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 PersonalJarvis · MCP Server · ★ 94
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PersonalJarvis safe to install? View the security audit →
Your personal AI ecosystem, controlled entirely by voice. It runs Jarvis Agents, drives coding CLIs in a live IDE, operates your computer, connects anything that speaks MCP, dictates into any app, and remembers everything. Open source, and it can run fully on your own hardware, with no cloud account anywhere in the chain. Real time, not sped up. One spoken command, and it takes the screen and does it. Install it in one command Windows (PowerShell) macOS and Linux Python 3.11+ and Git, nothing else. The installer asks nothing in the t
| Stars | 94 |
| Forks | 39 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.617481224943/100 |
| Open Issues | 49 |
| Last Updated | 2026-10-04 |
| Created | 2026-05-31 |
| Platforms | browser, claude-code, cli, mcp, python |
| Est. Tokens | ~23k |
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PersonalJarvis is Your AI assistant, built for the agentic era. Open source and local: talk to it, and it runs your agents, your coding CLIs, your browser and your apps. Windows, macOS, Linux.. It is categorized as a MCP Server with 94 GitHub stars.
PersonalJarvis is primarily written in Python. It covers topics such as agent-orchestration, ai-agent, ai-coding-assistant.
You can find installation instructions and usage details in the PersonalJarvis GitHub repository at github.com/PersonalJarvis/PersonalJarvis. The project has 94 stars and 39 forks, indicating an active community.
PersonalJarvis is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to PersonalJarvis on Agent Skills Hub include guaardvark, heard, omega-memory. 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: