An all-in-one automation skill for the t-label deep learning platform, covering everything from deployment to model exporting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install t-label
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 t-label 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 t-label Deep Learning Automation?
The t-label skill is a specialized automation framework designed to streamline the entire deep learning lifecycle. Based on the open-source xclabel project, this Openclaw Skills contribution provides developers with a robust environment for managing sample labeling, model training, and deployment with minimal manual intervention. It excels at white-labeling the original project by removing existing copyright identifiers to create a clean, proprietary-ready foundation.\n\nFurthermore, it introduces advanced capabilities such as support for the Qwen2-VL-Plus model from Alibaba Cloud and built-in coordinate conversion for visual data. Whether you are setting up a local virtual environment or a scalable Docker container, this skill ensures a standardized and efficient machine learning operation.
t-label Deep Learning Automation Use Cases
- Automated deployment of the xclabel labeling service for internal or external teams.\n- Scaling multi-user collaborative annotation tasks for large-scale datasets.\n- Training custom deep learning models with automated log evaluation and performance tracking.\n- Integrating Alibaba Qwen2-VL-Plus for advanced vision-language processing tasks.\n- Streamlining the export of trained models into various deployment-ready formats.
How t-label Deep Learning Automation Works
- Source Acquisition: Automatically clones the xclabel repository to the local environment.\n2. Branding Cleanup: Iterates through all files to remove original author information, signatures, and repository identifiers to ensure a clean slate.\n3. Knowledge Synthesis: Analyzes the cleaned codebase, documentation, and configurations to build a comprehensive functional logic map for the agent.\n4. Automated Deployment: Configures virtual environments or Docker services, including automated account initialization and environment setup.\n5. Task Execution: Manages the lifecycle of sample imports, collaborative labeling routines, and model training parameters.\n6. Optimization & Export: Validates model performance through automated testing and exports the final weights in the requested format.
t-label Deep Learning Automation Setup
To get started with the t-label automation within Openclaw Skills, ensure you have your target environment ready (Docker or Python Venv).\n\nbash\n# Initialize the environment and deploy via Docker\nskill-deploy t-label --method docker\n\n# Or deploy via virtual environment\nskill-deploy t-label --method venv\n\n\nThe skill will automatically handle repository cloning and perform the initial account and permission setup.
t-label Deep Learning Automation Data Schema & Taxonomy
The skill organizes its operations within a structured directory hierarchy to ensure traceability and modularity:\n\n| Directory | Description |\n| :--- | :--- |\n| scripts/ | Contains automation scripts for deployment, training, and model exporting. |\n| references/ | Houses documentation, API specifications, and configuration references. |\n| assets/ | Stores template files, sample configurations, and resource assets. |\n| logs/ | Automatically generated operation logs for task backtracking and optimization. |
t-label Deep Learning Automation Advanced Features
- Integrated support for Qwen2-VL-Plus (Alibaba Tongyi Qianwen) vision-language models.\n- Automatic coordinate transformation for high-precision bounding box labeling and data conversion.\n- Multi-format model export support optimized for different deployment scenarios.\n- Comprehensive white-labeling automation to sanitize source code copyright and personal identifiers.\n- Robust troubleshooting module for automated deployment failure recovery and log analysis.
SKILL.md
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