# AutoEQ

> Use this tool when you need to equalize audio for specific headphones or IEMs, solving sound quality issues and providing optimal listening experiences. AutoEQ offers a database of over 8,800 profiles, allowing users to search and retrieve parametric EQ settings and compare sound signatures. It takes headphone names, types, or sound signatures as input and outputs customized EQ profiles and rankings based on Harman preference scores.

Canonical page: https://skillsregistry.net/skills/veridyia-autoeq  
JSON: https://api.skillsregistry.net/v1/skills/veridyia-autoeq

## Description

Provides access to the AutoEQ headphone equalization database covering over 8,800 headphone and IEM profiles from 22 measurement sources. Supports searching by name, type, or sound signature, retrieving full parametric EQ profiles with per-band analysis, side-by-side headphone comparisons, and Harman preference score rankings. Automatically classifies sound signatures as neutral, warm, bright, dark, V-shaped, and other profiles based on frequency response deviation analysis.

## Trust

- **Trust score (0–1):** 0.89
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/veridyia-autoeq)
- **Repository:** <https://github.com/veridyia/autoeq-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": "veridyia-autoeq"
    }
  }
}
```

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