klingai-debug-bundle

# Klingai Debug Bundle

## Overview

This skill shows how to implement request/response logging, timing metrics, and debugging utilities for Kling AI integrations to quickly identify and resolve issues.

## Prerequisites

- Kling AI integration
- Python 3.8+ or Node.js 18+
- Logging infrastructure (optional but recommended)

## Instructions

Follow these steps to set up debugging:

1. **Configure Logging**: Set up structured logging
2. **Add Request Tracing**: Track all API requests
3. **Implement Timing**: Measure performance metrics
4. **Create Debug Utilities**: Build diagnostic tools
5. **Set Up Alerts**: Configure error notifications

## Output

Successful execution produces:
- Structured logging output
- Request traces with timing
- Performance metrics dashboard
- Debug reports for troubleshooting

## Error Handling

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

## Examples

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

## Resources

- [Python Logging](https://docs.python.org/3/library/logging.html)
- [Structured Logging Best Practices](https://www.structlog.org/)
- [OpenTelemetry](https://opentelemetry.io/)