Skill UI

This module provides a compliance-grade, tamper-evident logging solution for tracking all Kling AI operations. It records task submissions, status changes, and credential usage into structured, hashed logs. Use this when meeting regulatory compliance requirements or preparing for internal/external security audits.

A comprehensive guide to controlling cinematic camera movements (pan, tilt, zoom, dolly, roll) in AI video generation (text-to-video and image-to-video). Learn how to utilize specific parameters to add professional dynamic shots, such as dramatic zooms, slow pans, or upward reveals, significantly enhancing the artistic quality of your AI-generated content.

This guide provides a comprehensive reference for diagnosing and resolving common errors encountered when interacting with the Kling AI API. It covers various HTTP status codes (e.g., 400, 401, 429), task-level failures (e.g., content policy violations, low image quality), proper JWT token management, and implementing rate limit handling with exponential backoff. Use this resource when troubleshooting failed video generation or API integration issues.

A comprehensive framework for assessing the security, privacy, and compliance posture of Kling AI integrations. It covers crucial areas such as data residency, credential management, content policy adherence, and GDPR compliance (e.g., right to erasure). Use this guide when preparing for security audits or implementing robust data handling protocols for AI-generated content.

Implements robust content policy compliance for generative AI prompts and outputs, such as those from Kling AI. This skill provides pre-submission filtering functionality, preventing the generation of restricted content (e.g., violence, explicit material, hate speech) and saving API credits. It includes Python code examples for prompt screening and handling server-side rejection messages.

This utility provides robust cost control mechanisms for integrating AI generation APIs. It implements daily budget enforcement, per-request cost estimation, and usage alerting. Developers can use it to prevent unexpected overspending and effectively manage credits and resources when building AI-powered applications.

A comprehensive utility for structured logging, request tracing, and diagnostic purposes when integrating with the Kling AI API. It automatically captures request/response pairs, task lifecycle events, and detailed timing metrics for every API call, making troubleshooting video generation pipelines efficient and systematic. Ideal for developers building robust, observable AI applications.

This skill utilizes the Kling AI API to transform static images into cinematic, animated videos. It supports advanced control mechanisms including motion prompts (describing desired movement), dynamic masks (motion brushing for specific object paths), and static masks (freezing background areas). Users can also define start-to-end frame transitions using `image_tail`. Ideal for content creators needing high-quality, controllable video generation from existing photos.

This guide documents common mistakes, gotchas, and anti-patterns encountered during Kling AI API integration. It covers critical best practices regarding data types, token management, polling, URL persistence, and prompt engineering to ensure stable and reliable AI video generation workflows. Essential reading for robust development and troubleshooting.

This skill serves as a comprehensive reference guide for Kling AI's various generative models. It details capabilities across video generation (T2V, I2V), image styling, lip sync, and virtual try-on. Users can compare model versions based on quality, speed, and cost to select the optimal model for their creative or production pipeline.

This comprehensive guide provides strategies for optimizing video generation using Kling AI. It covers the crucial trade-offs between speed, quality, and cost by selecting the optimal model and parameters. Techniques detailed include performance benchmarking, implementing connection pooling, advanced caching strategies, and prompt engineering best practices to ensure efficient and high-quality outputs for any production use case.

A comprehensive checklist designed for ensuring that Kling AI video generation integrations are robust, secure, and reliable before going live or during a deployment review. It covers critical areas including authentication protocols, detailed error handling (retry logic, timeouts), cost control mechanisms, content safety policies, and necessary monitoring setup for a stable production environment.