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 lucagrippa · Agent Tool · ★ 154
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is obsidian-ai-tagger safe to install? View the security audit →
🪄 AI Tagger AI Tagger is an Obsidian (https://obsidian.md) plugin that simplifies tagging by using various Large Language Models (LLMs) to analyze and tag your document with one click! The plugin analyzes the current document that you have open in the editor and all of the previous tags that you have used. AI tagger will return up to 5 relevant tags that you have previously used and will generate up to 3 completely new tags. 🤖 Supported AI Models 🚀 Setup Install from Obsidian Community Plugins Enter
| Stars | 154 |
| Forks | 20 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 55.5350057260747/100 |
| Open Issues | 19 |
| Last Updated | 2024-12-27 |
| Created | 2023-10-06 |
| Platforms | cli, gemini, node |
| Est. Tokens | ~1140k |
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obsidian-ai-tagger is Simplify tagging in Obsidian. Instantly analyze and tag your document with one click for efficient note organization.. It is categorized as a Agent Tool with 154 GitHub stars.
obsidian-ai-tagger is primarily written in TypeScript. It covers topics such as ai, anthropic, gemini.
You can find installation instructions and usage details in the obsidian-ai-tagger GitHub repository at github.com/lucagrippa/obsidian-ai-tagger. The project has 154 stars and 20 forks, indicating an active community.
obsidian-ai-tagger is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to obsidian-ai-tagger on Agent Skills Hub include abso, cai, agent-builder. 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: