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 TejasS1233 · LLM Plugin · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is opencode-parser safe to install? View the security audit →
opencode-parser An opencode plugin that parses any file into structured text the LLM can work with. https://github.com/user-attachments/assets/ed9d6ee7-d30b-43d5-83e0-4e09dafaa422 Install Or install via CLI: Or copy into for a local zero-config setup. Supported formats Usage Options —
| Stars | 60 |
| Forks | 4 |
| Language | TypeScript |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 74.4851209897194/100 |
| Last Updated | 2026-09-29 |
| Created | 2026-06-04 |
| Platforms | node |
| Est. Tokens | ~3k |
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opencode-parser is Parse any file in opencode. Supports PDF, DOCX, XLSX, PPTX, images, EPUB, HTML, Markdown, Jupyter, archives, and plain text.. It is categorized as a LLM Plugin with 60 GitHub stars.
opencode-parser is primarily written in TypeScript. It covers topics such as csv, document-extraction, document-parser.
You can find installation instructions and usage details in the opencode-parser GitHub repository at github.com/TejasS1233/opencode-parser. The project has 60 stars and 4 forks, indicating an active community.
opencode-parser is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to opencode-parser on Agent Skills Hub include MinerU-Skill, deckprobe, pdf-mcp. 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: