onsystem: Keeps coding agents on your design system: catches palette colors, native elements and APIs your system lacks

by dgesteves · MCP Server · ★ 165

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

Featured in: AI Design Tools

How to install onsystem

/plugin marketplace add dgesteves/onsystem
/plugin install onsystem@dgesteves

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 62/100

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.

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.

accessibilityagent-guardrailsai-agentsclaude-codeclaude-code-plugincursordesign-systemdesign-system-lintdesign-tokenslinter

Quick Facts

Stars165
Forks4
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score62/100
Open Issues19
Last Updated2026-11-01
Created2026-10-06
Platformsclaude-code, mcp, node
Est. Tokens~16k

About onsystem

onsystem Keeps coding agents on your design system: it knows your real components, props, variants and tokens, catches the moment an agent invents one and has it fix it, and the same check gates your PRs. Local, zero config, works alongside @shadcn/lint. Built for design-system and platform teams with a React design-system package; a shadcn/ui app is the zero-config demo. Formerly (what changed).

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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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