data-annotation

What This Skill Does

The Data Annotation Skill is a comprehensive, AI-driven tool designed to streamline the lifecycle of data preparation tasks. Whether you are dealing with image sets, video footage, or raw text documents, this skill provides a structured framework for content understanding, classification, and metadata generation. It leverages advanced Vision-Language (VL) models to perform automated labeling and offers a centralized Web UI for human-in-the-loop verification, editing, and final dataset exportation. By integrating seamlessly into the OpenClaw workflow, it transforms manual, tedious labeling into a managed, plan-driven process.

Installation

To integrate this capability into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/aowind/sjht-data-annotation Ensure that your environment has the necessary dependencies like python-docx and ffmpeg installed to support document parsing and video frame extraction respectively.

Use Cases

• Computer Vision Dataset Preparation: Automatically tag objects in images or videos for machine learning model training.
• Document Digitization: Extract structured data from unstructured Word documents or image-based reports.
• Content Moderation: Batch process media files to identify and label sensitive or specific content categories.
• Workflow Automation: Large-scale data labeling projects that require consistent schema adherence and systematic progress tracking.

Example Prompts

1. "I have a folder of 500 product photos in /data/inventory; please analyze them and label each with its category based on the requirements in /docs/labeling_rules.docx."
2. "Start a new data annotation project for the surveillance videos located in /mnt/storage/videos, following the schema defined in the project dashboard."
3. "Please review the current progress of my ongoing dataset generation task and update the plan.json file with the newly processed items."

Tips & Limitations

• Plan-Driven Priority: Never attempt to process your entire dataset in one single command. Always utilize the plan.json mechanism to track progress and prevent timeout errors during long-running tasks.
• Systematic Processing: The skill is designed for sequential processing (1 item at a time). This ensures that if a model call fails or a network timeout occurs, you can resume exactly where you left off without losing data.
• Resource Management: For video data, ensure you perform frame extraction as a preprocessing step using ffmpeg to keep your LLM/VL API tokens efficient and cost-effective.
• Verification: While the AI provides high-accuracy labels, always use the built-in Web UI to conduct final quality assurance before deploying the dataset for production use.