceability.
- Writes a convenience preview file:
output/svggen/latest.svg.
- Tunable density and composition controls (e.g., nodes, noise, bands, rings).
Dependencies
Note: No third-party Python packages are specified in the provided documentation. If scripts/svg_gen.py imports external libraries, add them here with exact versions.
Example Usage
mkdir -p input
cat > input/brief.txt << 'EOF'
Scientific poster-style SVG: "Graph topology in latent space".
Include a calm lab-atlas aesthetic, visible grid + network + waveform layers,
and a few symbol rings. Use restrained colors, high text readability.
Keywords: latent space, manifold, spectral bands, topology.
EOF
python scripts/svg_gen.py
ls -la output/svggen/
Expected outputs:
output/svggen/latest.svg (latest render for quick preview)
output/svggen/<name>.svg (generated SVG)
output/svggen/<name>.json (metadata: includes prompt, seed, palette)
Implementation Details
Workflow
- Write requirements to
input/brief.txt (UTF-8).
- Adjust the configuration section at the top of
scripts/svg_gen.py (e.g., STYLE, canvas dimensions, density parameters).
- Run
python scripts/svg_gen.py.
- Open
output/svggen/latest.svg to inspect the result.
Prompt / Brief Guidelines
- Use clear research semantics: field, object, structure, atmosphere, keywords.
- English technical terms are allowed (e.g.,
latent space, graph topology) and should remain unchanged.
- Keep the brief concise; the script maps text into structural elements and symbols.
Composition & Quality Criteria
- Text readability: ensure key labels (e.g., prompt/mode text if present) are not obscured.
- Structural hierarchy: at least three layers should be simultaneously visible, chosen from:
- grid
- waveform / spectral bands
- network / nodes
- symbol rings
- Style consistency: avoid overly saturated colors; maintain scientific visual restraint.
Tuning / Troubleshooting Parameters
- Output too dense: decrease
node_count or noise_points.
- Output too empty: increase
band_count or ring_density.
- Style mismatch: switch
STYLE and regenerate.
Primary Entry Point
- Generator script:
scripts/svg_gen.py