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 jxlarrea · LLM Plugin · ★ 62
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is voice-satellite-card-llm-tools safe to install? View the security audit →
Extend your voice assistant's capabilities with web, Wikipedia, image, vide
| Stars | 62 |
| Forks | 9 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 67.0486925276839/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-20 |
| Created | 2026-02-23 |
| Platforms | python |
| Est. Tokens | ~16k |
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voice-satellite-card-llm-tools is LLM tools for Home Assistant that extends Voice Satellite with weather, financial, image and video search capabilities. Provides tools that conversation agents (OpenAI, Google Generative AI, Anthropic. It is categorized as a LLM Plugin with 62 GitHub stars.
voice-satellite-card-llm-tools is primarily written in Python. It covers topics such as hacs, hacs-integration, home-assistant.
You can find installation instructions and usage details in the voice-satellite-card-llm-tools GitHub repository at github.com/jxlarrea/voice-satellite-card-llm-tools. The project has 62 stars and 9 forks, indicating an active community.
voice-satellite-card-llm-tools is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to voice-satellite-card-llm-tools on Agent Skills Hub include llm_intents, home-generative-agent, ha-text-ai. 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: