claude-code-skills-reference/fact-checker/SKILL.md

---
name: fact-checker
description: 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 Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents.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 documentation, statistics, and general factual statements.
---

# 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- "Update outdated data in this file"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:

```markdown
## 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:**

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:**

```python
# 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:**

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:

```markdown
> **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:**
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