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• "Update outdated data in this file"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:

Fact-checking Progress:

• Step 1: Identify factual claims
• Step 2: Search authoritative sources
• Step 3: Compare claims against sources
• Step 4: Generate correction report
• Step 5: Apply corrections with user approval

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Claude 3.5 Sonnet: 200K tokens Claude Sonnet 4.5: 200K tokens ❌ Outdated model name platform.claude.com/docs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• ⚠️ Outdated - claim was true but superseded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:

Fact-Check Report

Summary

• Total claims checked: X
• Accurate: Y
• Issues found: Z

Issues Requiring Correction

Issue 1: Outdated AI Model Reference

Location: Line 77-80 in docs/file.md Current claim: "Claude 3.5 Sonnet: 200K tokens" Correction: "Claude Sonnet 4.5: 200K tokens" Source: https://platform.claude.com/docs/en/build-with-claude/context-windows Rationale: Claude 3.5 Sonnet has been superseded by Claude Sonnet 4.5 (released Sept 2025)

Issue 2: Incorrect Context Window

Location: Line 79 in docs/file.md Current claim: "GPT-4o: 128K tokens" Correction: "GPT-5.2: 400K tokens" Source: https://openai.com/index/introducing-gpt-5-2/ Rationale: 128K was output limit; context window is 400K. Model also updated to GPT-5.2

Step 5: Apply corrections with user approval

Before making changes:

• Show the correction report to the user

• Wait for explicit approval: "Should I apply these corrections?"

• Only proceed after confirmation

When applying corrections:

Use Edit tool to update document

Example:

Edit( file_path="docs/03-写作规范/AI辅助写书方法论.md", old_string="- Claude 3.5 Sonnet: 200K tokens（约 15 万汉字）", new_string="- Claude Sonnet 4.5: 200K tokens（约 15 万汉字）" )

After corrections:

• Verify all edits were applied successfully

• Note the correction summary (e.g., "Updated 4 claims in section 2.1")

• 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:

• Product official pages (highest authority)

• API documentation

• Official blog announcements

• 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:

• Prioritize most recent official documentation

• Note the discrepancy in the report

• Present both sources to the user

• Recommend contacting vendor if critical

When no source found:

• Mark as ❓ Unverifiable

• Suggest alternative phrasing: "According to [Source] as of [Date]..."

• 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:

注：具体上下文窗口以模型官方文档为准，本书写作时使用 Claude Sonnet 4.5 为主要工具。

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:

• Identify claims: Claude 3.5 Sonnet (200K), GPT-4o (128K), Gemini 1.5 Pro (2M)

• Search official docs for current models

• Find: Claude Sonnet 4.5, GPT-5.2, Gemini 3 Pro

• Generate report showing discrepancies

• Apply corrections after approval

Example 2: Statistical data verification

User request: "Verify the benchmark scores in chapter 5"

Process:

• Extract numerical claims

• Search for official benchmark publications

• Compare reported vs. source values

• Flag any discrepancies with source links

• Update with verified figures

Example 3: Version number validation

User request: "Check if these library versions are still current"

Process:

• List all version numbers mentioned

• Check package registries (npm, PyPI, etc.)

• Identify outdated versions

• Suggest updates with changelog references

• 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