Fact Checker Skill for Claude

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• "Update outdated data in this file"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| Claude 3.5 Sonnet: 200K tokens | Claude Sonnet 4.5: 200K tokens | ❌ Outdated model name | platform.claude.com/docs |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• ⚠️ Outdated - claim was true but superseded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