fernandezbaptiste/fact-checker

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• "Update outdated data in this file"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| Claude 3.5 Sonnet: 200K tokens | Claude Sonnet 4.5: 200K tokens | ❌ Outdated model name | platform.claude.com/docs | | GPT-4o: 128K tokens | GPT-5.2: 400K tokens | ❌ Incorrect version & spec | openai.com/index/gpt-5-2 |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• ⚠️ Outdated - claim was true but superseded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:

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:

# 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:

> **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