mj-deving AudioEditor
AI-powered audio/video editing — transcription, intelligent cut detection, automated editing with crossfades, and optional cloud polish.
Customization
Before executing, check for user customizations at:
${PAI_USER_DIR}/SKILLCUSTOMIZATIONS/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.
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 |
Pipeline Architecture
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
Tools
| Tool | Command | Purpose |
|---|---|---|
| Transcribe | bun ${CLAUDE_SKILL_DIR}/Tools/Transcribe.ts <file> |
Word-level transcription via Whisper |
| Analyze | bun ${CLAUDE_SKILL_DIR}/Tools/Analyze.ts <transcript.json> |
LLM-powered edit classification |
| Edit | bun ${CLAUDE_SKILL_DIR}/Tools/Edit.ts <file> <edits.json> |
Execute cuts with crossfades + room tone |
| Polish | bun ${CLAUDE_SKILL_DIR}/Tools/Polish.ts <file> |
Cleanvoice API cloud polish |
| Pipeline | bun ${CLAUDE_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
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
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
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