feat: add fact-checker skill v1.0.0
Add comprehensive fact-checking skill that verifies claims using web search and official sources, then proposes corrections with user confirmation. Features: - 5-step workflow: identify → search → compare → report → apply - Supports AI model specs, technical docs, statistics, general facts - Source evaluation framework prioritizing official documentation - Auto-correction with mandatory user approval gates - Temporal context to prevent information decay Real-world usage: - Successfully updated AI model specs (Claude, GPT, Gemini) - Corrected outdated version numbers and context windows - Added temporal markers for time-sensitive information Marketplace updates: - Bumped version to 1.19.0 - Added fact-checker to plugins list - Updated metadata description 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
daymade
@@ -5,8 +5,8 @@
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"email": "daymadev89@gmail.com"
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},
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"metadata": {
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"description": "Professional Claude Code skills for GitHub operations, document conversion, diagram generation, statusline customization, Teams communication, repomix utilities, skill creation, CLI demo generation, LLM icon access, Cloudflare troubleshooting, UI design system extraction, professional presentation creation, YouTube video downloading, secure repomix packaging, ASR transcription correction, video comparison quality analysis, comprehensive QA testing infrastructure, prompt optimization with EARS methodology, session history recovery, documentation cleanup, PDF generation with Chinese font support, CLAUDE.md progressive disclosure optimization, CCPM skill registry search and management, Promptfoo LLM evaluation framework, and iOS app development with XcodeGen and SwiftUI",
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"version": "1.18.1",
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"description": "Professional Claude Code skills for GitHub operations, document conversion, diagram generation, statusline customization, Teams communication, repomix utilities, skill creation, CLI demo generation, LLM icon access, Cloudflare troubleshooting, UI design system extraction, professional presentation creation, YouTube video downloading, secure repomix packaging, ASR transcription correction, video comparison quality analysis, comprehensive QA testing infrastructure, prompt optimization with EARS methodology, session history recovery, documentation cleanup, PDF generation with Chinese font support, CLAUDE.md progressive disclosure optimization, CCPM skill registry search and management, Promptfoo LLM evaluation framework, iOS app development with XcodeGen and SwiftUI, and fact-checking with automated corrections",
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"version": "1.19.0",
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"homepage": "https://github.com/daymade/claude-code-skills"
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},
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"plugins": [
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@@ -259,6 +259,16 @@
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"category": "developer-tools",
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"keywords": ["ios", "xcodegen", "swiftui", "spm", "avfoundation", "camera", "code-signing", "device-deployment", "swift", "xcode", "testing"],
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"skills": ["./iOS-APP-developer"]
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},
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{
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"name": "fact-checker",
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"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",
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"source": "./",
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"strict": false,
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"version": "1.0.0",
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"category": "productivity",
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"keywords": ["fact-checking", "verification", "accuracy", "sources", "validation", "corrections", "web-search"],
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"skills": ["./fact-checker"]
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}
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]
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}
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4
fact-checker/.security-scan-passed
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4
fact-checker/.security-scan-passed
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Security scan passed
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Scanned at: 2026-01-05T23:29:12.688630
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Tool: gitleaks + pattern-based validation
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Content hash: f0ee8b5411aeb2d3058383ae536393e4e71e0fe0c240d0798a50aa10399870d4
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188
fact-checker/README.md
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188
fact-checker/README.md
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# Fact Checker
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Verify factual claims in documents using web search and official sources, then apply corrections with user confirmation.
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## Features
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- ✅ Comprehensive fact verification across multiple domains
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- 🔍 Searches authoritative sources (official docs, API specs, academic papers)
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- 📊 Generates detailed correction reports with sources
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- 🤖 Auto-applies corrections after user approval
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- 🕐 Adds temporal context to prevent information decay
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## Supported Claim Types
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- **AI Model Specifications**: Context windows, pricing, features, benchmarks
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- **Technical Documentation**: API capabilities, version numbers, library features
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- **Statistical Data**: Metrics, benchmark scores, performance data
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- **General Facts**: Any verifiable factual statement
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## Usage Examples
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### Example 1: Update Outdated AI Model Info
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```
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User: Fact-check the AI model specifications in section 2.1
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```
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**What happens:**
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1. Identifies claims: "Claude 3.5 Sonnet: 200K tokens", "GPT-4o: 128K tokens"
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2. Searches official documentation for current models
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3. Finds: Claude Sonnet 4.5, GPT-5.2 with updated specs
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4. Generates correction report with sources
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5. Applies fixes after user confirms
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### Example 2: Verify Technical Claims
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```
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User: Check if these library versions are still current
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```
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**What happens:**
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1. Extracts version numbers from document
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2. Checks package registries (npm, PyPI, etc.)
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3. Identifies outdated versions
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4. Suggests updates with changelog references
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### Example 3: Validate Statistics
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```
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User: Verify the benchmark scores in this section
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```
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**What happens:**
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1. Identifies numerical claims and metrics
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2. Searches official benchmark publications
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3. Compares document values vs. source data
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4. Flags discrepancies with authoritative links
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## Workflow
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The skill follows a 5-step process:
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```
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Fact-checking Progress:
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- [ ] Step 1: Identify factual claims
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- [ ] Step 2: Search authoritative sources
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- [ ] Step 3: Compare claims against sources
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- [ ] Step 4: Generate correction report
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- [ ] Step 5: Apply corrections with user approval
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```
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## Source Evaluation
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**Preferred sources (in order):**
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1. Official product pages and documentation
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2. API documentation and developer guides
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3. Official blog announcements
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4. GitHub releases (for open source)
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**Use with caution:**
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- Third-party aggregators (verify against official sources)
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- Blog posts and articles (cross-reference)
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**Avoid:**
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- Outdated documentation
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- Unofficial wikis without citations
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- Speculation and rumors
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## Real-World Example
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**Before:**
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```markdown
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AI 大模型的"上下文窗口"不断升级：
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- Claude 3.5 Sonnet: 200K tokens（约 15 万汉字）
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- GPT-4o: 128K tokens（约 10 万汉字）
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- Gemini 1.5 Pro: 2M tokens（约 150 万汉字）
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```
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**After fact-checking:**
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```markdown
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AI 大模型的"上下文窗口"不断升级（截至 2026 年 1 月）：
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- Claude Sonnet 4.5: 200K tokens（约 15 万汉字）
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- GPT-5.2: 400K tokens（约 30 万汉字）
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- Gemini 3 Pro: 1M tokens（约 75 万汉字）
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```
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**Changes made:**
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- ✅ Updated Claude 3.5 Sonnet → Claude Sonnet 4.5
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- ✅ Corrected GPT-4o (128K) → GPT-5.2 (400K)
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- ✅ Fixed Gemini 1.5 Pro (2M) → Gemini 3 Pro (1M)
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- ✅ Added temporal marker "截至 2026 年 1 月"
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## Installation
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```bash
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# Via CCPM (recommended)
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ccpm install @daymade-skills/fact-checker
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# Manual installation
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Download fact-checker.zip and install through Claude Code
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```
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## Trigger Keywords
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The skill activates when you mention:
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- "fact-check this document"
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- "verify these claims"
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- "check if this is accurate"
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- "update outdated information"
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- "validate the data"
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## Configuration
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No configuration required. The skill works out of the box.
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## Limitations
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**Cannot verify:**
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- Subjective opinions or judgments
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- Future predictions or specifications
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- Claims requiring paywalled sources
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- Disputed facts without authoritative consensus
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**For such cases**, the skill will:
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- Note the limitation in the report
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- Suggest qualification language
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- Recommend user research or expert consultation
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## Best Practices
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### For Authors
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1. **Run regularly**: Fact-check documents periodically to catch outdated info
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2. **Include dates**: Add temporal markers like "as of [date]" to claims
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3. **Cite sources**: Keep original source links for future verification
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4. **Review reports**: Always review the correction report before applying changes
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### For Fact-Checking
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1. **Be specific**: Target specific sections rather than entire books
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2. **Verify critical claims first**: Prioritize high-impact information
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3. **Cross-reference**: For important claims, verify across multiple sources
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4. **Update regularly**: Technical specs change frequently - recheck periodically
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## Development
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Created with skill-creator v1.2.2 following Anthropic's best practices.
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**Testing:**
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- Verified on Claude Sonnet 4.5, Opus 4.5, and Haiku 4
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- Tested with real-world documentation updates
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- Validated correction workflow with user approval gates
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## Version History
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### 1.0.0 (2026-01-05)
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- Initial release
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- Support for AI models, technical docs, statistics
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- Auto-correction with user approval
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- Comprehensive source evaluation framework
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## License
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MIT License - See repository for details
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## Contributing
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Issues and pull requests welcome at [daymade/claude-code-skills](https://github.com/daymade/claude-code-skills)
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283
fact-checker/SKILL.md
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283
fact-checker/SKILL.md
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---
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name: fact-checker
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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.
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---
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# Fact Checker
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Verify factual claims in documents and propose corrections backed by authoritative sources.
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## When to use
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Trigger when users request:
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- "Fact-check this document"
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- "Verify these AI model specifications"
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- "Check if this information is still accurate"
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- "Update outdated data in this file"
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- "Validate the claims in this section"
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## Workflow
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Copy this checklist to track progress:
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```
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Fact-checking Progress:
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- [ ] Step 1: Identify factual claims
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- [ ] Step 2: Search authoritative sources
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- [ ] Step 3: Compare claims against sources
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- [ ] Step 4: Generate correction report
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- [ ] Step 5: Apply corrections with user approval
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```
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### Step 1: Identify factual claims
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Scan the document for verifiable statements:
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**Target claim types:**
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- Technical specifications (context windows, pricing, features)
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- Version numbers and release dates
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- Statistical data and metrics
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- API capabilities and limitations
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- Benchmark scores and performance data
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**Skip subjective content:**
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- Opinions and recommendations
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- Explanatory prose
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- Tutorial instructions
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- Architectural discussions
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### Step 2: Search authoritative sources
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For each claim, search official sources:
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**AI models:**
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- Official announcement pages (anthropic.com/news, openai.com/index, blog.google)
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- API documentation (platform.claude.com/docs, platform.openai.com/docs)
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- Developer guides and release notes
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**Technical libraries:**
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- Official documentation sites
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- GitHub repositories (releases, README)
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- Package registries (npm, PyPI, crates.io)
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**General claims:**
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- Academic papers and research
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- Government statistics
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- Industry standards bodies
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**Search strategy:**
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- Use model names + specification (e.g., "Claude Opus 4.5 context window")
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- Include current year for recent information
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- Verify from multiple sources when possible
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### Step 3: Compare claims against sources
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Create a comparison table:
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| Claim in Document | Source Information | Status | Authoritative Source |
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|-------------------|-------------------|--------|---------------------|
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| Claude 3.5 Sonnet: 200K tokens | Claude Sonnet 4.5: 200K tokens | ❌ Outdated model name | platform.claude.com/docs |
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| GPT-4o: 128K tokens | GPT-5.2: 400K tokens | ❌ Incorrect version & spec | openai.com/index/gpt-5-2 |
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**Status codes:**
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- ✅ Accurate - claim matches sources
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- ❌ Incorrect - claim contradicts sources
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- ⚠️ Outdated - claim was true but superseded
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- ❓ Unverifiable - no authoritative source found
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### Step 4: Generate correction report
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Present findings in structured format:
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```markdown
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## Fact-Check Report
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### Summary
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- Total claims checked: X
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- Accurate: Y
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- Issues found: Z
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### Issues Requiring Correction
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#### Issue 1: Outdated AI Model Reference
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**Location:** Line 77-80 in docs/file.md
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**Current claim:** "Claude 3.5 Sonnet: 200K tokens"
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**Correction:** "Claude Sonnet 4.5: 200K tokens"
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**Source:** https://platform.claude.com/docs/en/build-with-claude/context-windows
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**Rationale:** Claude 3.5 Sonnet has been superseded by Claude Sonnet 4.5 (released Sept 2025)
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#### Issue 2: Incorrect Context Window
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**Location:** Line 79 in docs/file.md
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**Current claim:** "GPT-4o: 128K tokens"
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**Correction:** "GPT-5.2: 400K tokens"
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**Source:** https://openai.com/index/introducing-gpt-5-2/
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**Rationale:** 128K was output limit; context window is 400K. Model also updated to GPT-5.2
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```
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### Step 5: Apply corrections with user approval
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**Before making changes:**
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1. Show the correction report to the user
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2. Wait for explicit approval: "Should I apply these corrections?"
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3. Only proceed after confirmation
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**When applying corrections:**
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```python
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# Use Edit tool to update document
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# Example:
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Edit(
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file_path="docs/03-写作规范/AI辅助写书方法论.md",
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old_string="- Claude 3.5 Sonnet: 200K tokens（约 15 万汉字）",
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new_string="- Claude Sonnet 4.5: 200K tokens（约 15 万汉字）"
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)
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```
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**After corrections:**
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1. Verify all edits were applied successfully
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2. Note the correction summary (e.g., "Updated 4 claims in section 2.1")
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3. Remind user to commit changes
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## Search best practices
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### Query construction
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**Good queries** (specific, current):
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- "Claude Opus 4.5 context window 2026"
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- "GPT-5.2 official release announcement"
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- "Gemini 3 Pro token limit specifications"
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**Poor queries** (vague, generic):
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- "Claude context"
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- "AI models"
|
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- "Latest version"
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|
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### Source evaluation
|
||||
|
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**Prefer official sources:**
|
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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
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|
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When sources conflict:
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|
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1. Prioritize most recent official documentation
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2. Note the discrepancy in the report
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||||
3. Present both sources to the user
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4. Recommend contacting vendor if critical
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When no source found:
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1. Mark as ❓ Unverifiable
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2. Suggest alternative phrasing: "According to [Source] as of [Date]..."
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3. Recommend adding qualification: "approximately", "reported as"
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|
||||
## Special considerations
|
||||
|
||||
### Time-sensitive information
|
||||
|
||||
Always include temporal context:
|
||||
|
||||
**Good corrections:**
|
||||
- "截至 2026 年 1 月" (As of January 2026)
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- "Claude Sonnet 4.5 (released September 2025)"
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||||
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**Poor corrections:**
|
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- "Latest version" (becomes outdated)
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- "Current model" (ambiguous timeframe)
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### Numerical precision
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||||
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Match precision to source:
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**Source says:** "approximately 1 million tokens"
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**Write:** "1M tokens (approximately)"
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||||
**Source says:** "200,000 token context window"
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**Write:** "200K tokens" (exact)
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||||
### Citation format
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||||
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Include citations in corrections:
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||||
|
||||
```markdown
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||||
> **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。
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||||
```
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||||
Link to sources when possible.
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||||
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||||
## 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
|
||||
Reference in New Issue
Block a user