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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 imagegenskill

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

83/100

Supported Platforms

Universal

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.

Substance
26/30
Structure
17/20
Description
12/15
Adoption
14/20
Freshness
15/15

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.

SkillScoreStarsUpdatedFormat
imagegenskill (this skill)by aipoch831.9k13d agoSKILL.md
Agent-Reachby Panniantong10086.4k15d agoCLAUDE.md
last30days-skillby mvanhorn10063.3ktodayCLAUDE.md
LocalAIby mudler10049.3ktodayMCP Server
algorithmic-artby anthropics100177.9k8d agoSKILL.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.

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

  • 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

# 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

Related Skills

View on GitHub
GitHub Stars1.9k
CategoryEducation
Updated13d ago
Forks175

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions