v1.1.0 to v1.1.1

10 added, 11 removed. Audit A to A.

---

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 Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents.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.'

- zh_description: "用于事实、checker，支持信息整理、沟通和执行管理。"

- version: "1.1.0"

+ description: 'Verify factual claims against current authoritative sources and apply requested corrections with citations; use for accuracy checks or outdated documentation.'

+ zh_description: "核实事实与时效性，并按请求修正文档及补充来源。"

+ version: "1.1.1"

author: "seaworld008"

source: "in-house"

source_url: ""

tags: '["checker", "fact", "productivity"]'

created_at: "2026-03-04"

- updated_at: "2026-07-27"

+ updated_at: "2026-09-06"

quality: 5

complexity: "intermediate"

---

# 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- "Update outdated data in this file"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 the exact claim subject + specification + provider

- Include current year for recent information

- Verify from multiple sources when possible

### Step 3: Compare claims against sources

Create a comparison table:

|-------------------|-------------------|--------|---------------------|

**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 Product Specification

**Location:** Line 77-80 in docs/file.md

**Current claim:** "<exact text from the document>"

**Correction:** "<claim supported by the current official source>"

**Source:** <direct official documentation URL>

**Rationale:** <what changed, including effective or release date>

#### Issue 2: Incorrect Scope or Unit

**Location:** Line 79 in docs/file.md

**Current claim:** "<exact text>"

**Correction:** "<correct value with unit and scope>"

**Source:** <direct primary-source URL>

**Rationale:** <explain whether the error was version, unit, geography, plan, or date>

```

- ### Step 5: Apply corrections with user approval

- **Before making changes:**

+ ### Step 5: Apply Requested Corrections

- 1. Show the correction report to the user

- 2. Wait for explicit approval: "Should I apply these corrections?"

- 3. Only proceed after confirmation

+ If the user requested corrections, apply supported edits and report their sources.

+ If the task is review-only, present the report without changing the document.

+ Ask about unresolved meaning or consequential scope changes, not routine edits

+ already authorized by the request.

**When applying corrections:**

```text

Apply an exact, anchored replacement only after re-reading the current file.

Preserve surrounding formatting and attach the source near the corrected claim.

```

**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):

- "<provider> <product> official plan limits <current year>"

- "<library> official release notes stable version"

- "<standards body> <metric> definition <effective year>"

**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**：产品规格和版本信息以链接的官方文档为准；核验日期：YYYY-MM-DD。

```

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. Extract each model name, limit, unit, plan, and date as a separate claim

2. Search each provider's current official model catalog and documentation

3. Record the exact source page and verification date

4. Generate report showing discrepancies

- 5. Apply corrections after approval

+ 5. Apply corrections within the requested scope; keep review-only tasks read-only

### 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