library/specializations/cli-mcp-development/skills/blessed-widget-generator/SKILL.md
Generate blessed widgets for Node.js terminal UIs with boxes, lists, forms, and dashboard layouts.
npx skillsauth add a5c-ai/babysitter blessed-widget-generatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Generate blessed widgets for Node.js terminal UIs.
Invoke this skill when you need to:
| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | projectName | string | Yes | Project name | | layout | string | No | Layout type (dashboard, form, list) | | widgets | array | No | Widget definitions |
const blessed = require('blessed');
const contrib = require('blessed-contrib');
// Create screen
const screen = blessed.screen({
smartCSR: true,
title: 'System Dashboard',
});
// Create grid layout
const grid = new contrib.grid({
rows: 12,
cols: 12,
screen: screen,
});
// CPU Line Chart
const cpuLine = grid.set(0, 0, 4, 6, contrib.line, {
style: { line: 'yellow', text: 'green', baseline: 'black' },
xLabelPadding: 3,
xPadding: 5,
showLegend: true,
label: 'CPU Usage',
});
// Memory Gauge
const memGauge = grid.set(0, 6, 4, 6, contrib.gauge, {
label: 'Memory Usage',
stroke: 'green',
fill: 'white',
});
// Log Box
const logBox = grid.set(4, 0, 4, 12, contrib.log, {
fg: 'green',
selectedFg: 'green',
label: 'Logs',
});
// Process Table
const processTable = grid.set(8, 0, 4, 12, contrib.table, {
keys: true,
fg: 'white',
selectedFg: 'white',
selectedBg: 'blue',
interactive: true,
label: 'Processes',
columnSpacing: 4,
columnWidth: [10, 30, 10, 10],
});
// Update data
function updateDashboard() {
// CPU data
cpuLine.setData([{
title: 'CPU',
x: ['1', '2', '3', '4', '5'],
y: [Math.random() * 100, Math.random() * 100, Math.random() * 100, Math.random() * 100, Math.random() * 100],
}]);
// Memory
memGauge.setPercent(Math.random() * 100);
// Logs
logBox.log(`[${new Date().toISOString()}] System event`);
// Processes
processTable.setData({
headers: ['PID', 'Name', 'CPU', 'MEM'],
data: [
['1234', 'node', '2.5%', '150MB'],
['5678', 'chrome', '15.2%', '500MB'],
],
});
screen.render();
}
// Key bindings
screen.key(['escape', 'q', 'C-c'], () => process.exit(0));
// Update interval
setInterval(updateDashboard, 1000);
updateDashboard();
screen.render();
const blessed = require('blessed');
const screen = blessed.screen({
smartCSR: true,
title: 'Configuration Form',
});
const form = blessed.form({
parent: screen,
keys: true,
left: 'center',
top: 'center',
width: 60,
height: 20,
border: { type: 'line' },
style: { border: { fg: 'blue' } },
});
// Input field
blessed.text({
parent: form,
top: 1,
left: 2,
content: 'Username:',
});
const usernameInput = blessed.textbox({
parent: form,
name: 'username',
top: 1,
left: 12,
height: 1,
width: 40,
inputOnFocus: true,
style: { fg: 'white', bg: 'black' },
});
// Password field
blessed.text({
parent: form,
top: 3,
left: 2,
content: 'Password:',
});
const passwordInput = blessed.textbox({
parent: form,
name: 'password',
top: 3,
left: 12,
height: 1,
width: 40,
censor: true,
inputOnFocus: true,
style: { fg: 'white', bg: 'black' },
});
// Submit button
const submitBtn = blessed.button({
parent: form,
name: 'submit',
content: ' Submit ',
top: 6,
left: 'center',
shrink: true,
style: {
fg: 'white',
bg: 'blue',
focus: { bg: 'green' },
},
});
submitBtn.on('press', () => {
form.submit();
});
form.on('submit', (data) => {
console.log('Form data:', data);
process.exit(0);
});
screen.key(['escape', 'q'], () => process.exit(0));
screen.render();
{
"dependencies": {
"blessed": "^0.1.81",
"blessed-contrib": "^4.10.1"
}
}
development
Model documentation skill for generating model cards following Google's model card framework.
development
MLflow integration skill for experiment tracking, model registry, and artifact management. Enables LLMs to log experiments, compare runs, manage model lifecycle, and retrieve artifacts through the MLflow API.
data-ai
LIME-based local explanation skill for individual predictions across tabular, text, and image data.
devops
Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML.