Al Video Generation

Use this skill as an entry point to discover, select, and fetch specific integration parameters for all supported AI video generation models.

ShortAPl@isdyh01

Install

openclaw skills install @isdyh01/shortapi-ai-video-generation

Video Generation Models Integration Skill

Use this skill to explore and integrate all available Video Generation models through the ShortAPI platform.

Overview

ShortAPI provides a unified /api/v1/job/create endpoint for video generation across multiple top-tier providers natively. This skill provides an overview of all available video generation models and how to dynamically acquire the specific JSON schema required to invoke them.

API Endpoint: https://api.shortapi.ai/api/v1/job/create
Category: text-to-video, image-to-video
Kind: inference

Available Video Models

Here is the list of fully supported video generation model IDs you can use:

Model ID	Description
`google/veo-3.1/text-to-video`	Generate videos from text using Veo 3.1
`google/veo-3.1/image-to-video`	Generate videos from images using Veo 3.1
`google/veo-3.1/extend-video`	Extend existing videos using Veo 3.1
`google/veo-3.1/first-last-frame-to-video`	Generate videos from first and last frames (Veo 3.1)
`google/veo-3.1/reference-to-video`	Generate videos from reference using Veo 3.1
`google/veo-3/text-to-video`	Generate videos from text using Veo 3
`google/veo-3/image-to-video`	Generate videos from images using Veo 3
`kwaivgi/kling-3.0/text-to-video`	Generate videos from text using Kling 3.0
`kwaivgi/kling-3.0/image-to-video`	Generate videos from images using Kling 3.0
`kwaivgi/kling-o1/text-to-video`	Generate videos from text using Kling O1
`kwaivgi/kling-o1/image-to-video`	Generate videos from images using Kling O1
`kwaivgi/kling-o1/video-to-video`	Transform videos using Kling O1
`kwaivgi/kling-2.6/text-to-video`	Generate videos from text using Kling 2.6
`kwaivgi/kling-2.6/image-to-video`	Generate videos from images using Kling 2.6
`bytedance/seedance-2.0/text-to-video`	Generate videos from text using Seedance 2.0
`vidu/vidu-q3/text-to-video`	Generate videos from text using Vidu Q3
`vidu/vidu-q3/image-to-video`	Generate videos from images using Vidu Q3
`vidu/vidu-q3/start-end-to-video`	Generate videos from start/end frames (Vidu Q3)
`vidu/vidu-q2/text-to-video`	Generate videos from text using Vidu Q2
`vidu/vidu-q2/image-to-video`	Generate videos from images using Vidu Q2
`vidu/vidu-q2/reference-to-video`	Generate videos from reference using Vidu Q2
`vidu/vidu-q2/start-end-to-video`	Generate videos from start/end frames (Vidu Q2)
`pixverse/pixverse-5.5/text-to-video`	Generate videos from text using Pixverse 5.5
`pixverse/pixverse-5.5/image-to-video`	Generate videos from images using Pixverse 5.5
`pixverse/pixverse-5.5/transition`	Create video transitions using Pixverse 5.5
`alibaba/wan-2.6/text-to-video`	Generate videos from text using Wan 2.6
`alibaba/wan-2.6/image-to-video`	Generate videos from images using Wan 2.6
`alibaba/wan-2.6/reference-to-video`	Generate videos from reference using Wan 2.6

How to use a Video Model

Because each video model supports different parameters (such as duration, resolution, aspect_ratio, fps, or advanced controls), you need to fetch the specific model's schema document to construct a valid API request payload.

Step 1: Fetch the specific Model API Skill Document (MANDATORY)

You MUST first fetch the detailed skill document for the specific <model_id> (e.g. google/veo-3.1/text-to-video) before attempting to construct the POST request payload. DO NOT skip this step. DO NOT hallucinate parameters because different video models have completely different parameter names for the same concept (e.g. one model might use duration while another uses length, one might use resolution while another uses quality).

Send a GET request to:

text

https://shortapi.ai/api/skill/<model_id>

(For example: GET https://shortapi.ai/api/skill/google/veo-3.1/text-to-video)

This URL will return a Markdown (.md) text document containing the exact Input Parameters Schema for that specific model, alongside code examples. You must parse it to understand which arguments go into the args object.

Step 2: Construct the JSON Payload

Using the exact schema document fetched from Step 1, construct a valid JSON payload. Only include arguments that were defined in the document fetched in Step 1. At a minimum, standard structures generally look like this:

json

{
  "model": "<model_id>",
  "args": {
    "prompt": "Your descriptive text prompt here..."
    // ...other model-specific required or optional parameters strictly parsed from Step 1
  },
  "callback_url": "YOUR_OPTIONAL_WEBHOOK_URL"
}

Step 3: Invoke the Unified Generation Endpoint

Make an HTTP POST request to the API Endpoint. Include the Bearer token in the Authorization header.

Bash (cURL) Example

bash

response=$(curl --request POST \
  --url https://api.shortapi.ai/api/v1/job/create \
  --header "Authorization: Bearer $SHORTAPI_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "google/veo-3.1/text-to-video",
    "args": {
      "prompt": "A cinematic drone shot flying over a futuristic city at sunset"
    }
  }')
JOB_ID=$(echo "$response" | grep -o '"job_id": *"[^"]*"' | sed 's/"job_id": *//; s/"//g')

Step 4: Fetch Generation Status (Background Polling)

Use the returned job_id to poll the query API: