# Otonom Aglar

> Use this tool when you need to build autonomous agents that can interact with their environment, manage data, and execute tasks independently. Otonom Aglar solves problems related to data retrieval, web scraping, and task automation by providing a framework for agents to think, act, and observe. It takes in various inputs such as files, database queries, and web pages, and outputs executed tasks, retrieved data, and managed issues.

Canonical page: https://skillsregistry.net/skills/arch-yunus-otonom-aglar  
JSON: https://api.skillsregistry.net/v1/skills/arch-yunus-otonom-aglar

## Description

Otonom Aglar is a Python-based autonomous agent framework built on MCP, implementing a ReAct loop (Thought-Action-Observation) with semantic memory via vector embeddings. It provides tools for file operations, SQLite queries, GitHub issue management, Playwright web scraping, RAG-based retrieval, sandboxed code execution, and recursive sub-agent delegation.

## Trust

- **Trust score (0–1):** 0.62
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/arch-yunus-otonom-aglar)
- **Repository:** <https://github.com/arch-yunus/otonom-aglar-mcp>

## 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": "arch-yunus-otonom-aglar"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/arch-yunus-otonom-aglar` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/arch-yunus-otonom-aglar/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