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 zeenie-ai · Codex Skill · ★ 979
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenCompany safe to install? View the security audit →
OpenCompany Your own AI assistant that does real work. Drag, drop, and connect AI agents to your email, calendar, messages, phone, and 50+ other services. Runs on your own machine — your data stays with you. No code required. No subscription. No usage limits. Bring your own API keys (or run models locally with Ollama / LM Studio for free). Quick Start Prerequisites: Node.js 22+, Python 3.12+ The canonical npm package is . The unscoped package is unrelated to this project and is neither installed nor removed by OpenCompany's tooling. Open http://localhost:3000 and click Credentials to connect your first AI provi
| Stars | 979 |
| Forks | 149 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 62.5774854681197/100 |
| Open Issues | 4 |
| Last Updated | 2026-10-03 |
| Created | 2025-10-13 |
| Platforms | python |
| Est. Tokens | ~16k |
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OpenCompany is Self-improving AI Employees turning LLM tokens into work and dollars.. It is categorized as a Codex Skill with 979 GitHub stars.
OpenCompany is primarily written in Python. It covers topics such as ai, ai-agents, assistant.
You can find installation instructions and usage details in the OpenCompany GitHub repository at github.com/zeenie-ai/OpenCompany. The project has 979 stars and 149 forks, indicating an active community.
OpenCompany is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenCompany on Agent Skills Hub include hello-claw, RedBox, 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: