extract-review

Name: extract-review
Availability: InStock
Author: jadecli

Score extraction quality and generate improvement deltas from crawl results.

1|1|Updated Mar 27, 2026

Search Tags:#validation #metrics #quality #review #improvements #data-extraction

Ready to run

npx skills add https://github.com/jadecli/researchers --skill extract-review

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: extract-review
Source: https://github.com/jadecli/researchers/tree/main/claude-code/.claude/skills/extract-review
Command: npx skills add https://github.com/jadecli/researchers --skill extract-review

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Review extracted data quality after crawling to ensure completeness and consistency, reducing manual QA time.

Core Features & Use Cases

Assess completeness, structure, code blocks, links, and tables across data extracts.
Compare against previous iterations and generate improvement context.
Use case: After a crawl, run this skill to score extraction quality and guide enhancements.

Quick Start

Run the extract-review skill to score the latest crawl extractions and generate a delta in improvements/.

Frequently Asked Questions about extract-review

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

FAQPage Schema

How do I validate data extraction quality after a web crawl?▼

To validate data extraction quality after a web crawl, you must review field completeness, structure, and link integrity. Automating this extraction quality review scores the latest crawl results and identifies formatting inconsistencies across pages to reduce manual QA time.

How do I measure extraction improvements between iterative crawling workflows?▼

Measuring extraction improvements across iterative crawling workflows involves comparing current crawl metrics against previous iterations. This generates a cross-iteration improvement delta to quantify enhancements in completeness and structure.

What specific data fields are checked during an extraction quality review?▼

During an extraction quality review, specific data fields checked include titles, content, and metadata. The process also assesses formatting consistency, validates code blocks, links, and tables across extracted pages.

Can I automate data completeness checks for large scale crawling without manual QA?▼

Yes, you can automate data completeness checks for iterative crawling workflows without manual QA. Automated extraction quality reviews identify and quantify data structure issues and link integrity directly from the latest crawl results.

When should I use an automated extraction review over manual data validation?▼

You should use an automated extraction review over manual data validation when you need to verify fields, formatting, and cross-iteration improvement deltas across multiple pages, significantly reducing manual QA time for iterative crawling.