By tonone-ai
Generate or fix theming systems including dark mode, multi-brand, and white-label token swap, producing CSS variables, Tailwind config, and style-dictionary architecture
npx claudepluginhub tonone-ai/tonone --plugin tone-themeBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Design Team — Tone: Design token engineering — token architecture, theming systems, style-dictionary pipelines
263+ design styles with multi-tenant Keycloak theming for AI-powered frontend development
Tailwind v4 theming and shadcn/ui component installation, customisation, and recipes.
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.
Build, document, and maintain scalable design systems — from tokens and components to accessibility and theming.
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".