# genpark-neural-audio-enhancement-stem-separator-skill

> genpark-neural-audio-enhancement-stem-separator-skill — alphaparkinc-genpark-neural-audio-enhancement-stem-separator-skill. Use this tool when you need to enhance and separate audio tracks from mixed recordings, removing reverb and isolating individual stems. It takes in mixed audio files as input and outputs separated multi-tracks, solving problems like noise reduction and track isolation in music production and post-production. Ideal for use cases where high-quality audio separation is required, such as music remixing, audio restoration, and film editing.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-neural-audio-enhancement-stem-separator-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-neural-audio-enhancement-stem-separator-skill

## Description

Neural audio enhancement and stem separation engine removing reverb and isolating multi-tracks (Descript style)

## 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:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/alphaparkinc/genpark-neural-audio-enhancement-stem-separator-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": "alphaparkinc-genpark-neural-audio-enhancement-stem-separator-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-neural-audio-enhancement-stem-separator-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-neural-audio-enhancement-stem-separator-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
