# qkb

> qkb — miguelarios-qkb. Use this tool when you need to efficiently search and retrieve specific notes within Obsidian vaults, leveraging both keyword and semantic search capabilities. It solves problems of information overload and disorganization by providing metadata filtering and sibling-document retrieval, enabling precise querying of notes. The qkb tool accepts search queries as input and returns relevant notes as output, making it ideal for use cases requiring rapid information retrieval and analysis.

Canonical page: https://skillsregistry.net/skills/miguelarios-qkb  
JSON: https://api.skillsregistry.net/v1/skills/miguelarios-qkb

## Description

Provides a hybrid search engine for Obsidian vaults, enabling LLM agents to query notes with BM25 keyword and vector semantic search, metadata filtering, and sibling-document retrieval.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/zkqb3gboof)
- **Repository:** <https://github.com/miguelarios/qkb>

## 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": "miguelarios-qkb"
    }
  }
}
```

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