fact-checker

name: fact-checker description: Verify claims in a draft before publication. Use this skill when reviewing essays, posts, or articles for factual accuracy, checking code examples work correctly, validating statistics and sources, or verifying quotes and attributions. Produces a structured fact-check report with verdicts for each claim.

Note (roadmap). Slated to be rebuilt as a dynamic, model-driven workflow rather than a static checklist — keep the intent (a verdict per claim), generalize the mechanics.

Verify technical, statistical, and attribution claims in a draft.

References:

references/claim-categories.md — Claim types and verification methods
references/report-format.md — Report template and examples

Verification Workflow

Step 1: Extract Claims

Scan the content and extract all verifiable claims:

Technical claims — Code, APIs, versions, tool capabilities
Statistical claims — Numbers, percentages, study references
Attribution claims — Quotes, sources, links

See references/claim-categories.md for identification signals.

Step 2: Prioritize

Focus verification effort on high-impact claims:

Priority	Examples
High	Code readers will copy, statistics supporting arguments, named quotes
Medium	Version numbers, tool comparisons
Low	Hyperbolic rhetoric, subjective assessments

Step 3: Verify Each Claim

Technical claims:

1. Code snippets → Execute to verify they run
2. API behavior → Web search for official docs
3. Version claims → Check release notes

Statistical claims:

1. Find original source via web search
2. Verify the number matches
3. Check if data is current

Attribution claims:

1. Web search for original quote/source
2. Verify link validity
3. Confirm attribution accuracy

Step 4: Flag for Human Review

Mark claims that cannot be automatically verified:

Insider knowledge or personal anecdotes
Future predictions
Unpublished/internal data
Controversial interpretations

Step 5: Generate Report

Produce a structured report using the template in references/report-format.md.

Verdicts:

VERIFIED — All claims check out
ISSUES FOUND — Corrections needed before publishing
NEEDS HUMAN REVIEW — Editor must decide on flagged items

Quick Check vs Deep Verification

Scope	Use Case	Approach
Quick check	Pre-publication review	This skill: scan, verify obvious claims, flag concerns
Deep verification	Investigative piece, controversial topic	Delegate to a research subagent for extended verification

Code Verification

For code blocks, attempt execution when possible:

# Run code in sandbox
# Capture: success/failure, output, errors
# Report: which blocks run, which fail

Report format for code:

Code Block	Location	Result
API example	Section 2	✓ Runs
Data pipeline	Section 4	✗ Error: missing import

Common Issues to Watch For

Issue	Detection	Recommendation
Outdated package names	Package not found on pip/npm	Check current package name
Deprecated API syntax	Code runs but with warnings	Update to current syntax
Broken links	404 or redirect to unrelated page	Find updated URL or remove
Misattributed quotes	Original source says different	Correct attribution or rephrase
Stale statistics	Data >2 years old	Find current data or note date
AI-fabricated quotes	Quote sounds plausible but no source confirms wording	Verify against the literal source, not just URL/date

Output Format

Always produce a report following the template in references/report-format.md.

Minimum report contents:

Overall verdict (VERIFIED / ISSUES FOUND / NEEDS HUMAN REVIEW)
Summary of findings
List of claims checked with individual verdicts
Code execution results (if applicable)
Specific recommendations for fixes