AudioEditor

Customization

Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/AudioEditor/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

Voice Notification

You MUST send this notification BEFORE doing anything else when this skill is invoked.

Send voice notification:

bash

curl -s -X POST http://localhost:31337/notify \
  -H "Content-Type: application/json" \
  -d '{"message": "Running the WORKFLOWNAME workflow in the AudioEditor skill to ACTION"}' \
  > /dev/null 2>&1 &

Output text notification:
Running the **WorkflowName** workflow in the **AudioEditor** skill to ACTION...

This is not optional. Execute this curl command immediately upon skill invocation.

What It Does

Cleans recorded audio automatically — strips filler words, false starts, stutters, and dead air, attenuates breaths, and crossfades every cut. It transcribes the file at the word level, has Claude classify each segment (KEEP, CUT_FILLER, CUT_FALSE_START, CUT_STUTTER, CUT_DEAD_AIR), then executes the cuts with ffmpeg. An optional Cleanvoice pass adds final polish. Modes: --preview, --aggressive, --polish.

The Problem

Cleaning a recording by hand means scrubbing a waveform for every "um," half-started sentence, and three-second silence, then crossfading each cut so it doesn't click. It's slow and tedious, and a blunt auto-tool over-cuts — it kills the rhetorical pause along with the accidental one, or leaves an audible seam where it spliced. This pipeline tells deliberate pauses apart from dead air, fills gaps with room tone, and crossfades each edit, so the output sounds clean rather than chopped.

How It Works

Whisper produces word-level timestamps, Claude classifies each segment (distinguishing rhetorical emphasis from accidental repetition), and ffmpeg executes the cuts with 40ms qsin crossfades, room-tone gap fill, and breath attenuation at 50% volume rather than removal. An optional Cleanvoice API pass handles mouth-sound removal, residual filler, and loudness normalization.

Pipeline

text

Audio Input
    |
[Transcribe] Whisper word-level timestamps (insanely-fast-whisper on MPS)
    |
[Analyze] Claude classifies each segment:
    |   KEEP / CUT_FILLER / CUT_FALSE_START / CUT_EDIT_MARKER / CUT_STUTTER / CUT_DEAD_AIR
    |   Distinguishes rhetorical emphasis from accidental repetition
    |
[Edit] ffmpeg executes cuts:
    |   - 40ms qsin crossfades at every edit point
    |   - Room tone extraction and gap filling
    |   - Breath attenuation (50% volume, not removal)
    |
[Polish] (optional) Cleanvoice API final pass:
        - Mouth sound removal
        - Remaining filler detection
        - Loudness normalization

Output: cleaned MP3/WAV

Workflow Routing

Workflow	Trigger	File
Clean	"clean audio", "edit audio", "remove filler words", "clean podcast", "remove ums", "cut dead air", "polish audio"	`Workflows/Clean.md`

Tools

Tool	Command	Purpose
Transcribe	`bun ${LIFEOS_SKILL_DIR}/Tools/Transcribe.ts <file>`	Word-level transcription via Whisper
Analyze	`bun ${LIFEOS_SKILL_DIR}/Tools/Analyze.ts <transcript.json>`	LLM-powered edit classification
Edit	`bun ${LIFEOS_SKILL_DIR}/Tools/Edit.ts <file> <edits.json>`	Execute cuts with crossfades + room tone
Polish	`bun ${LIFEOS_SKILL_DIR}/Tools/Polish.ts <file>`	Cleanvoice API cloud polish
Pipeline	`bun ${LIFEOS_SKILL_DIR}/Tools/Pipeline.ts <file> [--polish]`	Full end-to-end pipeline

API Keys Required

Service	Env Var	Where to Get
Anthropic (for analyze step)	`ANTHROPIC_API_KEY`	Already set via Claude Code
Cleanvoice (for polish step, optional)	`CLEANVOICE_API_KEY`	cleanvoice.ai Dashboard Settings API Key

Examples

Example 1: Clean a podcast recording

text

User: "clean up the audio on this podcast file"
-> Invokes Clean workflow
-> Runs full pipeline: transcribe -> analyze -> edit
-> Outputs cleaned MP3 with filler words, stutters, and dead air removed

Example 2: Preview edits before applying

text

User: "show me what edits you'd make to this recording"
-> Invokes Clean workflow with --preview flag
-> Transcribes and analyzes, shows proposed edits without modifying audio
-> User reviews edit list, then runs again to apply

Example 3: Aggressive clean with cloud polish

text

User: "aggressively clean this audio and polish it"
-> Invokes Clean workflow with --aggressive --polish flags
-> Tighter thresholds for filler detection
-> Cleanvoice API pass for mouth sounds and normalization

Gotchas

Transcription accuracy varies with audio quality. Background noise, multiple speakers, and accents reduce accuracy.
Cut detection is heuristic-based. Always preview edits before committing — automated cuts can remove intentional pauses.
Cloud polish uploads audio to external service. Confirm the user is okay with cloud processing for sensitive content.

Execution Log

After completing any workflow, append a single JSONL entry:

bash

echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"AudioEditor","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl

Replace WORKFLOW_USED with the workflow executed, 8_WORD_SUMMARY with a brief input description, and SECONDS with approximate wall-clock time. Log status: "error" if the workflow failed.