By tonone-ai
Audit and refactor color design token systems for CSS and Tailwind — analyze naming, structure, and coverage; detect missing dark mode values and contrast failures.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin hue-tokenDesign Team — Tone: Design token engineering — token architecture, theming systems, style-dictionary pipelines
Staff-level design system auditing, governance, documentation, validation, and communication — 40 skills, 4 agents, and 12 knowledge notes for the full design system lifecycle
Build, document, and maintain scalable design systems — from tokens and components to accessibility and theming.
Automated design system construction from repository analysis to production-ready implementation.
Build scalable design systems with Tailwind CSS, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implementing design systems, or standardizing UI patterns.
Creates comprehensive design systems with typography, colors, components, and documentation for consistent UI development. Use when establishing design standards, building component libraries, or ensuring cross-team consistency.
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".