How do I install the Video Upscaler skill?

Run npx skillmds@latest add majiayu000/video-upscaler in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Video Upscaler safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Video Upscaler?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Video Upscaler free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Video Upscaler?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Video Upscaler by majiayu000 — Agent Skill

Name: Video Upscaler
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

Summary

The Video Upscaler skill provides professional-grade video quality enhancement by leveraging a powerful, multi-model backend. It intelligently selects the best AI model (Topaz, SeedVR2, etc.) based on the user-defined profile to achieve optimal results, transforming low-resolution or noisy footage into crisp, cinematic-quality video.

This skill abstracts away the complexity of choosing and configuring different AI upscaling models. Instead of dealing with dozens of technical parameters, the user simply chooses a high-level goal, and the skill handles the rest.

Features

Multi-Model Backend: Dynamically routes requests to the best model for the job (Topaz, SeedVR2, etc.) via a unified API.
Profile-Based Enhancement: Offers a range of pre-configured profiles for common use cases, from standard 2x upscaling to 4K cinematic conversion and 60 FPS frame boosting.
Asynchronous by Design: Handles long-running video processing jobs without blocking the agent.
Simple Interface: Requires only a video URL and a profile name to start.

How It Works

The skill operates in a simple, two-step asynchronous workflow:

Submit Job: The agent calls the /upscale endpoint with a video URL and a profile name. The service validates the request, selects the appropriate AI model, and submits the job to the fal.ai backend. It immediately returns a task_id.
Poll for Status: The agent uses the task_id to periodically call the /status/{task_id} endpoint. The status will be queued, in_progress, or completed. Once completed, the response will contain the URL of the final, upscaled video.

Available Profiles

Profile Name	Description
`standard_x2`	2x upscale using Topaz Proteus v4. Best all-around quality for live-action footage.
`cinema_4k`	Upscale to 4K (2160p) using SeedVR2. Best for cinematic content requiring temporal consistency.
`frame_boost_60fps`	2x upscale + frame interpolation to 60 FPS using Topaz Apollo v8. Best for sports and action.
`ai_video_enhance`	4x upscale using Topaz. Best for AI-generated videos that need resolution boosting.
`web_optimized`	Upscale to 1080p with web-optimized H264 output. Best for social media and web publishing.

End-to-End Example

User Request: "Enhance this video to 4K cinematic quality: [video_url]"

1. Agent -> Skill (Submit Job)

The agent identifies the user's intent and calls the /upscale endpoint with the cinema_4k profile.

curl -X POST http://<your_backend_url>/upscale \
  -H "Content-Type: application/json" \
  -d 
    "video_url": "[video_url]",
    "profile": "cinema_4k"
  }

Response:

{
  "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "model_used": "fal-ai/seedvr/upscale/video",
  "profile": "cinema_4k"
}

2. Agent -> Skill (Poll for Status)

The agent waits and then polls the status endpoint.

curl http://<your_backend_url>/status/a1b2c3d4-e5f6-7890-1234-567890abcdef

Response (In Progress):

{
  "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "status": "in_progress",
  "logs": ["Processing frame 100/1200..."]
}

Response (Completed):

{
  "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "status": "completed",
  "result": {
    "video_url": "https://.../upscaled_video.mp4"
  }
}

3. Agent -> User

The agent delivers the final, upscaled video URL to the user.

---name: video-upscalerdescription: Intelligently upscale and enhance videos to cinematic quality using a multi-model backend (Topaz, SeedVR2).--- ## Summary The **Video Upscaler** skill provides professional-grade video quality enhancement by leveraging a powerful, multi-model backend. It intelligently selects the best AI model (Topaz, SeedVR2, etc.) based on the user-defined profile to achieve optimal results, transforming low-resolution or noisy footage into crisp, cinematic-quality video. This skill abstracts away the complexity of choosing and configuring different AI upscaling models. Instead of dealing with dozens of technical parameters, the user simply chooses a high-level goal, and the skill handles the rest. ## Features - **Multi-Model Backend**: Dynamically routes requests to the best model for the job (Topaz, SeedVR2, etc.) via a unified API. - **Profile-Based Enhancement**: Offers a range of pre-configured profiles for common use cases, from standard 2x upscaling to 4K cinematic conversion and 60 FPS frame boosting. - **Asynchronous by Design**: Handles long-running video processing jobs without blocking the agent. - **Simple Interface**: Requires only a video URL and a profile name to start. ## How It Works The skill operates in a simple, two-step asynchronous workflow: 1. **Submit Job**: The agent calls the `/upscale` endpoint with a video URL and a profile name. The service validates the request, selects the appropriate AI model, and submits the job to the `fal.ai` backend. It immediately returns a `task_id`. 2. **Poll for Status**: The agent uses the `task_id` to periodically call the `/status/{task_id}` endpoint. The status will be `queued`, `in_progress`, or `completed`. Once completed, the response will contain the URL of the final, upscaled video. ## Available Profiles | Profile Name | Description | | :--- | :--- | | `standard_x2` | **2x upscale** using Topaz Proteus v4. Best all-around quality for live-action footage. | | `cinema_4k` | Upscale to **4K (2160p)** using SeedVR2. Best for cinematic content requiring temporal consistency. | | `frame_boost_60fps` | 2x upscale + **frame interpolation to 60 FPS** using Topaz Apollo v8. Best for sports and action. | | `ai_video_enhance` | **4x upscale** using Topaz. Best for AI-generated videos that need resolution boosting. | | `web_optimized` | Upscale to **1080p** with web-optimized H264 output. Best for social media and web publishing. | ## End-to-End Example **User Request:** "Enhance this video to 4K cinematic quality: [video_url]" **1. Agent -> Skill (Submit Job)** The agent identifies the user's intent and calls the `/upscale` endpoint with the `cinema_4k` profile. ```bash curl -X POST http://<your_backend_url>/upscale \ -H "Content-Type: application/json" \ -d "video_url": "[video_url]", "profile": "cinema_4k" } ``` **Response:** ```json { "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef", "model_used": "fal-ai/seedvr/upscale/video", "profile": "cinema_4k" } ``` **2. Agent -> Skill (Poll for Status)** The agent waits and then polls the status endpoint. ```bash curl http://<your_backend_url>/status/a1b2c3d4-e5f6-7890-1234-567890abcdef ``` **Response (In Progress):** ```json { "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef", "status": "in_progress", "logs": ["Processing frame 100/1200..."] } ``` **Response (Completed):** ```json { "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef", "status": "completed", "result": { "video_url": "https://.../upscaled_video.mp4" } } ``` **3. Agent -> User** The agent delivers the final, upscaled video URL to the user.

Frequently asked questions

Video Upscaler