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 dealfluence · MCP Server · ★ 156
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is adeu safe to install? View the security audit →
Adeu: Track Changes for the LLM era LLMs speak Markdown; reviewers speak "Track Changes." Adeu is a docx ↔ LLM translator: a Model Context Protocol (MCP) server (Python and Node.js implementations) and accompanying SDKs that act as a Virtual DOM for Microsoft Word. It provides a two-way abstraction layer that lets AI agents freely edit document text without destroying the underlying formatting or complex DOCX XML. While standard libraries like excel at generating documents from scratch, they fail at non-destructive redlining. Adeu solves this by translating files into a token-effi
| Stars | 156 |
| Forks | 23 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.9825737700644/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-10 |
| Created | 2025-12-30 |
| Platforms | cli, gemini, mcp, python |
| Est. Tokens | ~16k |
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adeu is docx ↔ LLM translator. Projects .docx office files to Markdown for editing. Projects edits back to OOXML as tracked changes (redlines). Python and Node.js implementations.. It is categorized as a MCP Server with 156 GitHub stars.
adeu is primarily written in Python. It covers topics such as ai-agents, document-automation, docx-converter.
You can find installation instructions and usage details in the adeu GitHub repository at github.com/dealfluence/adeu. The project has 156 stars and 23 forks, indicating an active community.
adeu is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to adeu on Agent Skills Hub include azan-mcp, kinocut, mcp-video. 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: