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 SevenBT · Agent Tool · ★ 93
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is nemo-assistant safe to install? View the security audit →
Nemo Assistant · Floating Desktop AI Assistant Nemo Assistant is a lightweight desktop assistant that brings selection actions, screenshot OCR, Markdown notes, sticky notes, tool calling, and AI chat into one local-first workflow. It is not another chat window waiting for you to switch context. It is a set of small desktop actions that stay close to your work: translate while reading, polish while writing, capture and understand screen content, pin a quick sticky note, and let AI call tools, read notes, or save memories when needed. Built with PyQt6 + Fluent Design. Notes, memories, configuration, and conversations are stored locally by default. API keys are saved through the system keyring instead of plaintext config files. Chinese documentation
| Stars | 93 |
| Forks | 0 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 58.0386273606744/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-12 |
| Created | 2026-07-05 |
| Platforms | python |
| Est. Tokens | ~13k |
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nemo-assistant is Local-first floating desktop AI assistant for Windows — selection actions, OCR, notes, and a tool-calling agent.. It is categorized as a Agent Tool with 93 GitHub stars.
nemo-assistant is primarily written in Python. It covers topics such as assistant-personnel, fluent-design, pyqt6.
You can find installation instructions and usage details in the nemo-assistant GitHub repository at github.com/SevenBT/nemo-assistant. The project has 93 stars and 0 forks, indicating an active community.
nemo-assistant is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to nemo-assistant on Agent Skills Hub include mcpstore, mcp-gateway, claude-context-local. 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: