kling-video

Generate Kling videos from prompts or images via the Kling API into MP4s.

Updated Mar 26, 2026

Search Tags:#automation #validation #models #prompts #wrapper #video-generation #kling

Ready to run

npx skills add https://github.com/jlenrique/course-DEV-IDE-with-AGENTS --skill kling-video-jlenrique

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: kling-video
Source: https://github.com/jlenrique/course-DEV-IDE-with-AGENTS/tree/main/skills/kling-video
Command: npx skills add https://github.com/jlenrique/course-DEV-IDE-with-AGENTS --skill kling-video-jlenrique

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, python-dotenv, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Kling video generation for educational tasks is complex, requiring consistent prompts, model guidance, and robust validation. This skill bundles prompt patterns, model-selection guidance, and execution wrappers to enable reliable Kling video production.

Core Features & Use Cases

Prompt patterns and model guidance for Kling video generation (text2video and image2video) to produce educational visuals.
Validation-lane support with references, receipts, and production-informed decision trees to ensure reproducibility.
Execution wrappers to submit, poll, download, and patch state, enabling end-to-end production and reproducibility.

Quick Start

Provide a Kling prompt or image input, select a model and mode, and download the produced MP4.

Frequently Asked Questions about kling-video

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate Kling video content from text prompts for educational visuals?▼

Kling video generation converts text prompts into MP4s by applying prompt patterns and model guidance through execution wrappers that submit, poll, and download ready-to-use educational video files.

Can I use image inputs to create Kling videos with this API wrapper?▼

Image2video generation is supported alongside text2video. The execution wrappers accept image inputs, apply model guidance, and validate the resulting MP4 output through the production lane.

How does the validation workflow ensure reproducible Kling video generation?▼

The validation lane enforces reproducibility through canonical receipts, references, and local validation scripts. It applies production-informed decision trees to verify outputs before finalizing the MP4 download.

Do I need pyyaml and python-dotenv to run Kling video generation scripts?▼

Yes, pyyaml and python-dotenv are required dependencies. They manage configuration loading and environment variables needed by the execution wrappers to authenticate and interact with the Kling API.

What is the best way to automate Kling video submission and polling?▼

Execution wrappers provide end-to-end automation for Kling video submission, polling, and state patching. They drive the workflow from prompt input to MP4 download while maintaining safety and traceability.

Why does my Kling video generation workflow need canonical receipts and references?▼

Canonical receipts and references enforce safety and traceability across the production and validation lanes. They track the generation state, ensuring every MP4 output matches the applied prompt patterns and model guidance.