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 Nebutra · Claude Skill · ★ 94
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MinerU-Skill safe to install? View the security audit →
MinerU Skill An AI-Native document parser built for AI agents — turn PDF, Office & image files into clean Markdown with zero API key, zero install, and fast parallel batches. 中文文档 | English ⚡ Try it in 5 seconds (no signup
| Stars | 94 |
| Forks | 3 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 65.6836942987024/100 |
| Open Issues | 4 |
| Last Updated | 2026-07-24 |
| Created | 2026-02-13 |
| Platforms | claude-code, cli, python |
| Est. Tokens | ~93k |
These tools work well together with MinerU-Skill for enhanced workflows:
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MinerU-Skill is AI-Native document parser: PDF, Office & images → clean Markdown with LaTeX, tables & OCR. Zero-dependency CLI & skill for Claude Code, Cursor & AI agents.. It is categorized as a Claude Skill with 94 GitHub stars.
MinerU-Skill is primarily written in Python. It covers topics such as ai-agents, ai-skill, claude-code.
You can find installation instructions and usage details in the MinerU-Skill GitHub repository at github.com/Nebutra/MinerU-Skill. The project has 94 stars and 3 forks, indicating an active community.
MinerU-Skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to MinerU-Skill on Agent Skills Hub include LT2MD, pdf-mcp, pdfmux. 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: