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 google · Codex Skill · ★ 10.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is artemis safe to install? View the security audit →
Let AI assistants and test suites use real phones like a human. English • 中文文档 • Workflow Showcase • Quick Start • MCP for IDEs • Benchmarks • Discord Community Live
| Stars | 10,908 |
| Forks | 1,106 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 63.9994566557582/100 |
| Open Issues | 120 |
| Last Updated | 2026-10-01 |
| Created | 2026-08-13 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~17k |
These tools work well together with artemis for enhanced workflows:
Looking for a artemis alternative? If you're comparing artemis with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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Explore other popular codex skill tools:
artemis is ARTEMIS turns natural-language instructions into reliable Android automation. It automates end-to-end workflows, captures logs, and integrates seamlessly with AI coding assistants such as Antigravity,. It is categorized as a Codex Skill with 10.9k GitHub stars.
artemis is primarily written in Python. It covers topics such as ai-agents, android, google.
You can find installation instructions and usage details in the artemis GitHub repository at github.com/google/artemis. The project has 10.9k stars and 1106 forks, indicating an active community.
artemis is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to artemis on Agent Skills Hub include lamda, shortest, LibreChat. 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: