What is x402 Readability Extractor?
x402 Readability Extractor is a AI tool that Extract clean readable text from any web page. Strips ads, navigation, scripts and returns title + main content. Built-in SSRF protection. Perfect for content analysis and AI training data. Usage: /ap. It has a Nerq Trust Score of 40/100 (E). 0 GitHub stars. Published by 0xb4c2ee62cb5aa4ce853690bfcf7c434ce5a452a5. Last analyzed November 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 0 stars, 0 downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
What x402 Readability Extractor Does
x402 Readability Extractor is a agent in the AI tool category. Extract clean readable text from any web page. Strips ads, navigation, scripts and returns title + main content. Built-in SSRF protection. Perfect for content analysis and AI training data. Usage: /api/readability?url=https://example.com/article. It is published by 0xb4c2ee62cb5aa4ce853690bfcf7c434ce5a452a5 and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.
Who Should Use x402 Readability Extractor
x402 Readability Extractor is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | 0xb4c2ee62cb5aa4ce853690bfcf7c434ce5a452a5 |
|---|---|
| Category | AI tool |
| License | Not specified |
| Type | agent |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=x402-readability-extractor
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| MarkItDown | 42 | 92.8K |
| Time | 50 | 89.4K |
| Sequential Thinking | 50 | 89.4K |
| Filesystem | 50 | 89.4K |
| Fetch | 50 | 89.4K |
Frequently Asked Questions
Last updated November 2026. Trust scores based on automated analysis of public data.