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reactive-dashboard-performanceReact 式仪表板性能

Agent Skill

用于辅助前端页面、组件、样式和交互逻辑的开发与维护。它适合让 Agent 生成或审查 React、Next.js、Vue、Tailwind、CSS 等相关代码,整理组件结构,或定位布局和性能问题。使用时需要结合项目现有设计系统、路由和构建方式,避免只生成孤立片段;涉及页面改动时,应配合本地预览和构建检查确认视觉效果。

总安装

2,895

周安装

116

GitHub Stars

98

下载量

937
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reactive-dashboard-performance(React 式仪表板性能)
来源仓库:https://github.com/erichowens/some_claude_skills
仓库路径:skills/reactive-dashboard-performance
安装命令:
npx skills add https://github.com/erichowens/some_claude_skills --skill reactive-dashboard-performance
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/erichowens/some_claude_skills --skill reactive-dashboard-performance

简介

用于辅助 React 式仪表板性能优化分析,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位渲染瓶颈和内存泄漏。

  • 支持提供组件拆分建议、数据缓存策略和懒加载方案,提升页面响应速度。
  • 通过 npx skills add 命令从指定仓库安装,需确认本地环境是否支持 GitHub 技能加载。
  • 使用前建议核对项目路由、构建配置及测试框架版本,避免只生成孤立片段。
  • 涉及页面改动时,应配合本地预览和构建检查,确认视觉效果与交互行为一致。

SKILL.md

Reactive Dashboard Performance

Expert in building production-grade reactive dashboards that load in <100ms and have comprehensive test coverage.

Core Expertise

Performance Patterns (Linear, Vercel, Notion-grade)

  1. Skeleton-First Loading

- Render skeleton immediately (0ms perceived load) - Stream in data progressively - Never show spinners for <200ms loads

  1. Aggressive Caching

- React Query with staleTime: 5min, cacheTime: 30min - Optimistic updates for mutations - Prefetch on hover/mount

  1. Code Splitting

- Route-based splitting (Next.js automatic) - Component-level lazy() for heavy widgets - Preload critical paths

  1. Memoization Strategy

- useMemo for expensive computations - React.memo for pure components - useCallback for stable references

Testing Reactive Dashboards

  1. Mock Strategy

- Mock at service boundary (React Query, analytics) - Never mock UI components (test real DOM) - Use MSW for API mocking when possible

  1. Async Handling // WRONG - races with React render(<Dashboard />); const element = screen.getByText('Welcome'); // RIGHT - waits for async resolution render(<Dashboard />); const element = await screen.findByText('Welcome');
  2. Timeout Debugging

- Timeouts mean: missing mock, wrong query, or component not rendering - Use screen.debug() to see actual DOM - Check console for unmocked errors

  1. Test Wrapper Pattern const TestProviders = ({children}) => (<QueryClientProvider client={testQueryClient}> <AuthProvider> {children} </AuthProvider> </QueryClientProvider>);

Real-World Examples

  • Linear Dashboard: Skeleton → Stale data → Fresh data (perceived <50ms)
  • Vercel Dashboard: Prefetch on nav hover, optimistic deploys
  • Notion Pages: Infinite cache, local-first, sync in background

Diagnostic Protocol

Integration Test Timeouts

  1. Check what's actually rendering render(<Component />); screen.debug(); // See actual DOM
  2. Find unmocked dependencies

- Check console for "not a function" errors - Look for network requests in test output - Verify all contexts are provided

  1. Fix async queries

- Use findBy* instead of getBy* - Increase timeout if needed: waitFor(() => {...}, {timeout: 3000}) - Mock React Query properly

  1. Simplify component tree

- Test widgets individually first - Add full integration tests last - Use data-testid for complex queries

Performance Optimization

Dashboard Load Budget

PhaseTarget
Skeleton render0-16ms (1 frame)
First data paint<100ms
Full interactive<200ms
Lazy widgets<500ms

React Query Config

const queryClient = new QueryClient({
  defaultOptions: {
    queries: {
      staleTime: 5 * 60 * 1000, // 5min
      cacheTime: 30 * 60 * 1000, // 30min
      refetchOnWindowFocus: false,
      refetchOnMount: false,
      retry: 1,
    },
  },
});

Skeleton Pattern

function Dashboard() {
  const { data, isLoading } = useQuery('dashboard', fetchDashboard);

  // Show skeleton immediately, no loading check
  return (
    <div>
      {data ? <RealWidget data={data} /> : <SkeletonWidget />}
    </div>
  );
}

Common Pitfalls

  1. Spinners for fast loads - Use skeletons instead
  2. Unmemoized expensive computations - Wrap in useMemo
  3. Testing implementation details - Test user behavior
  4. Mocking too much - Mock at boundaries only
  5. Synchronous test expectations - Everything is async

When debugging test timeouts, ALWAYS start with screen.debug() to see what actually rendered.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

windsurf

26.52%
按下载量换算248

Claude Code

26.41%
按下载量换算247

Codex

18.19%
按下载量换算170

Antigravity

13.08%
按下载量换算123

OpenCode

7.41%
按下载量换算69

Cursor

3.53%
按下载量换算33

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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