An automated AI-driven tool for cleaning videos by removing unwanted text overlays and watermarks via the WeShop OpenAPI.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-text-remover-from-video-openapi-skill
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-text-remover-from-video-openapi-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AI Video Text Remover?
The AI Video Text Remover is a specialized agent designed to enhance video quality by eliminating distracting text, captions, or watermarks. By integrating with the WeShop OpenAPI, this skill allows developers and AI agents to programmatically process video files and restore their visual integrity without manual editing.
As part of the Openclaw Skills ecosystem, it provides a standardized way to handle complex computer vision tasks. It utilizes advanced segmentation and inpainting techniques to detect text regions automatically and fill them seamlessly, making it an essential tool for content creators and video editors looking to repurpose media.
AI Video Text Remover Use Cases
- Removing burned-in subtitles or captions from legacy video content.
- Cleaning up watermarks from stock footage or internal demos.
- Preparing social media videos for rebranding by removing old text overlays.
- Automating video post-production workflows within AI-driven pipelines.
How AI Video Text Remover Works
- Initialization: The agent receives a video URL and checks for a valid WESHOP_API_KEY in the environment.
- Job Submission: A POST request is sent to the WeShop OpenAPI to initiate the text removal process.
- Processing: The AI engine analyzes the video frames, identifying text via the autoDetect watermark selection mode.
- Polling: The system monitors the execution ID, tracking states from Pending and Segmenting to Running.
- Completion: Once the status reaches Success, the final cleaned video URL is retrieved from the result payload.
AI Video Text Remover Setup
To integrate this into your workflow, ensure the environment variable is set:
export WESHOP_API_KEY='your_api_key_here'
Installation requires access to the WeShop OpenAPI endpoint at [https://openapi.weshop.ai](https://openapi.weshop.ai). Ensure your AI agent is configured to pass the raw API key in the Authorization header without the Bearer prefix.
AI Video Text Remover Data Schema & Taxonomy
The skill utilizes a structured JSON request and response format to manage video assets:
| Parameter | Type | Description |
|---|---|---|
input.videos |
Array | A list containing the source video URL (max 1). |
watermarkSelectType |
String | Set to autoDetect for automated text recognition. |
executionId |
String | Unique identifier used to poll for the processed output. |
result.video |
String | The final URL of the video with text removed. |
AI Video Text Remover Advanced Features
- Auto-Detection Mode: Automatically identifies text regions without requiring manual coordinate mapping.
- Status Lifecycle Tracking: Detailed execution states including Segmenting and Running for real-time progress updates.
- Asset Management: Support for uploading local images to reusable URLs via the assets endpoint.
- Seamless Openclaw Skills Integration: Designed to work within multi-agent protocols for complex media automation.
SKILL.md
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