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home / skills / jeremylongshore / claude-code-plugins-plus-skills / klingai-debug-bundle

klingai-debug-bundle skill

/plugins/saas-packs/klingai-pack/skills/klingai-debug-bundle

This skill helps you implement comprehensive logging, tracing, and debugging utilities for Kling AI to quickly identify and resolve issues.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill klingai-debug-bundle

Review the files below or copy the command above to add this skill to your agents.

Files (7)
SKILL.md
1.5 KB
---
name: klingai-debug-bundle
description: |
  Execute set up comprehensive logging and debugging for Kling AI. Use when investigating issues or
  monitoring requests. Trigger with phrases like 'klingai debug', 'kling ai logging',
  'trace klingai', 'monitor klingai requests'.
allowed-tools: Read, Write, Edit, Grep
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---

# 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/)

Overview

This skill installs a comprehensive logging and debugging bundle for Kling AI integrations to make diagnosing issues fast and repeatable. It configures structured request/response logging, request tracing, timing metrics, and lightweight debug utilities. Use it to get consistent telemetry and readable diagnostic reports across services.

How this skill works

The bundle instruments Kling AI API calls to capture structured logs and traces, annotating each request with identifiers, timings, and error metadata. It emits logs in a standard JSON format, records timing metrics for latency analysis, and exposes simple debug endpoints or commands to generate trace summaries and diagnostic snapshots. Optional integrations can forward telemetry to your existing logging or APM systems.

When to use it

  • Investigating intermittent or reproducible failures in Kling AI integrations
  • Monitoring request volumes and latency after deployments
  • Collecting detailed diagnostics before escalating to platform engineering
  • Auditing request flows for troubleshooting or performance tuning
  • During local development to reproduce and capture failing interactions

Best practices

  • Use structured JSON logs with consistent keys: request_id, route, status, duration, and error
  • Attach a unique trace or request_id to every incoming and outgoing call for end-to-end correlation
  • Record both start and end timestamps and compute durations to capture true latency
  • Mask or redact sensitive fields (PII, secrets) before logging or exporting traces
  • Route high-volume telemetry to a sampling or metrics backend to avoid storage overload

Example use cases

  • Enable debug mode when a particular user report contains an error to capture full request/response traces
  • Add timing metrics to measure and compare response latency across model versions
  • Configure alerts for elevated error rates or spike in 5xx responses tied to Kling AI endpoints
  • Run a diagnostic command to produce a compact trace summary for a specific request_id
  • Integrate with OpenTelemetry or your existing logging platform to visualize call graphs and latency distributions

FAQ

Will this bundle log sensitive data?

No — follow the masking best practice included: redact PII and secrets before writing logs or exporting traces.

Can I integrate these traces with my existing APM?

Yes — the bundle emits standard telemetry and supports OpenTelemetry-compatible exporters for most APMs.

Does this support both Python and Node.js?

Yes — the approach is language-agnostic; example implementations target Python 3.8+ and Node.js 18+.