By dbc-oduffy
Frontend code review specialists for Claude Code — Pali catches design token violations and component anti-patterns, Fru reviews UX flows for clarity and trust. Sequential review ensures nothing ships without both perspectives.
npx claudepluginhub dbc-oduffy/coordinator-claude --plugin web-devUse this agent when you need front-end code review focusing on design system adherence, token validation, component patterns, and CSS architecture. The Front-End Reviewer (`web-dev:senior-front-end`) ensures UI code uses existing tokens, components, and patterns rather than bespoke values. This reviewer is pragmatic — 'close enough' to design specs is often correct when it means using standard utilities.
Use this agent when you need UX flow review, trust/clarity assessment, or user experience evaluation for interface changes. The UX Reviewer (`web-dev:staff-ux`) specializes in reviewing user-facing features for clarity, trust signals, and intuitive flow design. Invoke with 'UX: <flow>' for detailed UX flow review or 'UX short' for quick UX spot checks.
Agents for UI/UX design, accessibility, and user experience optimization
5つの専門エージェント(backend-reviewer, frontend-reviewer, test-reviewer, security-reviewer, ux-reviewer)がチームでコードレビューするスキル。
Structured development skills for any project. Provides brainstorming, planning, parallel agent development, browser testing, systematic debugging, and self-improvement workflows.
Use this agent when creating user interfaces, designing components, building design systems, or improving visual aesthetics. This agent specializes in creating beautiful, functional interfaces that can be implemented quickly within 6-day sprints. Examples:\n\n<example>\nContext: Starting a new app or feature design
Comprehensive UI/UX design plugin for mobile (iOS, Android, React Native) and web applications with design systems, accessibility, and modern patterns
Uses power tools
Uses Bash, Write, or Edit tools
Share bugs, ideas, or general feedback.
Tools for crafting, reviewing, analyzing, refining, and optimizing LLM prompts for clarity, precision, goal effectiveness, and token efficiency
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