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benchclaw-stage3-real-image-cleaning

Automate cleaning of real-image datasets for BenchClaw benchmarking with Data-Juicer.

Updated May 7, 2026
Search Tags:#benchmarking#data preparation#Data-Juicer#image cleaning#real-image dataset
One-click install
npx skills add https://github.com/EurecaMoment/BenchClaw --skill benchclaw-stage3-real-image-cleaning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchclaw-stage3-real-image-cleaning
Source: https://github.com/EurecaMoment/BenchClaw/tree/main/BenchClaw/skills/benchmark-stage3-evidence-compiler/skills/real-image-evidence-compilation/subskills/cleaning
Command: npx skills add https://github.com/EurecaMoment/BenchClaw --skill benchclaw-stage3-real-image-cleaning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires data-juicer, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the need for efficient cleaning of real-image datasets in the BenchClaw benchmarking process, ensuring data quality and readiness for annotation.

Core Features & Use Cases

  • Real-Image Cleaning: Automates the cleaning process for real-image datasets.
  • Data Integrity: Ensures the integrity of media files, sample existence, and metadata completeness.
  • Use Case: Ideal for use in the BenchClaw benchmarking pipeline, where image datasets must be cleaned and prepared for annotation.

Quick Start

Run the benchclaw-stage3-real-image-cleaning skill on the dataset located at 'data_14_real_image_collection_bundle/datasets/<dataset_id>/'.

Frequently Asked Questions about benchclaw-stage3-real-image-cleaning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate real-image dataset cleaning for benchmarking?▼

Automating real-image dataset cleaning applies scripts to validate media file integrity, confirm sample existence, and complete metadata. This prepares image collections for the BenchClaw benchmarking pipeline.

Do I need Data-Juicer to clean image datasets for BenchClaw?▼

Yes, Data-Juicer is required for processing and validation. The skill depends on it to execute the automated cleaning operations and ensure media integrity for real-image datasets.

What does real-image dataset cleaning check for in an AI pipeline?▼

Real-image dataset cleaning checks for media file integrity, sample existence, and metadata completeness. Ensuring these elements guarantees the dataset is fully valid and ready for annotation tasks.

How to prepare an image collection for annotation in a benchmarking pipeline?▼

Preparing an image collection for annotation requires running cleaning scripts on the dataset directory to validate media files and complete metadata. This ensures data readiness for the BenchClaw benchmarking pipeline.

What is the best way to ensure media integrity in real-image datasets?▼

Ensuring media integrity in real-image datasets is best achieved through automated cleaning scripts that validate sample existence and metadata. This skill automates that validation for the BenchClaw benchmarking pipeline.

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