Fetches build-dashboard from anthropics/knowledge-work-plugins and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/build-dashboard
Restart Cursor to activate build-dashboard. Access via /build-dashboard in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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:
Query the necessary data
Embed the results as JSON within the HTML file
If data is pasted or uploaded:
Parse and clean the data
Embed as JSON in the dashboard
If working from a description without data:
Create a realistic sample dataset matching the described schema
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
Save the dashboard as an HTML file with a descriptive name (e.g., sales_dashboard.html)
Open it in the user's default browser
Confirm it renders correctly
Provide instructions for updating data or customizing
Base Template
Every dashboard follows this structure:
<!DOCTYPEhtml><htmllang="en"><head><metacharset="UTF-8"><metaname="viewport"content="width=device-width, initial-scale=1.0"><title>Dashboard Title</title><scriptsrc="https://cdn.jsdelivr.net/npm/[email protected]"integrity="sha384-jb8JQMbMoBUzgWatfe6COACi2ljcDdZQ2OxczGA3bGNeWe+6DChMTBJemed7ZnvJ"crossorigin="anonymous"></script><scriptsrc="https://cdn.jsdelivr.net/npm/[email protected]"integrity="sha384-cVMg8E3QFwTvGCDuK+ET4PD341jF3W8nO1auiXfuZNQkzbUUiBGLsIQUE+b1mxws"crossorigin="anonymous"></script><style>/* Dashboard styles go here */</style></head><body><divclass="dashboard-container"><headerclass="dashboard-header"><h1>Dashboard Title</h1><divclass="filters"><!-- Filter controls --></div></header><sectionclass="kpi-row"><!-- KPI cards --></section><sectionclass="chart-row"><!-- Chart containers --></section><sectionclass="table-section"><!-- Data table --></section><footerclass="dashboard-footer"><span>Data as of: <spanid="data-date"></span></span></footer></div><script>// Embedded dataconstDATA=[];// Dashboard logicclassDashboard{constructor(data){this.rawData= data;this.filteredData= data;this.charts={};this.init();}init(){this.setupFilters();this.renderKPIs();this.renderCharts();this.renderTable();}applyFilters(){// Filter logicthis.
Implementation Guide
Prerequisites
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
