# Cowork QA

> Use this tool when you need to automate browser-based testing for quality assurance workflows, capturing detailed action traces and supporting structured test scenarios. It solves problems related to automated testing, providing comprehensive audit trails and enabling large language models (LLMs) to perform goal-driven testing. With Cowork QA, AI agents can input test scenarios and receive output in the form of detailed action traces and audit trails, ideal for use cases requiring thorough browser automation testing.

Canonical page: https://skillsregistry.net/skills/insideos-cowork-qa  
JSON: https://api.skillsregistry.net/v1/skills/insideos-cowork-qa

## Description

Cowork QA provides AI agents with goal-driven Playwright browser automation sessions for quality assurance workflows. It captures full action traces, supports structured test scenarios, and enables LLMs to perform automated browser-based testing with comprehensive audit trails. Published as the cowork-qa-mcp npm package.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** browser-automation
- **Updated:** 2026-09-01

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/insideos-cowork-qa)
- **Repository:** <https://github.com/insideos-designs/cowork-qa-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": "insideos-cowork-qa"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/insideos-cowork-qa` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/insideos-cowork-qa/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