klingai-usage-analytics

# Klingai Usage Analytics

## Overview

This skill shows how to build comprehensive usage analytics including generation metrics, cost analysis, trend reporting, and visualization dashboards for Kling AI.

## Prerequisites

- Kling AI API key configured
- Usage data collection in place
- Python 3.8+ with pandas/matplotlib (optional)

## Instructions

Follow these steps for analytics:

1. **Collect Data**: Capture usage events
2. **Aggregate Metrics**: Calculate key metrics
3. **Generate Reports**: Create usage reports
4. **Visualize Data**: Build dashboards
5. **Set Up Alerts**: Anomaly detection

## Output

Successful execution produces:
- Usage summary statistics
- Daily breakdown reports
- Top user analysis
- Anomaly detection alerts
- Exportable CSV data

## Error Handling

See `{baseDir}/references/errors.md` for comprehensive error handling.

## Examples

See `{baseDir}/references/examples.md` for detailed examples.

## Resources

- [Kling AI Dashboard](https://console.klingai.com/usage)
- [pandas Documentation](https://pandas.pydata.org/)
- [Data Visualization](https://matplotlib.org/)