Can I use this for technical code assertions?

Yes, it is specifically designed to check technical claims such as performance benchmarks, deprecation statuses, and library comparisons before you commit them to documentation.

What is adversarial search in this context?

Adversarial search is a methodology that forces the AI to actively look for evidence that proves a claim wrong, not just evidence that supports it, ensuring the final verdict is balanced and unbiased.

What kind of verdicts does the skill produce?

The skill returns one of five statuses: Supported, Partially Supported, Disputed, Unsupported, or Insufficient Evidence, accompanied by a High/Medium/Low confidence rating.

How does the skill handle source reliability?

It uses a four-tier ranking system: Tier 1 includes peer-reviewed journals and official stats; Tier 2 covers established news; Tier 3 includes expert blogs; and Tier 4 (unreliable sources) is discarded unless no other data exists.

Fact-Check & Claim Verification

Name: Fact-Check & Claim Verification
Author: n24q02m

byn24q02m

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Learning & Documentation

Verifies assertions using adversarial search to find both supporting and contradicting evidence for balanced decision-making.

The Fact-Check skill empowers Claude to perform rigorous, objective investigations of any claim by intentionally seeking out evidence from both sides of an argument. By decomposing complex assertions into verifiable sub-claims and categorizing sources into reliability tiers, it systematically eliminates confirmation bias and provides a structured verdict with a clear confidence score. This is an essential tool for ensuring technical accuracy in documentation, validating architectural trade-offs, or vetting external data before integration, ensuring that your codebase and its documentation remain grounded in verified fact.

Key Features

01Adversarial search methodology to proactively find contradicting evidence

022 GitHub stars

03Structured verdict reporting with confidence levels and nuanced caveats

04Automated claim decomposition into falsifiable statements

05Tiered source quality assessment (Peer-reviewed journals vs. blog posts)

06Support for academic, technical, historical, and statistical domain verification

Use Cases

01Fact-checking information intended for code comments, documentation, or internal wikis

02Validating technical performance claims or library comparisons before choosing a stack

03Verifying user-provided assumptions or statistics during the software requirements phase

Key Features

01Adversarial search methodology to proactively find contradicting evidence

022 GitHub stars

03Structured verdict reporting with confidence levels and nuanced caveats

04Automated claim decomposition into falsifiable statements

05Tiered source quality assessment (Peer-reviewed journals vs. blog posts)

06Support for academic, technical, historical, and statistical domain verification

Use Cases

01Fact-checking information intended for code comments, documentation, or internal wikis

02Validating technical performance claims or library comparisons before choosing a stack

03Verifying user-provided assumptions or statistics during the software requirements phase