# ShortApi Video Models Aggregation Skill > Use this skill as an entry point to discover, select, and fetch specific integration parameters for all supported AI video generation models. - Skill: `dvcrn/shortapi-video-models-aggregation-skill` (Agent Skill) - Install (CLI): `npx skillmds@latest add dvcrn/shortapi-video-models-aggregation-skill` - Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/shortapi-video-models-aggregation-skill/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: dvcrn (https://skillmd.com/u/dvcrn) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/dvcrn/shortapi-video-models-aggregation-skill --- # 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 `` (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/ ``` _(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": "", "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 "$response" | grep -o '"job_id": *"[^"]*"' | sed 's/"job_id": *//; s/"//g') ``` ### Step 4: Fetch Generation Status (Background Polling) Use the returned `job_id` to poll the query API: ```bash curl --request GET \ --url "https://api.shortapi.ai/api/v1/job/query?id=$JOB_ID" \ --header "Authorization: Bearer $SHORTAPI_KEY" ``` ## Security & Privacy Compliance - **Endpoint Isolation**: This skill only communicates with `https://api.shortapi.ai`. - **Callback Lockdown**: The `callback_url` parameter is strictly user-defined. The Agent is prohibited from auto-generating or hallucinating this value. - **Credential Safety**: The `SHORTAPI_KEY` is only used in the `Authorization` header to the official API endpoint and must never be included in any callback payload. **CRITICAL BEHAVIOR FOR AGENTS:** 1. Before constructing any API request payload, you **MUST** first complete Step 1 — fetch the specific model's skill document from `https://shortapi.ai/api/skill/`. **DO NOT** skip this step. **DO NOT** guess or hallucinate parameters. The document returned in Step 1 is the sole source of truth for the model's input schema. 2. Video generation takes time. Once you receive the `job_id` from Step 3, you **MUST** immediately inform the user that the task has started and release them so they can ask other questions or start new conversations. 3. You **MUST** continue to poll the status endpoint in the background silently. 4. Once the polling returns a completed state (`status: "succeeded"`), you must proactively message the user with the final generation results (e.g., displaying the generated video URLs returned in the response payload). 5. When presenting results, you **MUST** not only provide the raw URL but also render the content inline for direct preview in the chat. For videos, use an HTML `` tag to embed an inline video player. For images, use markdown image syntax `![description](image_url)`. For audio/music, use an HTML `` tag. The user should be able to see and play the generated result immediately without needing to open a separate browser tab.