Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation. Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents. Supports AI model specs, technical doc

Fact Checker Verify factual claims in documents and propose corrections backed by authoritative sources. When to use Trigger when users request: "Fact check this document" "Verify these AI model specifications" "Check if this information is still accurate" "Update outdated data in this file" "Validate the claims in this section" Workflow Copy this checklist to track progress: Step 1: Identify factual claims Scan the document for verifiable statements: Target claim types: Technical specifications (context windows, pricing, features) Version numbers and release dates Statistical data and metrics API capabilities and limitations Benchmark scores and performance data Skip subjective content: Opinions and recommendations Explanatory prose Tutorial instructions Architectural discussions Step 2: Search authoritative sources For each claim, search official sources: AI models: Official announcement pages (anthropic.com/news, openai.com/index, blog.google) API documentation (platform.claude.com/docs, platform.openai.com/docs) Developer guides and release notes Technical libraries: Official documentation sites GitHub repositories (releases, README) Package registries (npm, PyPI, crates.io) General claims: Academic papers and research Government statistics Industry standards bodies Search strategy: Use model names + specification (e.g., "Claude Opus 4.5 context window") Include current year for recent information Verify from multiple sources when possible Step 3: Compare claims against sources Create a comparison table: Claim in Document Source Information Status Authoritative Source Claude 3.5 Sonnet: 200K tokens Claude Sonnet 4.5: 200K tokens ❌ Outdated model name platform.claude.com/docs GPT 4o: 128K tokens GPT 5.2: 400K tokens ❌ Incorrect version & spec openai.com/index/gpt 5 2 Status codes: ✅ Accurate claim matches sources ❌ Incorrect claim contradicts sources ⚠️ Outdated claim was true but superseded ❓ Unverifiable no authoritative source found Step 4: Generate correction report Present findings in structured format: Step 5: Apply corrections with user approval Before making changes: 1. Show the correction report to the user 2. Wait for explicit approval: "Should I apply these corrections?" 3. Only proceed after confirmation When applying corrections: After corrections: 1. Verify all edits were applied successfully 2. Note the correction summary (e.g., "Updated 4 claims in section 2.1") 3. Remind user to commit changes Search best practices Query construction Good queries (specific, current): "Claude Opus 4.5 context window 2026" "GPT 5.2 official release announcement" "Gemini 3 Pro token limit specifications" Poor queries (vague, generic): "Claude context" "AI models" "Latest version" Source evaluation Prefer official sources: 1. Product official pages (highest authority) 2. API documentation 3. Official blog announcements 4. GitHub releases (for open source) Use with caution: Third party aggregators (llm stats.com, etc.) verify against official sources Blog posts and articles cross reference claims Social media only for announcements, verify elsewhere Avoid: Outdated documentation Unofficial wikis without citations Speculation and rumors Handling ambiguity When sources conflict: 1. Prioritize most recent official documentation 2. Note the discrepancy in the report 3. Present both sources to the user 4. Recommend contacting vendor if critical When no source found: 1. Mark as ❓ Unverifiable 2. Suggest alternative phrasing: "According to [Source] as of [Date]..." 3. Recommend adding qualification: "approximately", "reported as" Special considerations Time sensitive information Always include temporal context: Good corrections: "截至 2026 年 1 月" (As of January 2026) "Claude Sonnet 4.5 (released September 2025)" Poor corrections: "Latest version" (becomes outdated) "Current model" (ambiguous timeframe) Numerical precision Match precision to source: Source says: "approximately 1 million tokens" Write: "1M tokens (approximately)" Source says: "200,000 token context window" Write: "200K tokens" (exact) Citation format Include citations in corrections: Link to sources when possible. Examples Example 1: Technical specification update User request: "Fact check the AI model context windows in section 2.1" Process: 1. Identify claims: Claude 3.5 Sonnet (200K), GPT 4o (128K), Gemini 1.5 Pro (2M) 2. Search official docs for current models 3. Find: Claude Sonnet 4.5, GPT 5.2, Gemini 3 Pro 4. Generate report showing discrepancies 5. Apply corrections after approval Example 2: Statistical data verification User request: "Verify the benchmark scores in chapter 5" Process: 1. Extract numerical claims 2. Search for official benchmark publications 3. Compare reported vs. source values 4. Flag any discrepancies with source links 5. Update with verified figures Example 3: Version number validation User request: "Check if these library versions are still current" Process: 1. List all version numbers mentioned 2. Check package registries (npm, PyPI, etc.) 3. Identify outdated versions 4. Suggest updates with changelog references 5. Update after user confirms Quality checklist Before completing fact check: [ ] All factual claims identified and categorized [ ] Each claim verified against official sources [ ] Sources are authoritative and current [ ] Correction report is clear and actionable [ ] Temporal context included where relevant [ ] User approval obtained before changes [ ] All edits verified successful [ ] Summary provided to user Limitations This skill cannot: Verify subjective opinions or judgments Access paywalled or restricted sources Determine "truth" in disputed claims Predict future specifications or features For such cases: Note the limitation in the report Suggest qualification language Recommend user research or expert consultation Next Step: Export Verified Content After fact checking, suggest exporting the verified document:

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