obsidian-ai-tagger: Simplify tagging in Obsidian

by lucagrippa · Agent Tool · ★ 154

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Featured in: Obsidian for Claude Code & AI Agents

Security audit verdict: SAFE · quality 56/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aianthropicgeminigroqllmmistralaiobsidian-mdobsidian-pluginollamaopenai

Quick Facts

Stars154
Forks20
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score56/100
Open Issues19
Last Updated2024-12-27
Created2023-10-06
Platformscli, gemini, node
Est. Tokens~1140k

About obsidian-ai-tagger

🪄 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.

Compatible Skills

These tools work well together with obsidian-ai-tagger for enhanced workflows:

  • obsidian-agent-client — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • obsidian-plugin-skill — semantic(0.48)+complementary+rare_topics+similar_pop+shared_platform (61%)
  • jarvis-dashboard — semantic(0.35)+complementary+rare_topics+similar_pop+shared_platform (56%)
  • brainstorm-mcp — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • obsidian-mcp-server — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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