by Agents365-ai · Agent Tool · ★ 107
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AI Tagger Universe: Easy Tag Generation & Management for Obsidian Automatically generate intelligent tags for your Obsidian notes using AI. This plugin analyzes your content and adds relevant tags to your note's frontmatter, helping you organize and discover connections in your knowledge base. 🔌 Installation This plugin can be installed directly from the Obsidian Community Plugins browser: Open Obsidian Settings Navigate to Community Plugins Disable Safe Mode (if enabled) Search for "AI Tagger Universe" Click Install, then Enable Alternatively, you can manually install the plugin: Download the latest release from this repository Extract the files to your Obsidian vault's folder Reload Obsidian and enable the plugin in the Community Plugins settings ✨ Key Features 🤖 Flexible AI Integration Use your preferred
| Stars | 107 |
| Forks | 23 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 54.7198901781875/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-07 |
| Created | 2025-02-13 |
| Platforms | node |
| Est. Tokens | ~16k |
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obsidian-ai-tagger-universe is An intelligent Obsidian plugin that leverages AI to automatically analyze note content and suggest relevant tags, supporting both local and cloud-based LLM services.. It is categorized as a Agent Tool with 107 GitHub stars.
obsidian-ai-tagger-universe is primarily written in TypeScript.
You can find installation instructions and usage details in the obsidian-ai-tagger-universe GitHub repository at github.com/Agents365-ai/obsidian-ai-tagger-universe. The project has 107 stars and 23 forks, indicating an active community.
obsidian-ai-tagger-universe is released under the MIT license, making it free to use and modify according to the license terms.
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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.
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