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 AboveColin · LLM Plugin · ★ 50
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is HA-Jev safe to install? View the security audit →
Jev for Home Assistant Ask TypeSafe Jev questions about your house and get numbers back. Jev is a decision model rather than a chat model, so it answers a typed question with a probability, a choice or a score, and this integration turns each answer into an entity you can automate on. Not affiliated with TypeSafe. The API client is jevclient. What it does Questions in become sensors: a probability, one of your options with its distribution, or a number that can land between levels. Four actions answer inside an automation and return a response variable: , , and jev
| Stars | 50 |
| Forks | 4 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 72.9562393012225/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-22 |
| Created | 2026-09-17 |
| Platforms | python |
| Est. Tokens | ~16k |
These tools work well together with HA-Jev for enhanced workflows:
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HA-Jev is Ask your house a question, get a number back. Home Assistant integration for TypeSafe Jev: typed answers as sensors, four actions for automations, and a conversation agent for Assist.. It is categorized as a LLM Plugin with 50 GitHub stars.
HA-Jev is primarily written in Python. It covers topics such as ai, assist, conversation.
You can find installation instructions and usage details in the HA-Jev GitHub repository at github.com/AboveColin/HA-Jev. The project has 50 stars and 4 forks, indicating an active community.
HA-Jev is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to HA-Jev on Agent Skills Hub include home-mind, ha-text-ai, voice-satellite-card-llm-tools. 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: