Skip to main content
ClaudeWave
Skill693 repo starsupdated 12d ago

build-dashboard

The build-dashboard skill generates a single self-contained HTML file that displays interactive charts, KPI cards, filterable tables, and professional styling without requiring a server or external dependencies. Use this skill when creating executive dashboards with key metrics, converting query results into shareable reports, building team monitoring snapshots, or combining multiple visualizations with filtering capabilities into one browser-accessible file.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/openyak/openyak /tmp/build-dashboard && cp -r /tmp/build-dashboard/backend/app/data/plugins/data/skills/build-dashboard ~/.claude/skills/build-dashboard
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# /build-dashboard - Build Interactive Dashboards

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../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:

```html
<!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/chart.js@4.5.1" integrity="sha384-jb8JQMbMoBUzgWatfe6COACi2ljcDdZQ2OxczGA3bGNeWe+6DChMTBJemed7ZnvJ" crossorigin="anonymous"></script>
    <script src="https://cdn.jsdelivr.net/npm/chartjs-adapter-date-fns@3.0.0" 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();
instrument-data-to-allotropeSkill

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

nextflow-developmentSkill

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

scientific-problem-selectionSkill

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".

scvi-toolsSkill

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

single-cell-rna-qcSkill

Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.

startSkill

Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.

cowork-plugin-customizerSkill

>

create-cowork-pluginSkill

>