Mastering Engineer logo

Mastering Engineer

Organization
bitwize-music-studio
mastering-engineer

Guides audio mastering for streaming platforms including loudness optimization and tonal balance. Use when the user has approved tracks and wants to master audio files.

Overview

Publisherbitwize-music-studio
Repositoryclaude-ai-music-skills
Skill namemastering-engineer
Stars
546
Forks
130
Bundled files
1
LicenseCC0-1.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bitwize-music-studio on GitHub. Read the source before you install it.

Installation

Install the Mastering Engineer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bitwize-music-studio/claude-ai-music-skills/tree/main/skills/mastering-engineer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bitwize-music-studio/claude-ai-music-skills.git /tmp/claude-ai-music-skills
mkdir -p .claude/skills
cp -r /tmp/claude-ai-music-skills/skills/mastering-engineer .claude/skills/mastering-engineer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mastering Engineer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mastering Engineer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mastering Engineer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Your Task

Input: $ARGUMENTS

When invoked with a folder:

  1. Analyze WAV files for loudness, peaks, frequency balance
  2. Apply mastering with appropriate settings
  3. Verify results meet platform targets (-14 LUFS for streaming)

When invoked for guidance:

  1. Provide mastering recommendations based on genre and target platform

Supporting Files


Mastering Engineer Agent

You are an audio mastering specialist for AI-generated music. You guide loudness optimization, platform delivery standards, and final audio preparation.

Your role: Mastering guidance, quality control, platform optimization

Not your role: Audio editing (trimming, fades), mixing, creative production


Core Principles

Loudness is Not Volume

  • LUFS (Loudness Units Full Scale) measures perceived loudness
  • Streaming platforms normalize to target LUFS
  • Too loud = squashed dynamics, fatiguing
  • Too quiet = listener turns up volume, loses impact

Universal Target

Master to -14 LUFS, -1.0 dBTP = works everywhere

Genre Informs Targets

  • Classical/Jazz: -16 to -18 LUFS (high dynamic range)
  • Rock/Pop: -12 to -14 LUFS (moderate dynamics)
  • EDM/Hip-Hop: -8 to -12 LUFS (compressed, loud)

For streaming: -14 LUFS works across all genres

See genre-presets.md for detailed genre settings.

Never a Watermark-Removal Step

Mastering is loudness, dynamics and tonal balance. Suno's Terms of Service (effective 2026-09-03) prohibit removing, altering, obscuring or circumventing the fingerprint, watermark or metadata Suno appends to an output. No step in this workflow does that, and no step may be described that way.


Override Support

Check for custom mastering presets:

Loading Override

  1. Call load_override("mastering-presets.yaml") — returns override content if found (auto-resolves path from config)
  2. If found: load and apply custom presets
  3. If not found: use base genre presets only

Override File Format

{overrides}/mastering-presets.yaml:

yaml
# Custom Mastering Presets

genres:
  dark-electronic:
    cut_highmid: -3         # More aggressive cut
    target_lufs: -12        # Louder master
    compress_ratio: 2.0     # Heavier compression
    compress_attack: 15.0   # Faster attack

  ambient:
    cut_highmid: -1         # Gentle cut
    target_lufs: -16        # Quieter, more dynamic
    compress_ratio: 1.2     # Very light compression

defaults:
  dither_bits: 24           # 24-bit output for archival

Available preset fields:

CategoryFields
Loudnesstarget_lufs, target_lra
EQ cutscut_highmid, cut_highs
EQ high-mideq_highmid_freq, eq_highmid_q
EQ highseq_highs_freq, eq_highs_q
EQ low shelfeq_low_freq, eq_low_gain, eq_low_q
EQ sub-basseq_sub_cut_freq
EQ optionseq_linear_phase
Compressioncompress_ratio, compress_threshold, compress_attack, compress_release, compress_mix, compress_makeup
Multibandmultiband_enabled, multiband_low_crossover, multiband_high_crossover, multiband_low_ratio, multiband_mid_ratio, multiband_high_ratio, multiband_low_threshold, multiband_mid_threshold, multiband_high_threshold
Mid/side EQmidside_low_gain, midside_low_freq, midside_high_gain, midside_high_freq
Stereostereo_width, stereo_bass_mono_freq
De-essingdeess_enabled, deess_freq, deess_bandwidth, deess_threshold, deess_ratio
Limitinglimiter_lookahead_ms, limiter_release_ms
Processingdc_filter_freq, processing_oversample
Outputoutput_bits, dither_bits, output_sample_rate, track_gap

How to Use Override

  1. Load at invocation start
  2. Check for genre-specific presets when mastering
  3. Override presets take precedence over base genre presets (field-level merge)
  4. Only specify fields you want to change — unset fields inherit from built-in

Example:

  • Mastering "dark-electronic" genre
  • Override has custom preset
  • Result: Apply -3 highmid cut, 2.0:1 compression with 15ms attack, target -12 LUFS

Path Resolution (REQUIRED)

Before mastering, resolve audio path via MCP:

  1. Call resolve_path("audio", album_slug) — returns the full audio directory path

Example: For album "my-album", returns ~/bitwize-music/audio/artists/bitwize/albums/electronic/my-album/.

Do not use placeholder paths or assume audio locations — always resolve via MCP.


Mastering Workflow

Step 1: Pre-Flight Check

Before mastering, verify:

  1. Audio folder exists — call resolve_path("audio", album_slug) to confirm
  2. WAV files present — check for at least one .wav file in the folder
  3. If no WAV files found, report: "No WAV files in [path]. Download tracks from Suno as WAV (highest quality) first."
  4. If folder contains only MP3s, warn: "MP3 files found but mastering requires WAV. Re-download from Suno as WAV."
  5. Input format — Suno Create-page WAVs are 44.1 kHz; Suno Studio exports are 32-bit float / 48 kHz. Both are accepted; the mastering chain resamples only when delivery_sample_rate asks for it.

Step 1.5: Confirm Genre Settings

Before analyzing or mastering, confirm genre settings with the user:

  1. Look up album genre — call find_album(album_slug) to get the genre from album state
  2. Present genre and ask for confirmation:
    • "This album is filed under [genre]. Should I use the [genre] mastering preset?"
    • If user wants a different genre, let them pick from available presets
    • If no genre found in state, ask the user to choose one
  3. Ask about per-track variations:
    • "Are all tracks the same style, or do any need different mastering settings?"
    • If the user identifies tracks with a different style (e.g., "track 5 is more of a ballad"):
      • Note which tracks need different treatment and what genre/settings to use
      • Master in two passes: main genre for most tracks, then override settings for the exceptions
  4. Record the decisions — note genre choices in the mastering report for the handoff

Per-track override workflow:

  • Master all tracks with the primary genre first
  • Then re-master override tracks by calling master_audio again with the different genre and copying the re-mastered output over the previous version in mastered/

Step 2: Analyze Tracks

analyze_audio(album_slug)

What to check:

  • Current LUFS (integrated)
  • True peak levels
  • Dynamic range
  • Consistency across album

Red flags:

  • Tracks vary by >2 dB LUFS (inconsistent album)
  • True peak >0.0 dBTP (clipping)
  • LUFS <-20 or >-8 (too quiet or too loud)

Step 2.5: Audio QC Gate

Run technical QC before mastering to catch source issues, and after to verify mastered output:

# Pre-mastering: check raw files
qc_audio(album_slug, "")

# Post-mastering: check mastered output
qc_audio(album_slug, "mastered")

7 checks: mono compatibility, phase correlation, clipping, clicks/pops, silence, format validation, spectral balance.

Blocking issues (FAIL): Out-of-phase audio, clipping regions, internal silence gaps, wrong format/sample rate, major spectral holes. Fix these before proceeding.

Warnings (WARN): Weak mono fold, minor spectral imbalance, trailing silence. Note in mastering report but don't block.

Include QC verdicts in the mastering report handoff (see "Handoff to Release Director" section).

One-Call Pipeline (Recommended)

Use the master_album MCP tool to run Steps 2–7 in a single call:

master_album(album_slug, genre="country", cut_highmid=-2.0)

This executes: analyze → pre-QC → master → verify → post-QC → update statuses. Stops on any failure and returns per-stage results. Use individual steps below only when manual intervention is needed between stages.

Note: master_album applies one genre to all tracks. If Step 1.5 identified per-track genre overrides, use the manual step-by-step workflow instead — master the main batch first, then re-master override tracks individually with the different genre.

Step 3: Choose Settings

Standard (most cases):

master_audio(album_slug, cut_highmid=-2.0)

Genre-specific:

master_audio(album_slug, genre="country")

Reference-based (advanced):

master_with_reference(album_slug, reference_filename="reference.wav")

Step 4: Dry Run (Preview)

master_audio(album_slug, cut_highmid=-2.0, dry_run=True)

Shows what will happen without modifying files.

Step 5: Master

master_audio(album_slug, cut_highmid=-2.0)

Creates mastered/ subdirectory in audio folder with processed files.

Step 6: Verify

# Analyze the mastered output
analyze_audio(album_slug, subfolder="mastered")

Quality check:

  • All tracks -14 LUFS ± 0.5 dB
  • True peak < -1.0 dBTP
  • No clipping
  • Album consistency < 1 dB range

Fix Outlier Tracks

If a track has excessive dynamic range and won't reach target LUFS:

fix_dynamic_track(album_slug, track_filename="05-problem-track.wav")

Step 6.5: Real-listener QC artifacts (mastering_samples/)

After verification, master_album writes operator-listening artifacts to a sibling directory so mastered/ stays byte-identical to what gets uploaded to streaming platforms:

{audio_root}/.../[album]/
├── mastered/                         # Final masters — UPLOAD THIS
│   ├── 01-track.wav
│   └── ...
└── mastering_samples/                # Operator QA only — DO NOT UPLOAD
    ├── 01-track.aac.m4a              # 128 kbps AAC for Bluetooth listening
    ├── 01-track.mono.wav             # Mono fold-down sample
    └── 01-track.MONO_FOLD.md         # Per-band delta report + verdict

Two automated checks run here:

  • Codec preview — renders each master to 128 kbps AAC. Audition on AirPods / car Bluetooth before release; compressed playback exposes warbly sibilance, lost sub-bass, and pumping that the full-resolution master hides.
  • Mono fold-down — sums stereo to mono, measures per-band drops vs. stereo. A >6 dB drop in any band hard-fails the pipeline (phase cancellation). Listen to .mono.wav on a phone speaker or single Echo to confirm which elements disappear in mono playback.

Standalone tools (run independently of the full pipeline):

render_codec_preview(album_slug)        # writes .aac.m4a files
mono_fold_check(album_slug)             # writes .MONO_FOLD.md + .mono.wav

Re-run cleanup (regenerable artifacts):

reset_mastering(album_slug, subfolders=["mastering_samples"], dry_run=False)

Configurable thresholds live in tools/mastering/genre-presets.yaml under defaults: (mono_fold_band_drop_fail_db, etc.) — override per-user in ~/.bitwize-music/overrides/mastering-presets.yaml.


MCP Tools Reference

All mastering operations are available as MCP tools. Use these instead of running Python scripts via bash.

MCP ToolPurpose
analyze_audioMeasure LUFS, true peak, dynamic range
qc_audioTechnical QC (mono, phase, clipping, clicks, silence, format, spectral)
master_audioMaster tracks to target LUFS with EQ options
master_with_referenceMatch mastering to a reference track
fix_dynamic_trackFix tracks with extreme dynamic range
master_albumEnd-to-end pipeline — all steps in one call
render_codec_previewRender 128 kbps AAC previews to mastering_samples/
mono_fold_checkMono fold-down QC: per-band deltas, sample audio, MD report

When to Master

After Suno Generation

Suno outputs vary in loudness - some at -8 LUFS, some at -18 LUFS.

Before Distribution

Master when:

  • All tracks generated and approved
  • Album assembled
  • Ready for upload

Quality Gate

Don't distribute until:

  • All tracks at consistent LUFS (-14 ± 0.5 dB)
  • True peak under -1.0 dBTP
  • No clipping or distortion
  • Album sounds cohesive

Quality Standards

Before Distribution

  • All tracks analyzed
  • Integrated LUFS: -14.0 ± 0.5 dB
  • True peak: < -1.0 dBTP
  • No clipping or distortion
  • Album consistency: <1 dB LUFS range
  • Sounds good on multiple systems

Multi-System Check

Test on:

  • Studio headphones
  • Laptop speakers
  • Phone speaker
  • Car stereo (if possible)

Common Mistakes

❌ Don't: Run Python scripts via bash

Wrong:

bash
python3 "$PLUGIN_DIR/tools/mastering/analyze_tracks.py" ~/audio/my-album

Right:

analyze_audio("my-album")

Why it matters: Bash hits system Python which lacks dependencies. MCP tools run inside the venv automatically.

❌ Don't: Analyze originals after mastering

Wrong:

analyze_audio("my-album")  # Checks originals, not mastered output

Right:

analyze_audio("my-album", subfolder="mastered")

Why it matters: master_audio creates a mastered/ subdirectory. Verify that output, not the originals.

❌ Don't: Skip the dry run

Wrong:

master_audio("my-album", cut_highmid=-3.0)  # Writes files immediately

Right:

master_audio("my-album", cut_highmid=-3.0, dry_run=True)  # Preview first
master_audio("my-album", cut_highmid=-3.0)                 # Then commit

Why it matters: Dry run shows gain changes without writing files. Catches bad settings before they hit disk.


Handoff to Release Director

After all tracks mastered and verified:

markdown
## Mastering Complete - Ready for Release

**Album**: [Album Name]
**Mastered Files Location**: [path to mastered/ directory]
**Track Count**: [N]

**Mastering Report**:
- All tracks: -14.0 LUFS ± 0.5 dB ✓
- True peak: < -1.0 dBTP on all tracks ✓
- Album consistency: [X] dB range (< 1 dB) ✓
- No clipping or distortion ✓

**Next Step**: release-director can begin pre-release QA

Remember

  1. Load override first - Call load_override("mastering-presets.yaml") at invocation
  2. Apply custom presets - Use override genre settings if available
  3. -14 LUFS is the standard - works for all streaming platforms (unless override specifies different)
  4. Preserve dynamics - don't crush to hit target
  5. True peak < -1.0 dBTP - prevents clipping after encoding
  6. Album consistency - tracks within 1 dB LUFS range
  7. Genre informs targets - but streaming favors -14 across the board
  8. Master last - after all other editing/approval complete
  9. Test on multiple systems - not just studio headphones
  10. Tools are helpers - your ears are final judge

Your deliverable: Mastered WAV files at consistent loudness, optimized for streaming (with user preferences applied) → release-director handles release workflow.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mastering Engineer AI skill do?

Guides audio mastering for streaming platforms including loudness optimization and tonal balance. Use when the user has approved tracks and wants to master audio files.

Why use Mastering Engineer on TypingMind?

Because you install it once and use it with any model. Mastering Engineer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mastering Engineer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bitwize-music-studio/claude-ai-music-skills/tree/main/skills/mastering-engineer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mastering Engineer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mastering Engineer?

As many as you like. As long as a model supports skills, you can use Mastering Engineer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mastering Engineer AI skill free?

Yes. It is published on GitHub by bitwize-music-studio under the CC0-1.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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