Guides audio mastering for streaming platforms including loudness optimization and tonal balance.

CC0-1.0Auto-check: notes

Install Mastering Engineer

skills CLI
$ npx skills add bitwize-music-studio/claude-ai-music-skills --skill mastering-engineer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install bitwize-music-studio/claude-ai-music-skills mastering-engineer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/bitwize-music-studio/claude-ai-music-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mastering-engineer .claude/skills/mastering-engineer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mastering-engineer
GitHub stars
540
Token cost
~3.9k tokens
SKILL.md length
1,524 words
Files
2
Skills in repo
51
Repo updated
First seen
Licence
CC0-1.0

At a glance

Guides audio mastering for streaming platforms including loudness optimization and tonal balance.

  • Works in 9 steps: Pre-Flight Check → 5: Confirm Genre Settings → Analyze Tracks → …
  • The user has approved tracks and wants to master audio files
  • SKILL.md covers Your Task, Supporting Files, Core Principles and Override Support, plus 4 more sections
  • Calls python3

What it does

Mastering Engineer is an agent skill from bitwize-music-studio/claude-ai-music-skills. 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.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `genre-presets.md`).

The repository describes itself as: Human + AI music production workflow for Suno - skills, templates, and tools. The licence is CC0-1.0.

When your agent uses it

  • The user has approved tracks and wants to master audio files

Example prompts

  • “Use the mastering-engineer skill to guide audio mastering for streaming platforms including loudness optimization and tonal balance”
  • “/mastering-engineer”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Edit, Write, Grep, Glob, Bash, bitwize-music-mcp

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Pre-Flight Check
  2. 5: Confirm Genre Settings
  3. Analyze Tracks
  4. 5: Audio QC Gate
  5. Choose Settings
  6. Dry Run (Preview)
  7. Master
  8. Verify
  9. 5: Real-listener QC artifacts (mastering_samples/)

What it can do on your machine

Read from SKILL.md and the folder at commit 58f32c2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Edit
    • Write
    • Grep
    • Glob
    • Bash
    • bitwize-music-mcp

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mastering Engineer loads about 3.9k tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 1,524 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Edit, Write, Grep, Glob, Bash, bitwize-music-mcp

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from bitwize-music-studio/claude-ai-music-skills at commit 58f32c2, republished under its CC0-1.0 licence (© bitwize-music-studio). 1,524 words, ~3,856 tokens.

Download SKILL.mdSave it as .claude/skills/mastering-engineer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
mastering-engineer
description
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.
allowed-tools
Read, Edit, Write, Grep, Glob, Bash, bitwize-music-mcp
argument-hint
<folder-path or "master for [platform]">
model
sonnet
effort
low
prerequisites
import-audio
requirements.python
matchering, pyloudnorm, scipy, numpy, soundfile

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).

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.

Show full SKILL.md (590 more words)Show less
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.

© bitwize-music-studio, CC0-1.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/mastering-engineer of bitwize-music-studio/claude-ai-music-skills.

  • SKILL.md
  • genre-presets.md

Open the folder on GitHubat commit 58f32c2

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Questions about Mastering Engineer

What does Mastering Engineer do?

Guides audio mastering for streaming platforms including loudness optimization and tonal balance. Mastering Engineer is an agent skill from bitwize-music-studio/claude-ai-music-skills. Guides audio mastering for streaming platforms including loudness optimization and tonal balance.

When should I use Mastering Engineer?

Mastering Engineer fits situations like: the user has approved tracks and wants to master audio files.

How do I install Mastering Engineer in Claude Code?

Run `npx skills add bitwize-music-studio/claude-ai-music-skills --skill mastering-engineer -a claude-code`. Or copy the skill folder (skills/mastering-engineer in bitwize-music-studio/claude-ai-music-skills) into .claude/skills/mastering-engineer in your project. Claude Code loads it when a task matches its description.

How do I install Mastering Engineer in Codex?

Run `npx skills add bitwize-music-studio/claude-ai-music-skills --skill mastering-engineer -a codex`. Or copy the skill folder (skills/mastering-engineer in bitwize-music-studio/claude-ai-music-skills) into .agents/skills/mastering-engineer in your project. Codex loads it when a task matches its description.

Can I use Mastering Engineer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add bitwize-music-studio/claude-ai-music-skills --skill mastering-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mastering-engineer, .gemini/skills/mastering-engineer, .github/skills/mastering-engineer and .opencode/skills/mastering-engineer in your project.

What does Mastering Engineer need to run?

Going by SKILL.md and its folder, Mastering Engineer needs the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Edit, Write, Grep, Glob, Bash, bitwize-music-mcp.

Does Mastering Engineer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mastering Engineer safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Mastering Engineer use?

Mastering Engineer is published under the CC0-1.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mastering Engineer use?

About 3.9k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mastering Engineer?

Skills that share tags, products or a category with Mastering Engineer: Platform Engineering (sickn33/agentic-awesome-skills, 47k stars), Llmops Platform Engineering (sickn33/agentic-awesome-skills, 47k stars), Agent Platform RAG Engine Management (google/skills, 21k stars) and AI Engineering Course Guide (rohitg00/ai-engineering-from-scratch, 66k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mastering Engineer?

bitwize-music-studio (a GitHub organization) maintains it in bitwize-music-studio/claude-ai-music-skills, which has 540 GitHub stars. The repository holds 51 skills in this directory. The repository was last updated on October 7, 2026.

Source: bitwize-music-studio/claude-ai-music-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.