Verify factual claims in documents and propose corrections backed by authoritative sources.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfact-checkerExecute the skills CLI command in your project's root directory to begin installation:
Fetches fact-checker from daymade/claude-code-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate fact-checker. Access via /fact-checker in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Verify factual claims in documents and propose corrections backed by authoritative sources.
Trigger when users request:
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
Scan the document for verifiable statements:
Target claim types:
Skip subjective content:
For each claim, search official sources:
AI models:
Technical libraries:
General claims:
Search strategy:
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:
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
Before making changes:
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:
Good queries (specific, current):
Poor queries (vague, generic):
Prefer official sources:
Use with caution:
Avoid:
When sources conflict:
When no source found:
Always include temporal context:
Good corrections:
Poor corrections:
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)
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.
User request: "Fact-check the AI model context windows in section 2.1"
Process:
User request: "Verify the benchmark scores in chapter 5"
Process:
User request: "Check if these library versions are still current"
Process:
Before completing fact-check:
This skill cannot:
For such cases:
After fact-checking, suggest exporting the verified document:
Fact-check complete: [N] claims verified, [M] corrections proposed.
Options:
A) Export as PDF — run /pdf-creator (Recommended for formal documents)
B) Create slides — run /ppt-creator from verified content
C) No thanks — I'll use the corrected document directly
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
fact-checker reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend fact-checker for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for fact-checker matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: fact-checker is focused, and the summary matches what you get after install.
Useful defaults in fact-checker — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for fact-checker matched our evaluation — installs cleanly and behaves as described in the markdown.
fact-checker is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in fact-checker — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
fact-checker has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend fact-checker for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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