# chatlab

> chatlab — ricardoborges-chatlab. Use this tool when you need to integrate and test large language models (LLMs) in a conversational interface, solving problems such as validating chat app functionality and ensuring seamless interactions between users and AI agents. It takes in inputs from various tools like llama-stack-client and Ollama, and outputs test results, providing a comprehensive interface for integration tests. Ideal for use cases involving LLM testing and validation in a chat app environment.

Canonical page: https://skillsregistry.net/skills/ricardoborges-chatlab  
JSON: https://api.skillsregistry.net/v1/skills/ricardoborges-chatlab

## Description

LLM  chat app for integration tests using llama-stack-client, llama, Ollama, MCP, Tools

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-20

## Source

- **Source listing:** [GitHub](https://github.com/ricardoborges/chatlab)

## 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": "ricardoborges-chatlab"
    }
  }
}
```

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