Fact Checking

Fact-Checking

This skill enables an AI agent to systematically verify claims and statements. Rather than offering a simple true/false judgment, the agent extracts discrete checkable claims from the input, identifies authoritative sources for each, cross-references evidence, and produces a structured verdict with a confidence score and supporting reasoning. The approach is designed to handle everything from single factual assertions to full articles containing dozens of claims.

Workflow

1. Extract Claims: Parse the input text and isolate individual, verifiable assertions. Each claim should be a single, self-contained statement that can be independently checked. Discard opinions, subjective judgments, and unfalsifiable statements, but note them as "not checkable" in the output.

2. Classify Claim Types: Categorize each claim by type — statistical (involves numbers or data), historical (references past events), scientific (references research findings), definitional (defines a term), or attribution (attributes a statement to a person or organization). The category guides where to look for verification.

3. Identify Authoritative Sources: For each claim, determine the most appropriate verification sources. Use primary sources whenever possible: official datasets for statistics, peer-reviewed papers for scientific claims, archived transcripts for quotations, and government records for legal or policy assertions. Supplement with reputable secondary sources like established fact-checking organizations (Snopes, PolitiFact, Full Fact).

4. Cross-Reference and Evaluate Evidence: Check each claim against at least two independent sources. Note whether sources corroborate, partially support, or contradict the claim. Assess source credibility by considering authority, recency, methodology, and potential bias.

5. Assign Verdicts and Confidence Scores: For each claim, assign a verdict from the scale: True, Mostly True, Half True, Mostly False, False, or Unverifiable. Accompany each verdict with a confidence score (0.0-1.0) reflecting the strength and consistency of available evidence, and a brief justification.

6. Compile the Fact-Check Report: Present findings in a structured format: list each claim, its verdict, confidence score, supporting evidence, and source links. Include an overall assessment summarizing the accuracy of the original text.

Usage

Provide the agent with a statement, paragraph, or full article to fact-check. You can optionally specify which claims to prioritize or request a particular output format.

Fact-check the following statement:
"Python is the most popular programming language in 2025 according to the TIOBE Index,
and it was created by James Gosling at Sun Microsystems in 1991."

Examples

Example 1: Fact-Checking a Technical Blog Post

User Request:

Fact-check the key claims in this blog excerpt: "Rust has zero runtime overhead compared to C, making it the fastest systems language available. The Linux kernel adopted Rust as its primary development language in 2024, replacing C entirely. According to Stack Overflow's 2024 survey, Rust has been the most admired language for nine consecutive years."

Extracted Claims:

Fact-Check Results:

Overall Assessment: The blog post contains a mix of approximately true technical claims and significantly exaggerated statements about Rust's role in the Linux kernel. Readers should be cautious about the kernel-related claims.

Example 2: Fact-Checking Statistical Claims

User Request:

Verify: "Global AI spending reached $200 billion in 2024, with 75% of Fortune 500 companies deploying generative AI in production. The average ROI for enterprise AI projects is 3.5x within the first year."

Extracted Claims:

Fact-Check Results:

Overall Assessment: The spending figure is approximately correct, the adoption statistic is in the right ballpark but imprecise, and the ROI claim lacks credible sourcing and should not be cited without qualification.

Best Practices

• Isolate each claim before verifying. Complex sentences often bundle multiple assertions. Splitting them ensures nothing is overlooked and verdicts remain precise.
• Prioritize primary sources over secondary reporting. A news article saying "a study found X" is less reliable than reading the study itself. Always trace claims to their origin.
• Account for context and framing. A technically true number can be misleading if taken out of context. Note when a claim is true but presented in a way that implies something false.
• Use the confidence score honestly. A score of 0.5 is not a failure — it reflects genuine ambiguity. Overconfident verdicts erode trust more than honest uncertainty.
• Check the date of the claim and the source. A claim that was true in 2020 may be false in 2025. Always verify that the evidence is temporally relevant to the assertion.
• Distinguish between "false" and "unverifiable." If no credible evidence exists either way, the verdict should be "Unverifiable," not "False."

Edge Cases

• Claims about the future: Predictions ("AI will replace 50% of jobs by 2030") cannot be fact-checked against evidence. Label them as "Predictive — not verifiable" and note the credibility of the source making the prediction.
• Rapidly changing statistics: If the claim involves a metric that updates frequently (e.g., cryptocurrency prices, COVID case counts), note the date the claim refers to and the date of verification, since the answer may differ.
• Satirical or hyperbolic content: If the source material is clearly satirical or uses deliberate exaggeration for rhetorical effect, note this context rather than issuing a literal "False" verdict.
• Claims with no authoritative source: Some niche or proprietary claims (e.g., internal company metrics) may have no publicly verifiable source. Label these "Unverifiable — no public source" and recommend the user request documentation from the claimant.
• Ambiguous wording: When a claim can be interpreted multiple ways (e.g., "most popular" could mean by usage, by survey, or by downloads), evaluate the most reasonable interpretation and note the ambiguity.

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