What problem does it solve? Online articles, AI announcements, and viral claims often mix verified facts with speculation, reposts, and outright errors. This Skill separates what is directly supported by primary sources from what is partially supported, unverified, or misleading, so you get a grounded verdict instead of a generic summary. ## Core Features & Use Cases - Claim-by-claim verification: Breaks articles into checkable claims (dates, quantities, attributions, technical details) and verifies each against primary sources like GitHub APIs, official docs, and government registries. - Source quality ranking: Distinguishes first-party statements, neutral reporting, reposts, and low-provenance blogs, and ranks evidence by strength for sensitive accusations. - Specialized verification patterns: Covers AI product disambiguation, privacy-policy and data-retention analysis, SaaS pricing checks, domain ownership reconciliation, app-market intelligence, and prompt-injection audits. - Use Case: When a viral article claims a GitHub project plagiarized another's architecture, the Skill checks repo creation dates, commit history, and issue threads via the GitHub API, then reports which claims are supported, which are reposted accusations, and what evidence would settle the dispute. ## Quick Start Verify the claims in this article and tell me which statements are supported by primary sources and which are unverified or misleading.