build-dashboard
anthropics/knowledge-work-plugins · Agent Skill
建立包含圖表、篩選器和表格的互動式 HTML 儀錶板,可直接在瀏覽器中開啟。
前往安裝 ↓總覽
| 發布者 | |
|---|---|
| 儲存庫 | anthropics/knowledge-work-plugins ↗ |
| 技能資料夾 | build-dashboard |
| 星 | 23,908 |
| 附帶檔案 | 1 |
| 授權 | Apache-2.0 |
| 最近更新 | 2026年3月12日 |
| 連結 | 技能原始碼 ↗ · 儲存庫 ↗ |
Mozgi 檢查
我們在技能檔案中發現的內容。詳細資訊需訂閱。
安裝
技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。
把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。
Install the “build-dashboard” skill from mozgi.io (https://mozgi.io/skills/build-dashboard/) for me. 1. Download the archive: https://mozgi.io/skills/build-dashboard/download If it fails, take the skill folder from the source: https://github.com/anthropics/knowledge-work-plugins/tree/main/data/skills/build-dashboard 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “build-dashboard” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/build-dashboard/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins mkdir -p .claude/skills cp -r /tmp/knowledge-work-plugins/data/skills/build-dashboard .claude/skills/build-dashboard
重新啟動 Claude Code — 技能就會出現在清單中,手動呼叫:/build-dashboard。要套用到所有專案,請放在 ~/.claude/skills。
git clone --depth 1 --branch main https://github.com/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins mkdir -p .agents/skills cp -r /tmp/knowledge-work-plugins/data/skills/build-dashboard .agents/skills/build-dashboard
Codex 會從目前資料夾往上到儲存庫根目錄,在 .agents/skills 中尋找技能;用 /skills 或 $build-dashboard 呼叫。所有專案:~/.agents/skills。
git clone --depth 1 --branch main https://github.com/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins mkdir -p .cursor/skills cp -r /tmp/knowledge-work-plugins/data/skills/build-dashboard .cursor/skills/build-dashboard
Cursor 啟動時會載入技能;在 Agent 聊天輸入「/」呼叫。Cursor 也會讀取 .claude/skills 與 .agents/skills。所有專案:~/.cursor/skills。
git clone --depth 1 --branch main https://github.com/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins mkdir -p .gemini/skills cp -r /tmp/knowledge-work-plugins/data/skills/build-dashboard .gemini/skills/build-dashboard
任務符合時,Gemini CLI 會提議啟用技能,由你確認。所有專案:~/.gemini/skills。
安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。
SKILL.md 內容
作者原文 · 唯讀/build-dashboard - Build Interactive Dashboards
> If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Build a self-contained interactive HTML dashboard with charts, filters, tables, and professional styling. Opens directly in a browser -- no server or dependencies required.
Usage
/build-dashboard <description of dashboard> [data source]
Workflow
1. Understand the Dashboard Requirements
Determine:
- Purpose: Executive overview, operational monitoring, deep-dive analysis, team reporting
- Audience: Who will use this dashboard?
- Key metrics: What numbers matter most?
- Dimensions: What should users be able to filter or slice by?
- Data source: Live query, pasted data, CSV file, or sample data
2. Gather the Data
If data warehouse is connected:
1. Query the necessary data
2. Embed the results as JSON within the HTML file
If data is pasted or uploaded:
1. Parse and clean the data
2. Embed as JSON in the dashboard
If working from a description without data:
1. Create a realistic sample dataset matching the described schema
2. Note in the dashboard that it uses sample data
3. Provide instructions for swapping in real data
3. Design the Dashboard Layout
Follow a standard dashboard layout pattern:
┌──────────────────────────────────────────────────┐
│ Dashboard Title [Filters ▼] │
├────────────┬────────────┬────────────┬───────────┤
│ KPI Card │ KPI Card │ KPI Card │ KPI Card │
├────────────┴────────────┼────────────┴───────────┤
│ │ │
│ Primary Chart │ Secondary Chart │
│ (largest area) │ │
│ │ │
├─────────────────────────┴────────────────────────┤
│ │
│ Detail Table (sortable, scrollable) │
│ │
└──────────────────────────────────────────────────┘
Adapt the layout to the content:
- 2-4 KPI cards at the top for headline numbers
- 1-3 charts in the middle section for trends and breakdowns
- Optional detail table at the bottom for drill-down data
- Filters in the header or sidebar depending on complexity
4. Build the HTML Dashboard
Generate a single self-contained HTML file using the base template below. The file includes:
Structure (HTML):
- Semantic HTML5 layout
- Responsive grid using CSS Grid or Flexbox
- Filter controls (dropdowns, date pickers, toggles)
- KPI cards with values and labels
- Chart containers
- Data table with sortable headers
Styling (CSS):
- Professional color scheme (clean whites, grays, with accent colors for data)
- Card-based layout with subtle shadows
- Consistent typography (system fonts for fast loading)
- Responsive design that works on different screen sizes
- Print-friendly styles
Interactivity (JavaScript):
- Chart.js for interactive charts (included via CDN)
- Filter dropdowns that update all charts and tables simultaneously
- Sortable table columns
- Hover tooltips on charts
- Number formatting (commas, currency, percentages)
Data (embedded JSON):
- All data embedded directly in the HTML as JavaScript variables
- No external data fetches required
- Dashboard works completely offline
5. Implement Chart Types
Use Chart.js for all charts. Common dashboard chart patterns:
- Line chart: Time series trends
- Bar chart: Category comparisons
- Doughnut chart: Composition (when <6 categories)
- Stacked bar: Composition over time
- Mixed (bar + line): Volume with rate overlay
Use the Chart.js integration patterns below for each chart type.
6. Add Interactivity
Use the filter and interactivity implementation patterns below for dropdown filters, date range filters, combined filter logic, sortable tables, and chart updates.
7. Save and Open
1. Save the dashboard as an HTML file with a descriptive name (e.g., sales_dashboard.html)
2. Open it in the user's default browser
3. Confirm it renders correctly
4. Provide instructions for updating data or customizing
---
Base Template
Every dashboard follows this structure:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Dashboard Title</title>
<script src="https://cdn.jsdelivr.net/npm/[email protected]" integrity="sha384-jb8JQMbMoBUzgWatfe6COACi2ljcDdZQ2OxczGA3bGNeWe+6DChMTBJemed7ZnvJ" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]" integrity="sha384-cVMg8E3QFwTvGCDuK+ET4PD341jF3W8nO1auiXfuZNQkzbUUiBGLsIQUE+b1mxws" crossorigin="anonymous"></script>
<style>
/* Dashboard styles go here */
</style>
</head>
<body>
<div class="dashboard-container">
<header class="dashboard-header">
<h1>Dashboard Title</h1>
<div class="filters">
<!-- Filter controls -->
</div>
</header>
<section class="kpi-row">
<!-- KPI cards -->
</section>
<section class="chart-row">
<!-- Chart containers -->
</section>
<section class="table-section">
<!-- Data table -->
</section>
<footer class="dashboard-footer">
<span>Data as of: <span id="data-date"></span></span>
</footer>
</div>
<script>
// Embedded data
const DATA = [];
// Dashboard logic
class Dashboard {
constructor(data) {
this.rawData = data;
this.filteredData = data;
this.charts = {};
this.init();
}
init() {
this.setupFilters();
this.renderKPIs();
this.renderCharts();
this.renderTable();
}
applyFilters() {
// Filter logic
this.filteredData = this.rawData.filter(row => {
// Apply each active filter
return true; // placeholder
});
this.renderKPIs();
this.updateCharts();
this.renderTable();
}
// ... methods for each section
}
const dashboard = new Dashboard(DATA);
</script>
</body>
</html>
KPI Card Pattern
<div class="kpi-card">
<div class="kpi-label">Total Revenue</div>
<div class="kpi-value" id="kpi-revenue">$0</div>
<div class="kpi-change positive" id="kpi-revenue-change">+0%</div>
</div>
function renderKPI(elementId, value, previousValue, format = 'number') {
const el = document.getElementById(elementId);
const changeEl = document.getElementById(elementId + '-change');
// Format the value
el.textContent = formatValue(value, format);
// Calculate and display change
if (previousValue && previousValue !== 0) {
const pctChange = ((value - previousValue) / previousValue) * 100;
const sign = pctChange >= 0 ? '+' : '';
changeEl.textContent = `${sign}${pctChange.toFixed(1)}% vs prior period`;
changeEl.className = `kpi-change ${pctChange >= 0 ? 'positive' : 'negative'}`;
}
}
function formatValue(value, format) {
switch (format) {
case 'currency':
if (value >= 1e6) return `$${(value / 1e6).toFixed(1)}M`;
if (value >= 1e3) return `$${(value / 1e3).toFixed(1)}K`;
return `$${value.toFixed(0)}`;
case 'percent':
return `${value.toFixed(1)}%`;
case 'number':
if (value >= 1e6) return `${(value / 1e6).toFixed(1)}M`;
if (value >= 1e3) return `${(value / 1e3).toFixed(1)}K`;
return value.toLocaleString();
default:
return value.toString();
}
}
Chart.js Integration
Chart Container Pattern
<div class="chart-container">
<h3 class="chart-title">Monthly Revenue Trend</h3>
<canvas id="revenue-chart"></canvas>
</div>
Line Chart
function createLineChart(canvasId, labels, datasets) {
const ctx = document.getElementById(canvasId).getContext('2d');
return new Chart(ctx, {
type: 'line',
data: {
labels: labels,
datasets: datasets.map((ds, i) => ({
label: ds.label,
data: ds.data,
borderColor: COLORS[i % COLORS.length],
backgroundColor: COLORS[i % COLORS.length] + '20',
borderWidth: 2,
fill: ds.fill || false,
tension: 0.3,
pointRadius: 3,
pointHoverRadius: 6,
}))
},
options: {
responsive: true,
maintainAspectRatio: false,
interaction: {
mode: 'index',
intersect: false,
},
plugins: {
legend: {
position: 'top',
labels: { usePointStyle: true, padding: 20 }
},
tooltip: {
callbacks: {
label: function(context) {
return `${context.dataset.label}: ${formatValue(context.parsed.y, 'currency')}`;
}
}
}
},
scales: {
x: {
grid: { display: false }
},
y: {
beginAtZero: true,
ticks: {
callback: function(value) {
return formatValue(value, 'currency');
}
}
}
}
}
});
}
Bar Chart
function createBarChart(canvasId, labels, data, options = {}) {
const ctx = document.getElementById(canvasId).getContext('2d');
const isHorizontal = options.horizontal || labels.length > 8;
return new Chart(ctx, {
type: 'bar',
data: {
labels: labels,
datasets: [{
label: options.label || 'Value',
data: data,
backgroundColor: options.colors || COLORS.map(c => c + 'CC'),
borderColor: options.colors || COLORS,
borderWidth: 1,
borderRadius: 4,
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
indexAxis: isHorizontal ? 'y' : 'x',
plugins: {
legend: { display: false },
tooltip: {
callbacks: {
label: function(context) {
return formatValue(context.parsed[isHorizontal ? 'x' : 'y'], options.format || 'number');
}
}
}
},
scales: {
x: {
beginAtZero: true,
grid: { display: isHorizontal },
ticks: isHorizontal ? {
callback: function(value) {
return formatValue(value, options.format || 'number');
}
} : {}
},
y: {
beginAtZero: !isHorizontal,
grid: { display: !isHorizontal },
ticks: !isHorizontal ? {
callback: function(value) {
return formatValue(value, options.format || 'number');
}
} : {}
}
}
}
});
}
Doughnut Chart
function createDoughnutChart(canvasId, labels, data) {
const ctx = document.getElementById(canvasId).getContext('2d');
return new Chart(ctx, {
type: 'doughnut',
data: {
labels: labels,
datasets: [{
data: data,
backgroundColor: COLORS.map(c => c + 'CC'),
borderColor: '#ffffff',
borderWidth: 2,
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
cutout: '60%',
plugins: {
legend: {
position: 'right',
labels: { usePointStyle: true, padding: 15 }
},
tooltip: {
callbacks: {
label: function(context) {
const total = context.dataset.data.reduce((a, b) => a + b, 0);
const pct = ((context.parsed / total) * 100).toFixed(1);
return `${context.label}: ${formatValue(context.parsed, 'number')} (${pct}%)`;
}
}
}
}
}
});
}
Updating Charts on Filter Change
function updateChart(chart, newLabels, newData) {
chart.data.labels = newLabels;
if (Array.isArray(newData[0])) {
// Multiple datasets
newData.forEach((data, i) => {
chart.data.datasets[i].data = data;
});
} else {
chart.data.datasets[0].data = newData;
}
chart.update('none'); // 'none' disables animation for instant update
}
Filter and Interactivity Implementation
Dropdown Filter
<div class="filter-group">
<label for="filter-region">Region</label>
<select id="filter-region" onchange="dashboard.applyFilters()">
<option value="all">All Regions</option>
</select>
</div>
function populateFilter(selectId, data, field) {
const select = document.getElementById(selectId);
const values = [...new Set(data.map(d => d[field]))].sort();
// Keep the "All" option, add unique values
values.forEach(val => {
const option = document.createElement('option');
option.value = val;
option.textContent = val;
select.appendChild(option);
});
}
function getFilterValue(selectId) {
const val = document.getElementById(selectId).value;
return val === 'all' ? null : val;
}
Date Range Filter
<div class="filter-group">
<label>Date Range</label>
<input type="date" id="filter-date-start" onchange="dashboard.applyFilters()">
<span>to</span>
<input type="date" id="filter-date-end" onchange="dashboard.applyFilters()">
</div>
function filterByDateRange(data, dateField, startDate, endDate) {
return data.filter(row => {
const rowDate = new Date(row[dateField]);
if (startDate && rowDate < new Date(startDate)) return false;
if (endDate && rowDate > new Date(endDate)) return false;
return true;
});
}
Combined Filter Logic
applyFilters() {
const region = getFilterValue('filter-region');
const category = getFilterValue('filter-category');
const startDate = document.getElementById('filter-date-start').value;
const endDate = document.getElementById('filter-date-end').value;
this.filteredData = this.rawData.filter(row => {
if (region && row.region !== region) return false;
if (category && row.category !== category) return false;
if (startDate && row.date < startDate) return false;
if (endDate && row.date > endDate) return false;
return true;
});
this.renderKPIs();
this.updateCharts();
this.renderTable();
}
Sortable Table
function renderTable(containerId, data, columns) {
const container = document.getElementById(containerId);
let sortCol = null;
let sortDir = 'desc';
function render(sortedData) {
let html = '<table class="data-table">';
// Header
html += '<thead><tr>';
columns.forEach(col => {
const arrow = sortCol === col.field
? (sortDir === 'asc' ? ' ▲' : ' ▼')
: '';
html += `<th onclick="sortTable('${col.field}')" style="cursor:pointer">${col.label}${arrow}</th>`;
});
html += '</tr></thead>';
// Body
html += '<tbody>';
sortedData.forEach(row => {
html += '<tr>';
columns.forEach(col => {
const value = col.format ? formatValue(row[col.field], col.format) : row[col.field];
html += `<td>${value}</td>`;
});
html += '</tr>';
});
html += '</tbody></table>';
container.innerHTML = html;
}
window.sortTable = function(field) {
if (sortCol === field) {
sortDir = sortDir === 'asc' ? 'desc' : 'asc';
} else {
sortCol = field;
sortDir = 'desc';
}
const sorted = [...data].sort((a, b) => {
const aVal = a[field], bVal = b[field];
const cmp = aVal < bVal ? -1 : aVal > bVal ? 1 : 0;
return sortDir === 'asc' ? cmp : -cmp;
});
render(sorted);
};
render(data);
}
CSS Styling for Dashboards
Color System
:root {
/* Background layers */
--bg-primary: #f8f9fa;
--bg-card: #ffffff;
--bg-header: #1a1a2e;
/* Text */
--text-primary: #212529;
--text-secondary: #6c757d;
--text-on-dark: #ffffff;
/* Accent colors for data */
--color-1: #4C72B0;
--color-2: #DD8452;
--color-3: #55A868;
--color-4: #C44E52;
--color-5: #8172B3;
--color-6: #937860;
/* Status colors */
--positive: #28a745;
--negative: #dc3545;
--neutral: #6c757d;
/* Spacing */
--gap: 16px;
--radius: 8px;
}
Layout
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
background: var(--bg-primary);
color: var(--text-primary);
line-height: 1.5;
}
.dashboard-container {
max-width: 1400px;
margin: 0 auto;
padding: var(--gap);
}
.dashboard-header {
background: var(--bg-header);
color: var(--text-on-dark);
padding: 20px 24px;
border-radius: var(--radius);
margin-bottom: var(--gap);
display: flex;
justify-content: space-between;
align-items: center;
flex-wrap: wrap;
gap: 12px;
}
.dashboard-header h1 {
font-size: 20px;
font-weight: 600;
}
KPI Cards
.kpi-row {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: var(--gap);
margin-bottom: var(--gap);
}
.kpi-card {
background: var(--bg-card);
border-radius: var(--radius);
padding: 20px 24px;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.08);
}
.kpi-label {
font-size: 13px;
color: var(--text-secondary);
text-transform: uppercase;
letter-spacing: 0.5px;
margin-bottom: 4px;
}
.kpi-value {
font-size: 28px;
font-weight: 700;
color: var(--text-primary);
margin-bottom: 4px;
}
.kpi-change {
font-size: 13px;
font-weight: 500;
}
.kpi-change.positive { color: var(--positive); }
.kpi-change.negative { color: var(--negative); }
Chart Containers
.chart-row {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(400px, 1fr));
gap: var(--gap);
margin-bottom: var(--gap);
}
.chart-container {
background: var(--bg-card);
border-radius: var(--radius);
padding: 20px 24px;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.08);
}
.chart-container h3 {
font-size: 14px;
font-weight: 600;
color: var(--text-primary);
margin-bottom: 16px;
}
.chart-container canvas {
max-height: 300px;
}
Filters
.filters {
display: flex;
gap: 12px;
align-items: center;
flex-wrap: wrap;
}
.filter-group {
display: flex;
align-items: center;
gap: 6px;
}
.filter-group label {
font-size: 12px;
color: rgba(255, 255, 255, 0.7);
}
.filter-group select,
.filter-group input[type="date"] {
padding: 6px 10px;
border: 1px solid rgba(255, 255, 255, 0.2);
border-radius: 4px;
background: rgba(255, 255, 255, 0.1);
color: var(--text-on-dark);
font-size: 13px;
}
.filter-group select option {
background: var(--bg-header);
color: var(--text-on-dark);
}
Data Table
.table-section {
background: var(--bg-card);
border-radius: var(--radius);
padding: 20px 24px;
box-shadow: 0 1px 3px rgba(0, 0, 0, 0.08);
overflow-x: auto;
}
.data-table {
width: 100%;
border-collapse: collapse;
font-size: 13px;
}
.data-table thead th {
text-align: left;
padding: 10px 12px;
border-bottom: 2px solid #dee2e6;
color: var(--text-secondary);
font-weight: 600;
font-size: 12px;
text-transform: uppercase;
letter-spacing: 0.5px;
white-space: nowrap;
user-select: none;
}
.data-table thead th:hover {
color: var(--text-primary);
background: #f8f9fa;
}
.data-table tbody td {
padding: 10px 12px;
border-bottom: 1px solid #f0f0f0;
}
.data-table tbody tr:hover {
background: #f8f9fa;
}
.data-table tbody tr:last-child td {
border-bottom: none;
}
Responsive Design
@media (max-width: 768px) {
.dashboard-header {
flex-direction: column;
align-items: flex-start;
}
.kpi-row {
grid-template-columns: repeat(2, 1fr);
}
.chart-row {
grid-template-columns: 1fr;
}
.filters {
flex-direction: column;
align-items: flex-start;
}
}
@media print {
body { background: white; }
.dashboard-container { max-width: none; }
.filters { display: none; }
.chart-container { break-inside: avoid; }
.kpi-card { border: 1px solid #dee2e6; box-shadow: none; }
}
Performance Considerations for Large Datasets
Data Size Guidelines
| Data Size | Approach |
|---|---|
| <1,000 rows | Embed directly in HTML. Full interactivity. |
| 1,000 - 10,000 rows | Embed in HTML. May need to pre-aggregate for charts. |
| 10,000 - 100,000 rows | Pre-aggregate server-side. Embed only aggregated data. |
| >100,000 rows | Not suitable for client-side dashboard. Use a BI tool or paginate. |
Pre-Aggregation Pattern
Instead of embedding raw data and aggregating in the browser:
// DON'T: embed 50,000 raw rows
const RAW_DATA = [/* 50,000 rows */];
// DO: pre-aggregate before embedding
const CHART_DATA = {
monthly_revenue: [
{ month: '2024-01', revenue: 150000, orders: 1200 },
{ month: '2024-02', revenue: 165000, orders: 1350 },
// ... 12 rows instead of 50,000
],
top_products: [
{ product: 'Widget A', revenue: 45000 },
// ... 10 rows
],
kpis: {
total_revenue: 1980000,
total_orders: 15600,
avg_order_value: 127,
}
};
Chart Performance
- Limit line charts to <500 data points per series (downsample if needed)
- Limit bar charts to <50 categories
- For scatter plots, cap at 1,000 points (use sampling for larger datasets)
- Disable animations for dashboards with many charts:
animation: falsein Chart.js options - Use
Chart.update('none')instead ofChart.update()for filter-triggered updates
DOM Performance
- Limit data tables to 100-200 visible rows. Add pagination for more.
- Use
requestAnimationFramefor coordinated chart updates - Avoid rebuilding the entire DOM on filter change -- update only changed elements
// Efficient table pagination
function renderTablePage(data, page, pageSize = 50) {
const start = page * pageSize;
const end = Math.min(start + pageSize, data.length);
const pageData = data.slice(start, end);
// Render only pageData
// Show pagination controls: "Showing 1-50 of 2,340"
}
Examples
/build-dashboard Monthly sales dashboard with revenue trend, top products, and regional breakdown. Data is in the orders table.
/build-dashboard Here's our support ticket data [pastes CSV]. Build a dashboard showing volume by priority, response time trends, and resolution rates.
/build-dashboard Create a template executive dashboard for a SaaS company showing MRR, churn, new customers, and NPS. Use sample data.
Tips
- Dashboards are fully self-contained HTML files -- share them with anyone by sending the file
- For real-time dashboards, consider connecting to a BI tool instead. These dashboards are point-in-time snapshots
- Request "dark mode" or "presentation mode" for different styling
- You can request a specific color scheme to match your brand
常見問題
build-dashboard 技能有什麼用途?
建立包含圖表、篩選器和表格的互動式 HTML 儀錶板,可直接在瀏覽器中開啟。
如何安裝 build-dashboard?
打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。
build-dashboard 適用哪些 AI 模型?
技能是純 Markdown 指示(Agent Skills 格式),因此 build-dashboard 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。
build-dashboard 是免費的嗎?
此技能由 anthropics 公開發布,採用 Apache-2.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。
安裝技能安全嗎?
技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。
技能和 MCP 伺服器或外掛有何不同?
MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。