Generate high-quality SVG graphics using Python scripts. All scripts output valid SVG to stdout (or file with -o).
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsvg-artExecute the skills CLI command in your project's root directory to begin installation:
Fetches svg-art from kv0906/cc-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate svg-art. Access via /svg-art in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
2
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2
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Run in your terminal
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this week
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Generate high-quality SVG graphics using Python scripts. All scripts output valid SVG to stdout (or file with -o).
| Script | Purpose | Key Options |
|---|---|---|
generate_grid.py |
Grid patterns | --cols, --rows, --shape, --vary-* |
generate_radial.py |
Radial/spiral/sunburst | --spiral, --concentric, --sunburst |
generate_fractal.py |
Fractals (tree, koch, sierpinski) | --tree, --koch, --sierpinski, --depth |
generate_wave.py |
Waves and audio viz | --layers, --noise, --bars |
generate_particles.py |
Scatter/cluster/constellation | --cluster, --gradient, --constellation |
generate_chart.py |
Data visualization | --bar, --line, --pie, --donut |
generate_icon.py |
Common UI icons | --icon NAME, --list, --filled |
optimize_svg.py |
Minify/optimize SVG | --aggressive, --stats |
# Grid with size variation
python scripts/generate_grid.py -c 6 -r 6 --vary-size --vary-opacity -o grid.svg
# Spiral pattern
python scripts/generate_radial.py --spiral -n 60 --turns 4 -o spiral.svg
# Fractal tree
python scripts/generate_fractal.py --tree --depth 8 --vary-angle -o tree.svg
# Layered waves with fill
python scripts/generate_wave.py --layers 5 --fill -o waves.svg
# Constellation network
python scripts/generate_particles.py --constellation -n 30 --connect-distance 25 -o network.svg
# Bar chart
python scripts/generate_chart.py --bar --data "30,50,80,45,90" --labels "A,B,C,D,E" -o chart.svg
# Heart icon
python scripts/generate_icon.py --icon heart --filled --stroke "#E11D48" -o heart.svg
# Optimize existing SVG
python scripts/optimize_svg.py input.svg --aggressive -o output.svg
python scripts/generate_grid.py \
-c 8 -r 8 # columns and rows
-s 10 -g 2 # size and gap
--shape circle # rect, circle, or diamond
--vary-size # random size variation
--vary-opacity # random opacity
--vary-hue # color variation
--seed 42 # reproducible randomness
# Concentric rings
python scripts/generate_radial.py --concentric --rings 5 --vary-hue
# Sunburst rays
python scripts/generate_radial.py --sunburst -n 24 --vary-length
# Koch snowflake
python scripts/generate_fractal.py --koch --depth 4 --fill "#3B82F6"
# Sierpinski triangle
python scripts/generate_fractal.py --sierpinski --depth 5
# Line chart with points
python scripts/generate_chart.py --line --data "10,30,20,50" --show-points --smooth
# Donut chart
python scripts/generate_chart.py --donut --data "40,30,20,10" --labels "A,B,C,D"
# List all available icons
python scripts/generate_icon.py --list
# Common icons: check, x, plus, menu, search, home, user, settings,
# mail, heart, star, play, file, download, edit, share, sun, moon, etc.
--fill COLOR: Fill color (default: #3B82F6)--stroke COLOR: Stroke color--stroke-width N: Stroke width--seed N: Random seed for reproducibility-o FILE: Output to file instead of stdoutScripts can be piped together:
# Generate and optimize
python scripts/generate_grid.py -c 10 -r 10 | python scripts/optimize_svg.py --aggressive
# Check optimization stats
python scripts/generate_fractal.py --tree --depth 10 | python scripts/optimize_svg.py --stats
See references/svg-fundamentals.md for:
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: svg-art is focused, and the summary matches what you get after install.
svg-art reduced setup friction for our internal harness; good balance of opinion and flexibility.
svg-art has been reliable in day-to-day use. Documentation quality is above average for community skills.
svg-art is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in svg-art — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
svg-art fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for svg-art matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend svg-art for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added svg-art from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
svg-art reduced setup friction for our internal harness; good balance of opinion and flexibility.
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