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 Zipstack · MCP Server · ★ 7.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is unstract safe to install? View the security audit →
Unstract Turn Unstructured Documents into Structured Data Documentation | Enterprise <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/ass
| Stars | 7,267 |
| Forks | 720 |
| Language | Python |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 67.3787613964398/100 |
| Open Issues | 96 |
| Last Updated | 2026-10-01 |
| Created | 2024-02-21 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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unstract is LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows. It is categorized as a MCP Server with 7.3k GitHub stars.
unstract is primarily written in Python. It covers topics such as ai-agents, data-engineering, document-ai.
You can find installation instructions and usage details in the unstract GitHub repository at github.com/Zipstack/unstract. The project has 7.3k stars and 720 forks, indicating an active community.
unstract is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to unstract on Agent Skills Hub include hexstrike-ai, ruby_llm, rowboat. 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: