onboard

name	onboard
description	First-run setup for the audio-production plugin. Provisions the persistent user-data directory, registers the user's primary microphone, captures a 3-min sample, profiles it, seeds default EQ presets, and produces 1-min A/B auditions. Run once before using profile-voice, suggest-eq, or apply-preset. Re-run any time to refresh.
disable-model-invocation	true
allowed-tools	Bash(mkdir *), Bash(cp *), Bash(test *), Bash(ls *), Bash(cat *), Bash(date *), Bash(ffprobe *), Bash(ffmpeg *), Bash(python3 *), Bash(pip *), Bash(pip3 *), Read, Write

Onboard — Audio-Production Plugin

This skill provisions the plugin's persistent user-data directory and walks the user through registering their first microphone profile.

Profiles are mic-bound: each registered mic gets its own sample, analysis, and presets, so the user can switch microphones without retraining the whole pipeline.

Data directory convention

Resolve the plugin's data directory as $CLAUDE_USER_DATA/audio-production/ if CLAUDE_USER_DATA is set; otherwise $XDG_DATA_HOME/claude-plugins/audio-production/ if XDG_DATA_HOME is set; otherwise ~/.local/share/claude-plugins/audio-production/.

Layout:

<data-dir>/
  config.json                 # defaults — loudness target, default_mic_id, …
  mics/
    <mic-id>/
      metadata.json           # mic name, make/model, interface, room notes
      sample.wav              # 3-min canonical sample
      sample-source.txt       # original source path + offset
      analysis.json           # spectral profile
  presets/
    <name>.json               # has mic_id field
  auditions/
    <preset>__<mic-id>__<ts>/ # before.wav / after.wav / diff.txt
  state/                      # runtime state

Never write plugin data under ~/.claude/. That's the install surface and is overwritten on plugin update.

Procedure

1. Resolve and create the data dir

PLUGIN_DATA_DIR="${CLAUDE_USER_DATA:-${XDG_DATA_HOME:-$HOME/.local/share}/claude-plugins}/audio-production"
mkdir -p "$PLUGIN_DATA_DIR/mics" "$PLUGIN_DATA_DIR/presets" "$PLUGIN_DATA_DIR/auditions" "$PLUGIN_DATA_DIR/state"

2. Migrate any legacy data

If <data-dir>/voice/ exists from a pre-mic-aware version of the plugin:

• Ask the user for an id and friendly name to attach to the existing data (suggest default if they don't care).
• Move <data-dir>/voice/sample.wav → <data-dir>/mics/<mic-id>/sample.wav.
• Move <data-dir>/voice/analysis.json → <data-dir>/mics/<mic-id>/analysis.json.
• Synthesise a <data-dir>/mics/<mic-id>/metadata.json with whatever the user can recall.
• For each existing preset in <data-dir>/presets/, add a mic_id field pointing at the new mic.
• Remove the empty <data-dir>/voice/ directory.

3. Write or update config.json

If absent, create:

{
  "loudness_target_lufs": -16,
  "true_peak_ceiling_dbtp": -1,
  "default_workspace_parent": "~/repos/github/my-repos",
  "default_use_case": "podcast",
  "default_mic_id": null
}

If present, fill in any missing fields with defaults; leave existing values alone.

4. Verify dependencies

Hand off to /audio-production:install-deps. That skill provisions a plugin-owned uv venv at <data-dir>/venv/, installs all Python tooling into it, and verifies system binaries (ffmpeg, etc.). Every Python-using command in this plugin invokes <data-dir>/venv/bin/python directly — system Python is never touched.

If the user declines optional installs, onboarding can continue as long as ffmpeg, the venv, and librosa/numpy/scipy are present (required for mic registration and voice profiling). deepfilternet is only needed for denoise and polish --mode=noisy, so its absence is non-blocking for onboarding.

5. Register the mic

Hand off to /audio-production:add-mic. Walk the user through:

• A kebab-case mic id (used as directory name). Suggest something descriptive: sm7b-desk, at2020-usb, lav-zoom-h6.
• A friendly name for display.
• Make / model (e.g. "Shure SM7B").
• Interface (optional — e.g. "Focusrite Scarlett 2i2", "USB direct", "Zoom H6 channel 1").
• Source recording — path to a clean voice sample of the user speaking with this mic. At least 3 minutes preferred.
• Environment notes (optional — room treatment, mic distance, gain setting).

add-mic extracts a 3-min sample from the source via /audio-production:extract-sample, runs /audio-production:profile-voice, seeds presets via /audio-production:suggest-eq, and sets default_mic_id if it was unset.

6. Audition the seeded presets

For the podcast, vocals, and spoken-word presets just created, invoke /audio-production:audition-preset to produce 1-min A/B WAV pairs the user can play back to evaluate the suggestion.

7. Report

• Data directory path.
• Registered mic summary (id, name, make/model).
• Sample location and a one-line read of the analysis (pitch median, brightness, mud-vs-sibilance).
• Presets created and their audition paths.
• Note that re-running this skill or /audio-production:add-mic adds another mic profile rather than overwriting.
• Note that all plugin user data lives under one root and can be backed up by copying that directory.

Notes

• Public plugin → never reference user-specific MCPs. All audio analysis runs via standard CLI tools (ffmpeg, python3 + librosa).
• If the user declines to register a mic, still create the data dir and config.json, leave default_mic_id null, and tell them they can register one any time with /audio-production:add-mic.