fact-checker

Name: fact-checker
Availability: InStock
Author: moonlight-lupin

Verify factual claims by cross-checking multiple independent sources.

15|3|Updated Jul 8, 2026

Search Tags:#research #verification #cross-check #fact-check #confidence rating

Ready to run

npx skills add https://github.com/moonlight-lupin/agent-skills --skill fact-checker-moonlight-lupin

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fact-checker
Source: https://github.com/moonlight-lupin/agent-skills/tree/main/research/fact-checker
Command: npx skills add https://github.com/moonlight-lupin/agent-skills --skill fact-checker-moonlight-lupin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, lxml, BeautifulSoup, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a targeted verification pipeline for factual claims, ensuring accuracy and reliability in research and reporting.

Core Features & Use Cases

Targeted Verification: Verify specific factual claims against multiple independent sources.
Cross-Checking: Assess source independence, agreement, contradictions, and recency.
Confidence Rating: Rate the confidence level of the verification (verified, likely true, disputed, unverified, outdated).
Use Case: When you have a claim about a statistic, event, relationship, quote, or date, use this Skill to determine its factual accuracy.

Quick Start

From this skill directory, run:

python scripts/verify.py structure --claim "The Great Wall of China is the only man-made structure visible from space."

Frequently Asked Questions about fact-checker

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

FAQPage Schema

How do I verify a factual claim against multiple independent sources?▼

To verify a factual claim, you can cross-check it against multiple independent sources to assess agreement, contradictions, and recency. This Skill automates that pipeline, searching for and extracting data to ensure factual accuracy and provide a confidence rating.

How does cross-checking sources produce an accurate confidence rating?▼

Cross-checking produces a confidence rating by evaluating source independence, agreement, contradictions, and recency. Based on these factors, the verification pipeline assigns a rating of verified, likely true, disputed, unverified, or outdated.

Do I need Python and web scraping libraries to fact-check claims?▼

Yes, you need Python along with web search tools and data extraction libraries like requests, lxml, and BeautifulSoup. These dependencies are required to run the verification scripts that search and extract data for cross-checking.

What is the best way to fact-check statistics, quotes, or event dates?▼

The best way to fact-check statistics, quotes, or event dates is using a targeted verification pipeline that searches and cross-checks multiple independent sources. This approach ensures precise verification and outputs a reliable confidence rating.

Can I use this fact-checking approach for research workflows requiring precise verification?▼

Yes, this approach is specifically designed for research workflows requiring precise verification. It ensures source independence, checks for agreement and recency, and outputs a confidence rating to determine the factual accuracy of claims.