# user-research-skill

> Use this tool when you need to conduct thorough user research, gathering insights to inform product development and improve user experience. It solves problems such as understanding user needs, identifying pain points, and informing design decisions, by taking in user data and feedback as inputs and producing actionable recommendations as outputs. Ideal for use in product development, UX design, and market research contexts, where user-centric insights are crucial.

Canonical page: https://skillsregistry.net/skills/cookiy-ai-user-research-skill  
JSON: https://api.skillsregistry.net/v1/skills/cookiy-ai-user-research-skill

## Description

User Research Omni Skill for AI Agents — Claude, Codex, OpenClaw, Cursor

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-04-17

## Source

- **Source listing:** [GitHub](https://github.com/cookiy-ai/user-research-skill)

## 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": "cookiy-ai-user-research-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cookiy-ai-user-research-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cookiy-ai-user-research-skill/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