imagegenskill
Generate renderable, scientific-style SVG graphics directly from natural-language requirements (no image models)
Install / Use
npx skills add aipoch/medical-research-skills --skill imagegenskillInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Education & ResearchSupported Platforms
Our assessment of imagegenskill
imagegenskill scores 83/100 on our quality scale, 267th of 357 Education & Research skills we index.
Its SKILL.md is 4.3 KB long, well organised into 24 sections with 1 code example: a solid amount of guidance for an agent.
With 1,916 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 13 days ago, so imagegenskill is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
imagegenskill compared with similar skills
All 4 of these similar skills score higher than imagegenskill; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| imagegenskill (this skill)by aipoch | 83 | 1.9k | 13d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.4k | 15d ago | CLAUDE.md |
| last30days-skillby mvanhorn | 100 | 63.3k | today | CLAUDE.md |
| LocalAIby mudler | 100 | 49.3k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 8d ago | SKILL.md |
Frequently asked questions
- How do I install imagegenskill?
- Run
npx skills add aipoch/medical-research-skills --skill imagegenskill. The install tabs above show the steps for each supported agent. - Which AI agents does imagegenskill work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is imagegenskill safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is imagegenskill still maintained?
- The repository was last updated 13 days ago, so imagegenskill is actively maintained.
Skill content
View source on GitHubname: 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
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 feelsignal-loom: denser spectral waveforms, stronger texturelattice-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.pyimports external libraries, add them here with exact versions.
Example Usage
# 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: includesprompt,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.svgto 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_countornoise_points. - Output too empty: increase
band_countorring_density. - Style mismatch: switch
STYLEand regenerate.
Primary Entry Point
- Generator script:
scripts/svg_gen.py
Related Skills
Agent-Reach
86.4kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
last30days-skill
63.3kAI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
LocalAI
49.3kLocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
algorithmic-art
177.9kCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems.
Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
