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 impactjo · MCP Server · ★ 77
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is home-memory safe to install? View the security audit →
Home Memory Ask your home. Your AI assistant's memory for everything in and around your home. ▶ Watch the 1-minute demo on YouTube Home Memory is an MCP server that gives your AI assistant structured, persistent knowledge about your home: every room, every device, every pipe and cable, every item you own. It works with AI clients on your computer that support MCP, including Claude Desktop, Claude Code, Codex App, Codex CLI, and others. You talk naturally; the AI reads and writes your home data through Home Memory's tools. Document what exists, plan what's coming, and keep track of what's been removed. No app to learn. No forms to fill out. Your home data stays in a single file on your machine, and the AI is your interface. Tell your AI about
| Stars | 77 |
| Forks | 3 |
| Language | C# |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 69.567453236026/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-20 |
| Created | 2026-03-13 |
| Platforms | claude-code, dotnet, mcp |
| Est. Tokens | ~20k |
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home-memory is MCP server that lets your AI assistant remember everything about your home.. It is categorized as a MCP Server with 77 GitHub stars.
home-memory is primarily written in C#. It covers topics such as building, claude, csharp.
You can find installation instructions and usage details in the home-memory GitHub repository at github.com/impactjo/home-memory. The project has 77 stars and 3 forks, indicating an active community.
home-memory is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to home-memory on Agent Skills Hub include MCPSharp, UnityMCP, SharpToolsMCP. 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: