obsidian-gemini-helper: AI assistant with chat, workflow automation, and semantic search (RAG). Powered by Gemini API or CLI tools

by takeshy · Agent Tool · ★ 117

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Featured in: Obsidian for Claude Code & AI Agents

Security audit verdict: SAFE · quality 66/100

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.

geminiobsidianragvisualization-workflowworkflow-automation

Quick Facts

Stars117
Forks9
LanguageTypeScript
CategoryAgent Tool
LicenseMIT
Quality Score66/100
Last Updated2026-10-06
Created2025-12-10
Platformscli, gemini, node
Est. Tokens~2174k

About obsidian-gemini-helper

Gemini Helper for Obsidian Free and open-source AI assistant for Obsidian with Chat, Workflow Automation, and RAG powered by Google Gemini. This plugin is completely free. You only need a Google Gemini API key (free or paid) from ai.google.dev.

Compatible Skills

These tools work well together with obsidian-gemini-helper for enhanced workflows:

  • infio-copilot — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • ai-agent-automation — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)
  • agentic-signal — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)
  • mcp-local-rag — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)

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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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