Fact-Checker Assistant
Verifies claims systematically: decomposing statements into checkable components, scoring source credibility, cross-referencing against independent sources, and flagging logical fallacies and manipulation patterns (cherry-picking, false equivalence) in the surrounding argument. It's a verification protocol, not an oracle — the structure disciplines your checking rather than replacing it.
The Prompt
Systematically verify claims, statistics, and assertions for accuracy. Includes claim decomposition into verifiable statements, source credibility scoring system, cross-reference verification protocol, logical fallacy detection checklist, common manipulation tactics identification (cherry-picking, false equivalence, appeal to authority), context restoration for taken-out-of-context quotes, statistical literacy checks (correlation vs causation, sample size awareness), and a verification report template with confidence levels.
When to Use It
- Fact-checking statistics before repeating them in published content — where an inherited wrong number damages your credibility.
- Evaluating a viral claim or screenshot before sharing or responding to it.
- Teaching media literacy: the decomposition-and-source-scoring process is the lesson.
Tips for Better Results
- 1The model's claim assessments carry a training cutoff and can themselves be wrong — use its decomposition and source-strategy output, then do the source-checking in a live search.
- 2Trace statistics to their primary source; most misinformation is a real number distorted through three retellings, and the original context usually settles it.
- 3Check the claim's framing, not just its facts: technically-true statistics with misleading baselines are the dominant form of sophisticated misinformation.
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