library/specializations/cli-mcp-development/skills/cli-progress-bar-setup/SKILL.md
Configure cli-progress with custom formatters, multi-bar support, and ETA calculations for CLI progress indication.
npx skillsauth add a5c-ai/babysitter cli-progress-bar-setupInstall 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.
Configure cli-progress for advanced progress indication.
Invoke this skill when you need to:
| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | language | string | Yes | Target language | | format | string | No | Bar format template | | presets | array | No | Progress bar presets |
import cliProgress, { SingleBar, MultiBar, Presets } from 'cli-progress';
import chalk from 'chalk';
// Custom format with colors
const defaultFormat = chalk.cyan('{bar}') +
' {percentage}% | ETA: {eta}s | {value}/{total} | {filename}';
// Create single progress bar
export function createProgressBar(options?: {
format?: string;
barSize?: number;
}): SingleBar {
return new SingleBar({
format: options?.format || defaultFormat,
barCompleteChar: '\u2588',
barIncompleteChar: '\u2591',
barsize: options?.barSize || 40,
hideCursor: true,
clearOnComplete: false,
stopOnComplete: true,
}, Presets.shades_classic);
}
// Multi-bar for parallel tasks
export function createMultiBar(): MultiBar {
return new MultiBar({
format: '{name} |' + chalk.cyan('{bar}') + '| {percentage}% | {value}/{total}',
barCompleteChar: '\u2588',
barIncompleteChar: '\u2591',
hideCursor: true,
clearOnComplete: false,
stopOnComplete: false,
}, Presets.shades_grey);
}
// Download progress with speed
export function createDownloadBar(): SingleBar {
return new SingleBar({
format: 'Downloading |' + chalk.cyan('{bar}') +
'| {percentage}% | {speed} MB/s | {value}/{total} MB',
barCompleteChar: '\u2588',
barIncompleteChar: '\u2591',
barsize: 30,
formatValue: (value, options, type) => {
if (type === 'value' || type === 'total') {
return (value / 1024 / 1024).toFixed(2);
}
return String(value);
},
}, Presets.shades_classic);
}
// Wrapper for async operations
export async function withProgress<T>(
total: number,
fn: (update: (current: number, payload?: Record<string, any>) => void) => Promise<T>,
options?: { format?: string }
): Promise<T> {
const bar = createProgressBar(options);
bar.start(total, 0);
try {
const result = await fn((current, payload) => {
bar.update(current, payload);
});
bar.stop();
return result;
} catch (error) {
bar.stop();
throw error;
}
}
// Batch processing with progress
export async function processBatch<T, R>(
items: T[],
processor: (item: T, index: number) => Promise<R>,
options?: { label?: string }
): Promise<R[]> {
const bar = createProgressBar({
format: `${options?.label || 'Processing'} |{bar}| {percentage}% | {value}/{total}`,
});
bar.start(items.length, 0);
const results: R[] = [];
for (let i = 0; i < items.length; i++) {
results.push(await processor(items[i], i));
bar.update(i + 1);
}
bar.stop();
return results;
}
// Parallel processing with multi-bar
export async function processParallel<T, R>(
tasks: Array<{
name: string;
items: T[];
processor: (item: T) => Promise<R>;
}>
): Promise<Map<string, R[]>> {
const multiBar = createMultiBar();
const bars = new Map<string, SingleBar>();
const results = new Map<string, R[]>();
// Create bars for each task
for (const task of tasks) {
const bar = multiBar.create(task.items.length, 0, { name: task.name.padEnd(15) });
bars.set(task.name, bar);
results.set(task.name, []);
}
// Process all tasks in parallel
await Promise.all(tasks.map(async (task) => {
const bar = bars.get(task.name)!;
const taskResults = results.get(task.name)!;
for (let i = 0; i < task.items.length; i++) {
taskResults.push(await task.processor(task.items[i]));
bar.update(i + 1);
}
}));
multiBar.stop();
return results;
}
{
"dependencies": {
"cli-progress": "^3.12.0",
"chalk": "^5.0.0"
},
"devDependencies": {
"@types/cli-progress": "^3.11.0"
}
}
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.