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 geezerrrr · Codex Skill · ★ 117
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is motive safe to install? View the security audit →
Motive Say it. Walk away. A macOS menu bar app that runs AI agents in the background — and finds you when they need approval. Documentation · Download · Feedback Why Motive? AI coding agents are powerful, but they all assume you're watching. Switch away from the terminal or editor, and you'll come back to find the agent stuck on a permission approval that's been sitting there for minutes. Motive puts the agent in your menu bar. No window to b
| Stars | 117 |
| Forks | 8 |
| Language | Swift |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 71.6823526449327/100 |
| Open Issues | 1 |
| Last Updated | 2026-02-28 |
| Created | 2026-01-20 |
| Platforms | claude-code, cli, codex, gemini |
| Est. Tokens | ~4312k |
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motive is The AI agent that works while you don't watch. Lives in your menu bar, finds you when needed.. It is categorized as a Codex Skill with 117 GitHub stars.
motive is primarily written in Swift. It covers topics such as ai-agent, ai-agents, ai-tools.
You can find installation instructions and usage details in the motive GitHub repository at github.com/geezerrrr/motive. The project has 117 stars and 8 forks, indicating an active community.
motive is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to motive on Agent Skills Hub include cowork-forge, compose-skill, kindly-web-search-mcp-server. 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: