# obsidio

> obsidio — deeplook-obsidio. Use this tool when you need to interact with Obsidian vaults programmatically, to read, analyze, and manipulate their contents without launching the Electron app. It solves problems of automation, integration, and analysis in Python workflows, terminal use, and AI tooling, accepting vault directories and metadata as inputs and producing manipulated vault contents as outputs. Ideal for use cases involving automated knowledge graph updates, note analysis, and version control with git.

Canonical page: https://skillsregistry.net/skills/deeplook-obsidio  
JSON: https://api.skillsregistry.net/v1/skills/deeplook-obsidio

## Description

Python interface to Obsidian vaults, to read, analyze, and manipulate vaults without running the Electron app, for Python workflows, terminal use, and AI tooling.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/deeplook/obsidio)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "deeplook-obsidio"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/deeplook-obsidio` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/deeplook-obsidio/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt