# catchintent

> Use this tool when you need to identify and manage intents behind user inputs, such as text or voice commands, to improve conversational interfaces and automate workflows. It solves problems like intent detection, entity extraction, and dialogue management, enabling more accurate and efficient human-computer interactions. With git integration, it accepts text-based inputs and outputs intent classifications and relevant data for further processing.

Canonical page: https://skillsregistry.net/skills/akashrajpurohit-catchintent  
JSON: https://api.skillsregistry.net/v1/skills/akashrajpurohit-catchintent

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-02

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/akashrajpurohit/catchintent)

## 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": "akashrajpurohit-catchintent"
    }
  }
}
```

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