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 SeloraHomes · MCP Server · ★ 86
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ha-selora-ai safe to install? View the security audit →
Selora AI — Home Assistant Integration Selora AI is a smart-home AI butler for Home Assistant. It connects to an LLM backend (Anthropic Claude, OpenAI, or a local Ollama model), learns your home's patterns, and proactively generates automations — all while keeping you in full control. Documentation Features Exposes a [Model Context Protocol](https://modelcontextprotoc
| Stars | 86 |
| Forks | 2 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 61.7698336289169/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-22 |
| Created | 2026-03-12 |
| Platforms | mcp, python |
| Est. Tokens | ~14k |
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ha-selora-ai is Your smart assistant to manage and maintain your home.. It is categorized as a MCP Server with 86 GitHub stars.
ha-selora-ai is primarily written in Python. It covers topics such as hacs, home-assistant, mcp.
You can find installation instructions and usage details in the ha-selora-ai GitHub repository at github.com/SeloraHomes/ha-selora-ai. The project has 86 stars and 2 forks, indicating an active community.
ha-selora-ai is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ha-selora-ai on Agent Skills Hub include mcp-assist, hass-mcp-server, ha-mcp-integration. 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: