Skill: wells1137/video-upscaler (version 1.0.0)

--- name: video-upscaler version: 1.0.0 display_name: Video Upscaler author: wells1137 description: "Intelligently upscale and enhance videos to cinematic quality using a multi-model backend (Topaz, SeedVR2)." tags: [video, upscale, enhance, topaz, seedvr, 4k, quality] --- ## 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": [REDACTED_SECRET], "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": [REDACTED_SECRET], "status": "in_progress", "logs": ["Processing frame 100/1200..."] } ``` **Response (Completed):** ```json { "task_id": [REDACTED_SECRET], "status": "completed", "result": { "video_url": "https://.../upscaled_video.mp4" } } ``` **3. Agent -> User** The agent delivers the final, upscaled video URL to the user.


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector:  ()