styleseed: Open-source design-method engine for Claude Code, Codex & Cursor

by bitjaru · Claude Skill · ★ 1.0k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Featured in: AI Design Tools

Security audit verdict: SAFE · quality 71/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

🧪 Tested by Agent Skills Hub

One of 10 tools we ran end to end on the same task for our AI Design Tools page (2026-10-10).

Result: shares 2.0 of 4 with the page built with no skill (fonts, accent, background) · its two builds share 1 of 4 · 7.0 of 7 page checks passed

See both builds → · See the whole test →

agent-skillsai-codingai-designai-uianti-slopclaude-codeclaude-skillscodexcursordesign-judgment

Quick Facts

Stars1,002
Forks96
LanguageJavaScript
CategoryClaude Skill
LicenseMIT
Quality Score71/100
Open Issues26
Last Updated2026-11-06
Created2026-04-07
Platformsclaude-code, codex, node
Est. Tokens~16k

About styleseed

styleseed Make AI code like a UI/UX designer. A design engine that teaches Claude Code and Cursor how to think like a designer. Pick any brand skin. Get professional UI. No designer needed. Get Started · Engine + Skins · Skills · Wiki · 한국어 What is StyleSeed? AI coding tools generate functional UI — but it looks generic. The missing piece isn't components or color tokens. It's design judgment.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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