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 KoljaB · Agent Tool · ★ 812
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Linguflex safe to install? View the security audit →
Bringing the sci-fi dream of a capable Jarvis-style AI companion into reality. Linguflex 2.0 Born out of my passion for science fiction, this project aims to simulate engaging, authentic, human-like interaction with AI personalities. It offers voice-based conversation with custom characters, alongside an array of practical features: controlling smart home devices, playing music, searching the internet, fetching emails, displaying current weather information and news, assisting in scheduling, and searching or generating images. I invite you to explore the framework, whether you're a user seeking an innovative AI experience or a fellow developer interested in the project. All insights, suggestions, and contributions are appreciated. I want to bring this personal passion project towards its f
| Stars | 812 |
| Forks | 80 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 51.2312153765668/100 |
| Open Issues | 10 |
| Last Updated | 2025-06-17 |
| Created | 2023-06-03 |
| Platforms | python |
| Est. Tokens | ~368k |
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Linguflex is Command Your World with Voice. It is categorized as a Agent Tool with 812 GitHub stars.
Linguflex is primarily written in Python. It covers topics such as chatbot, function-calling, openai.
You can find installation instructions and usage details in the Linguflex GitHub repository at github.com/KoljaB/Linguflex. The project has 812 stars and 80 forks, indicating an active community.
The top alternatives to Linguflex on Agent Skills Hub include core, LightAgent, openai-assistant-swarm. 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: