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Agent Skill · 研究检索

building-tables(搭建桌子)

building-tables 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

building-tables 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 48,042。

总安装 706周安装 30GitHub Stars 350下载 247核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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:building-tables(搭建桌子)
来源仓库:https://github.com/ancoleman/ai-design-components
仓库路径:skills/building-tables
安装命令:
npx skills add https://github.com/ancoleman/ai-design-components --skill building-tables
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/ancoleman/ai-design-components --skill building-tables

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

该技能系统化构建表格与数据网格组件,支持从简单 HTML 到百万行虚拟化渲染。适用于各类结构化数据展示场景,提供性能、无障碍与响应式设计决策框架。根据数据量与功能需求推荐合适实现方案,确保跨设备兼容性。安装方式:通过 npx skills add 命令从 GitHub 仓库添加,需明确数据规模与交互需求。

building-tables 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/building-tables

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/ancoleman/ai-design-components --skill buildin…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

building-tables 怎么安装?

在终端执行 npx skills add https://github.com/ancoleman/ai-design-components --skill building-tables。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

building-tables 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

building-tables 需要什么权限?

权限级别是需要联网。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。

building-tables 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

building-tables 用的人多吗?

公开数据是累计安装 706 次,近一周安装 30 次,下载 247 次,GitHub 350 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

building-tables 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
building-tables

搭建桌子 · 本页

706350需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Building Tables & Data Grids

Purpose

This skill enables systematic creation of tables and data grids from simple HTML tables to enterprise-scale virtualized grids handling millions of rows. It provides clear decision frameworks based on data volume and required features, ensuring optimal performance, accessibility, and responsive design across all implementations.

When to Use

Activate this skill when:

  • Creating tables, data grids, or spreadsheet-like interfaces
  • Displaying tabular or structured data
  • Implementing sorting, filtering, or pagination features
  • Handling large datasets or addressing performance concerns
  • Building inline editing or data entry interfaces
  • Requiring row selection or bulk operations
  • Implementing data export (CSV, Excel, PDF)
  • Ensuring table accessibility or responsive behavior

Quick Decision Framework

Select implementation tier based on data volume:

<100 rows        → Simple HTML table with progressive enhancement
100-1,000 rows   → Client-side features (sort, filter, paginate)
1,000-10,000     → Server-side operations with API pagination
10,000-100,000   → Virtual scrolling with windowing
>100,000 rows    → Enterprise grid with streaming and workers

For detailed selection criteria, reference references/selection-framework.md.

Core Implementation Patterns

Tier 1: Basic Tables (<100 rows)

For simple, read-only data display:

  • Use semantic HTML <table> structure
  • Add responsive behavior via CSS
  • Implement client-side sorting if needed
  • Reference references/basic-tables.md for patterns

Example: examples/simple-responsive-table.tsx

Tier 2: Interactive Tables (100-10K rows)

For feature-rich interactions:

  • Add filtering, pagination, and selection
  • Implement inline or modal editing
  • Use client-side operations up to 1K rows
  • Switch to server-side beyond 1K rows
  • Reference references/interactive-tables.md

Example: examples/sortable-filtered-table.tsx

Tier 3: Advanced Grids (10K+ rows)

For massive datasets:

  • Implement virtual scrolling
  • Use server-side aggregation
  • Add grouping and hierarchies
  • Consider enterprise solutions
  • Reference references/advanced-grids.md

Example: examples/virtual-scrolling-grid.tsx

Performance Optimization

Critical performance thresholds:

  • Client-side operations: <1,000 rows (instant, <50ms)
  • Server-side operations: 1,000-10,000 rows (<200ms API)
  • Virtual scrolling: 10,000+ rows (60fps, constant memory)
  • Streaming: 100,000+ rows (progressive rendering)

To benchmark performance:

# Generate test data
python scripts/generate_mock_data.py --rows 10000

# Analyze rendering performance
node scripts/analyze_performance.js

For optimization strategies, reference references/performance-optimization.md.

Feature Implementation

Sorting

  • Single or multi-column sorting
  • Custom sort logic (numeric, date, natural)
  • Visual indicators and keyboard support
  • Reference references/sorting-filtering.md

Filtering & Search

  • Column-specific filters (text, range, select)
  • Global search across all columns
  • Advanced filter logic (AND/OR)
  • Reference references/sorting-filtering.md

Pagination

  • Client-side for small datasets
  • Server-side for large datasets
  • Infinite scroll alternative
  • Reference references/pagination-strategies.md

Selection & Bulk Actions

  • Single or multi-row selection
  • Range selection (Shift+click)
  • Bulk operations toolbar
  • Reference references/selection-patterns.md

Inline Editing

  • Cell-level or row-level editing
  • Validation and error handling
  • Optimistic updates
  • Reference references/editing-patterns.md

Export

  • CSV, Excel, PDF formats
  • Preserve formatting and encoding
  • Stream large exports
  • Run scripts/export_table_data.py

Accessibility Requirements

Essential WCAG compliance:

  • Semantic HTML with proper structure
  • ARIA grid pattern for interactive tables
  • Full keyboard navigation
  • Screen reader announcements

To validate accessibility:

node scripts/validate_accessibility.js

For complete requirements, reference references/accessibility-patterns.md.

Responsive Design

Four proven strategies:

  1. Horizontal scroll - Simple, preserves structure
  2. Card stack - Transform rows to cards on mobile
  3. Priority columns - Hide less important columns
  4. Truncate & expand - Compact with details on demand

See examples/responsive-patterns.tsx for implementations. Reference references/responsive-strategies.md for details.

Library Recommendations

Primary: TanStack Table (Headless)

Best for custom designs and complete control:

  • TypeScript-first with excellent DX
  • Small bundle size (~15KB)
  • Framework agnostic
  • Virtual scrolling support
npm install @tanstack/react-table

See examples/tanstack-basic.tsx for setup.

Enterprise: AG Grid

Best for feature-complete solutions:

  • Handles millions of rows
  • Built-in advanced features
  • Community (free) + Enterprise (paid)
  • Excel-like user experience
npm install ag-grid-react

See examples/ag-grid-enterprise.tsx for setup.

For detailed comparison, reference references/library-comparison.md.

Design Token Integration

Tables use the design-tokens skill for consistent theming:

  • Color tokens for backgrounds, borders, and states
  • Spacing tokens for cell padding
  • Typography tokens for text styling
  • Shadow tokens for elevation

Supports light, dark, high-contrast, and custom themes. Reference the design-tokens skill for theme switching.

Working Examples

Start with the example matching the requirements:

simple-responsive-table.tsx    # Basic HTML table
sortable-filtered-table.tsx    # With sorting and filtering
paginated-server-table.tsx      # Server-side pagination
virtual-scrolling-grid.tsx      # High-performance for 100K+ rows
editable-data-grid.tsx         # Inline editing with validation
grouped-aggregated-table.tsx   # Hierarchical with aggregations

Testing Tools

Generate test data:

python scripts/generate_mock_data.py --rows 100000 --columns 20

Benchmark performance:

node scripts/analyze_performance.js --rows 10000

Validate accessibility:

node scripts/validate_accessibility.js

Next Steps

  1. Determine the data volume and feature requirements
  2. Select the appropriate implementation tier
  3. Choose between TanStack Table (flexibility) or AG Grid (features)
  4. Start with the matching example file
  5. Implement core features progressively
  6. Test performance and accessibility
  7. Apply responsive strategy for mobile

平台分布

Claude Code

30.3%
按下载量换算75

Gemini CLI

23.7%
按下载量换算59

Cursor

16.81%
按下载量换算42

Antigravity

14.03%
按下载量换算35

OpenCode

7.57%
按下载量换算19

github-copilot

3.27%
按下载量换算8

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。

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