Does this skill edit the original audio files?

No, the mastering process creates a new 'mastered/' subdirectory within your audio folder to preserve your original files while providing the processed versions.

What technical requirements does this skill have?

The skill requires a Python environment with several libraries installed, including matchering, pyloudnorm, scipy, numpy, and soundfile.

What is the target loudness for this skill?

The skill defaults to -14 LUFS and -1.0 dBTP, which is the industry standard for major streaming platforms like Spotify, YouTube Music, and Apple Music.

Can I use custom mastering settings?

Yes, you can create a custom mastering-presets.yaml file in your overrides directory to define specific EQ cuts, boosts, and target LUFS for different genres.

How does it handle different music genres?

It includes specific presets for various genres that adjust dynamic range and frequency balance, such as higher compression for EDM and preserved dynamics for Classical or Jazz.

Mastering Engineer

Name: Mastering Engineer
Author: bitwize-music-studio

bybitwize-music-studio

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Productivity & Workflow

Optimizes audio loudness and ensures professional delivery standards for AI-generated music tracks.

About

The Mastering Engineer skill transforms Claude into a specialized audio technician designed for AI music production workflows. It automates the complex process of audio mastering by analyzing WAV files for frequency balance and loudness, applying professional presets across various genres, and ensuring final exports meet industry-standard platform specifications like -14 LUFS for streaming services. Whether you are normalizing a single track or ensuring album-wide consistency, this skill provides the automated tools and expert guidance needed to achieve professional sound quality directly from your command line.

Key Features

3 GitHub stars
Genre-specific mastering presets for EDM, Rock, Pop, and Ambient music
Batch processing capabilities for maintaining consistency across entire albums
Automated loudness analysis and optimization to industry standards (-14 LUFS)
Support for custom mastering overrides via configuration files
True peak limiting and clipping prevention for high-fidelity delivery

Use Cases

Preparing AI-generated music from platforms like Suno for professional release
Verifying that audio files meet technical delivery requirements for distribution platforms
Normalizing loudness across a multi-track album to ensure a cohesive listening experience

About

Key Features

3 GitHub stars
Genre-specific mastering presets for EDM, Rock, Pop, and Ambient music
Batch processing capabilities for maintaining consistency across entire albums
Automated loudness analysis and optimization to industry standards (-14 LUFS)
Support for custom mastering overrides via configuration files
True peak limiting and clipping prevention for high-fidelity delivery

Use Cases

Preparing AI-generated music from platforms like Suno for professional release
Verifying that audio files meet technical delivery requirements for distribution platforms
Normalizing loudness across a multi-track album to ensure a cohesive listening experience