audioeditor

name	AudioEditor
description	AI-powered audio and video editing pipeline: Whisper word-level transcription (insanely-fast-whisper on MPS) → Claude segment classification (KEEP / CUT_FILLER / CUT_FALSE_START / CUT_STUTTER / CUT_DEAD_AIR / CUT_EDIT_MARKER) → ffmpeg execution with 40ms qsin crossfades and room-tone gap fill → optional Cleanvoice API cloud polish for mouth sounds and loudness normalization. Distinguishes rhetorical pauses from accidental ones. Breaths attenuated to 50% volume (not removed). Preview mode (--preview flag) shows proposed cuts without modifying audio. Aggressive mode (--aggressive flag) applies tighter filler detection thresholds. Polish step (--polish flag) uploads to Cleanvoice API for mouth sound removal and loudness normalization — confirm before cloud upload of sensitive content. Pipeline tools: Transcribe.ts, Analyze.ts, Edit.ts, Polish.ts, Pipeline.ts. Single workflow: Clean.md. Requires ANTHROPIC_API_KEY; CLEANVOICE_API_KEY optional for polish step. USE WHEN: clean audio, edit audio, remove filler words, clean podcast, remove ums, cut dead air, polish audio, trim recording, audio cleanup, cut stutters, edit interview recording, preview edits, aggressive clean. NOT FOR video composition or animation (use Remotion).
effort	medium

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: ~/.claude/PAI/USER/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.

Voice Notification

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

1. Send voice notification:

   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 &
   

2. 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.

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

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:

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/PAI/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.