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 Mobile-Artificial-Intelligence · Agent Tool · ★ 2.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is maid safe to install? View the security audit →
Maid - Mobile Artificial Intelligence Distribution <a href='https://play.google.com/store/apps/details?id=com.danemadsen.maid&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-Par
| Stars | 2,684 |
| Forks | 290 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65.4732194728902/100 |
| Open Issues | 10 |
| Last Updated | 2026-09-14 |
| Created | 2023-10-17 |
| Platforms | claude-code, node |
| Est. Tokens | ~15k |
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maid is Maid is a free and open source application for interfacing with llama.cpp models locally, and with Anthropic, DeepSeek, Ollama, Mistral and OpenAI models remotely.. It is categorized as a Agent Tool with 2.7k GitHub stars.
maid is primarily written in TypeScript. It covers topics such as android, anthropic, chatbot.
You can find installation instructions and usage details in the maid GitHub repository at github.com/Mobile-Artificial-Intelligence/maid. The project has 2.7k stars and 290 forks, indicating an active community.
maid is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to maid on Agent Skills Hub include dialoqbase, langchain4j, casibase. 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: