No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by oxylabs · Agent Tool · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-scraper-py safe to install? View the security audit →
AI-Scraper The AI-Scraper is an experimental scraping tool by Oxylabs AI Studio that extracts data from a single webpage using AI. It identifies and parses relevant information based on a natural language prompt, then delivers results in either structured JSON (for automation and APIs) or Markdown format (best for readable outputs and AI workflows). This AI scraper removes the need for CSS/XPath selectors or custom parsers, so it integrates seamlessly with various automation pipelines. Automatic schema generation and flexible output formats provide users with an easy way to extract clean, structured data without ever needing to maintain parsing logic. Key features Natural language prompt-based extraction – Define your needs in plain English, and the scrape agent will retrieve the relevant information. Multiple output form
| Stars | 1,090 |
| Forks | 3 |
| Category | Agent Tool |
| Quality Score | 68.5503233195936/100 |
| Last Updated | 2026-04-02 |
| Created | 2025-09-25 |
| Platforms | browser |
| Est. Tokens | ~3k |
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ai-scraper-py is AI Scraper is a powerful scraping tool and scrape agent built to automate data extraction with unmatched precision. Ideal for scalable AI scraping tasks across diverse web sources, this tool simplifie. It is categorized as a Agent Tool with 1.1k GitHub stars.
You can find installation instructions and usage details in the ai-scraper-py GitHub repository at github.com/oxylabs/ai-scraper-py. The project has 1.1k stars and 3 forks, indicating an active community.
The top alternatives to ai-scraper-py on Agent Skills Hub include ouroboros, MakeMoneyWithAI, awesome-openclaw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: