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 openclaw · MCP Server · ★ 5.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Peekaboo safe to install? View the security audit →
Peekaboo 🫣 — Mac automation that sees the screen and does the clicks. Peekaboo is a macOS CLI and menu-bar app for screen capture, accessibility inspection, and native UI automation. Use it directly, let its agent plan multi-step work, or expose the same tool
| Stars | 5,185 |
| Forks | 399 |
| Language | Swift |
| Category | MCP Server |
| License | MIT |
| Quality Score | 76.4516869402906/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-19 |
| Created | 2025-05-22 |
| Platforms | cli, mcp |
| Est. Tokens | ~16k |
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Peekaboo is Peekaboo is a macOS CLI & optional MCP server that enables AI agents to capture screenshots of applications, or the entire system, with optional visual question answering through local or remote AI mo. It is categorized as a MCP Server with 5.2k GitHub stars.
Peekaboo is primarily written in Swift. It covers topics such as ai, macos, mcp.
You can find installation instructions and usage details in the Peekaboo GitHub repository at github.com/openclaw/Peekaboo. The project has 5.2k stars and 399 forks, indicating an active community.
Peekaboo is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Peekaboo on Agent Skills Hub include Peekaboo, Kiln, amical. 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: