Agent Skills: frontend-agent
Handles frontend/UX/route work for Unite-Hub. Fixes UI bugs, implements React components, updates layouts, ensures responsive design, and maintains shadcn/ui consistency.
ID: aiskillstore/marketplace/frontend-agent
42745
Install this agent skill to your local
Skill Files
The files that make up frontend-agent.
Loading file tree…
More agent skills
- paper-write-sci根据 LaTeX 论文项目撰写、修订和润色 SCI 期刊论文,默认 AI 自主模式,也支持人机协作仅输出审查计划;提供作者风格化写作、数字事实核验、逻辑树多轮审查与 PDF/Word 渲染闭环。⚠️ 不适用:仅改格式/样式参数、纯参考文献管理、图片处理、非论文写作任务。
- calendarInteract with Apple Calendar via AppleScript. Use when the user asks to check calendar, view events, create events, manage schedule, find free time, or list calendars. Triggers include "my calendar", "my schedule", "calendar events", "create event", "add to calendar", "what's on my calendar", "free time", "available slots", "upcoming events", "today's events". Requires macOS with Calendar.app.
- agent-memory-systemsMemory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
- label-core-bugsReview open mastra-ai/mastra GitHub issues, identify direct @mastra/core bugs, and apply the @mastra/core label. Use when auditing issues for core ownership, labeling direct core bugs, or periodically reconciling the @mastra/core issue label.
- minimal-code-disciplineYAGNI-ladder coding discipline for writing changes — existence-first, reuse before writing, dependency ladder, shortest correct diff, with non-negotiables that must never be minimized away
- cocoindexComprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.