# canterbury

> canterbury — cthierer-canterbury. Use this tool when you need to integrate AI agents with Obsidian vaults, enabling controlled access and synchronization of data through a service layer. It solves problems of data consistency and accessibility across AI systems and Obsidian repositories. Ideal for use cases requiring automated knowledge management and version control via git.

Canonical page: https://skillsregistry.net/skills/cthierer-canterbury  
JSON: https://api.skillsregistry.net/v1/skills/cthierer-canterbury

## Description

An experimental system for connecting AI agents to an Obsidian vault through a controlled service layer.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/cthierer/canterbury)

## 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": "cthierer-canterbury"
    }
  }
}
```

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