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 robsonfelix · Agent Tool · ★ 110
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is robsonfelix-hass-addons safe to install? View the security audit →
Robson Felix's Home Assistant Add-ons Custom add-ons for Home Assistant. Add-ons Installation Or manually: Settings → Add-ons → Add-on Store → ⋮ → Repositories → Add License MIT License
| Stars | 110 |
| Forks | 37 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 53.1409614735409/100 |
| Open Issues | 7 |
| Last Updated | 2026-07-09 |
| Created | 2026-01-14 |
| Platforms | claude-code, python |
| Est. Tokens | ~11k |
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robsonfelix-hass-addons is Home Assistant add-ons by Robson Felix. Includes Claude Code - run Anthropic's AI coding assistant directly in Home Assistant to create automations and manage your smart home.. It is categorized as a Agent Tool with 110 GitHub stars.
robsonfelix-hass-addons is primarily written in Python. It covers topics such as ai, anthropic, automation.
You can find installation instructions and usage details in the robsonfelix-hass-addons GitHub repository at github.com/robsonfelix/robsonfelix-hass-addons. The project has 110 stars and 37 forks, indicating an active community.
robsonfelix-hass-addons is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to robsonfelix-hass-addons on Agent Skills Hub include hass-mcp, openpaw, claude-emporium. 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: