kg-extract

Extract validated knowledge graph claims from scientific papers into KG JSON.

Updated Feb 24, 2026
One-click install
npx skills add https://github.com/mcleanT/AutoReview --skill kg-extract
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kg-extract
Source: https://github.com/mcleanT/AutoReview/tree/main/.claude/skills/kg-extract
Command: npx skills add https://github.com/mcleanT/AutoReview --skill kg-extract

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates turning unstructured paper text into a validated knowledge graph extraction, removing manual claim curation and reducing inconsistent or invalid KG outputs.

Core Features & Use Cases

  • Single-paper extraction: fetches or reads a DOI, URL, PDF, or text and runs the v4 KG extraction prompt to produce structured JSON claims and evidence.
  • Corpus batch extraction: prepares and launches batch jobs against the Anthropic Batches API with mandatory user confirmation, polls results, applies coercion maps, validates with Pydantic, and writes per-paper outputs.
  • Robust post-processing: applies predicate coercion, migrates legacy evidence formats, enforces schema validation, and reports claim/evidence counts and coercion actions.

Quick Start

Use the kg-extract skill to extract knowledge graph claims from the paper with DOI 10.1038/example and return the validated KG JSON and a summary of claims and evidence.

Frequently Asked Questions about kg-extract

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

FAQPage Schema
How do I extract knowledge graph claims from scientific papers?▼

You can extract knowledge graph claims from a scientific paper by providing its DOI, URL, PDF file path, or paper index to trigger the v4 extraction pipeline and output validated KG JSON.

Can I use Pydantic schema validation for knowledge graph extraction?▼

Yes, Pydantic schema validation is applied during knowledge graph extraction to enforce data integrity, alongside predicate coercion mapping and evidence_links migration for robust post-processing.

Does the Anthropic Batches API support corpus batch extraction for multiple papers?▼

Yes, the Anthropic Batches API supports confirmed corpus batch extraction by preparing, launching, and polling batch jobs to process multiple scientific papers and write per-paper extraction files.

What is the best way to automate KG JSON extraction from a DOI?▼

The best way to automate KG JSON extraction from a DOI is to use the single-paper extraction feature, which fetches the document and applies the v4 prompt template to generate structured claims.

Why does predicate coercion mapping matter for knowledge graph extraction?▼

Predicate coercion mapping matters for knowledge graph extraction because it standardizes extracted relationships, ensuring consistent and valid KG JSON outputs by normalizing varied predicate terms.