# agentic-browser-0-1-2

> agentic-browser-0-1-2 — xyny89-agentic-browser-0-1-2. Use this tool when you need to automate browser interactions for AI agents, solving problems such as data extraction, web scraping, and automated testing. It takes inputs via inference.sh and outputs automated browser actions, providing a seamless interface for AI-driven workflows. Ideal for use cases requiring efficient and scalable browser automation, such as data mining and web application testing.

Canonical page: https://skillsregistry.net/skills/xyny89-agentic-browser-0-1-2  
JSON: https://api.skillsregistry.net/v1/skills/xyny89-agentic-browser-0-1-2

## Description

Browser automation for AI agents via inference.sh.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** browser-automation
- **Updated:** 2026-09-13

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/xyny89-agentic-browser-0-1-2)

## 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": "xyny89-agentic-browser-0-1-2"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/xyny89-agentic-browser-0-1-2` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/xyny89-agentic-browser-0-1-2/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