imagegenskill
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.
Best use case
imagegenskill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using imagegenskill 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/imagegenskill/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How imagegenskill Compares
| Feature / Agent | imagegenskill | 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?
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.
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) ## 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 - Python `3.8+` > 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 ```bash # 1) Create the brief (UTF-8) 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 # 2) (Optional) Edit configuration at the top of the generator script # - STYLE (lab-atlas | signal-loom | lattice-field) # - canvas width/height # - density parameters (node_count, noise_points, band_count, ring_density) # Example: # sed -i 's/^STYLE = .*/STYLE = "lab-atlas"/' scripts/svg_gen.py # 3) Run generation python scripts/svg_gen.py # 4) View output # Primary output directory: ls -la output/svggen/ # Quick preview file: # open output/svggen/latest.svg (macOS) # xdg-open output/svggen/latest.svg (Linux) # start output/svggen/latest.svg (Windows) ``` 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 1. Write requirements to `input/brief.txt` (UTF-8). 2. Adjust the configuration section at the top of `scripts/svg_gen.py` (e.g., `STYLE`, canvas dimensions, density parameters). 3. Run `python scripts/svg_gen.py`. 4. 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`
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