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 ThinkFlowLab · MCP Server · ★ 109
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is system1-agents safe to install? View the security audit →
system1-agents [!NOTE] Give your agents a System 1 decision model. Start from a prebuilt agent or build your own. Describe the task. Claude Code or Codex runs a prebuilt agent or builds a new one, on Jev, Laya or Cua-S1 Nano. Browser use, computer use, robotics and games ship ready to run. Up to 6× faster and 25× cheaper than a chat model, at the same score. Quickstart · From Claude Code or Codex · Benchmarks · Docs 1
| Stars | 109 |
| Forks | 18 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.4102246172008/100 |
| Open Issues | 23 |
| Last Updated | 2026-09-29 |
| Created | 2026-09-22 |
| Platforms | browser, claude-code, mcp, python |
| Est. Tokens | ~15k |
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system1-agents is System 1 decision models (Jev, Laya, Cua-S1) as brain for agents: Browser use, computer use, games and robotics. It is categorized as a MCP Server with 109 GitHub stars.
system1-agents is primarily written in Python. It covers topics such as ai-agents, browser-agent, browser-automation.
You can find installation instructions and usage details in the system1-agents GitHub repository at github.com/ThinkFlowLab/system1-agents. The project has 109 stars and 18 forks, indicating an active community.
system1-agents is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to system1-agents on Agent Skills Hub include jev-social, jevcore, web-agent-protocol. 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: