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audioeditor

AI-powered audio/video editing — transcription, intelligent cut detection, automated editing with crossfades, and optional cloud polish. USE WHEN clean audio, edit audio, remove filler words, clean podcast, remove ums, fix audio, cut dead air, polish audio, clean recording, transcribe and edit.

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Overview

Install command

npx skills add https://github.com/danielmiessler/Personal_AI_Infrastructure --skill audioeditor

Copy and paste this command into Claude Code to install the skill

Source

danielmiessler/Personal_AI_Infrastructure

Stars14,605

Forks2,073

UpdatedFebruary 28, 2026 at 13:24

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SKILL.md

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name	AudioEditor
description	AI-powered audio/video editing — transcription, intelligent cut detection, automated editing with crossfades, and optional cloud polish. USE WHEN clean audio, edit audio, remove filler words, clean podcast, remove ums, fix audio, cut dead air, polish audio, clean recording, transcribe and edit.

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.

Send voice notification:

curl -s -X POST http://localhost:8888/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.

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/skills/Utilities/AudioEditor/Tools/Transcribe.ts <file>`	Word-level transcription via Whisper
Analyze	`bun ~/.claude/skills/Utilities/AudioEditor/Tools/Analyze.ts <transcript.json>`	LLM-powered edit classification
Edit	`bun ~/.claude/skills/Utilities/AudioEditor/Tools/Edit.ts <file> <edits.json>`	Execute cuts with crossfades + room tone
Polish	`bun ~/.claude/skills/Utilities/AudioEditor/Tools/Polish.ts <file>`	Cleanvoice API cloud polish
Pipeline	`bun ~/.claude/skills/Utilities/AudioEditor/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

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Source

danielmiessler

danielmiessler/Personal_AI_Infrastructure

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Useful forSOC

Computer Occupations, All OtherComputer and Mathematical Occupations15-1299L4

Film and Video EditorsL4 Software DevelopersL4 Sound Engineering TechniciansL4

name	AudioEditor
description	AI-powered audio/video editing — transcription, intelligent cut detection, automated editing with crossfades, and optional cloud polish. USE WHEN clean audio, edit audio, remove filler words, clean podcast, remove ums, fix audio, cut dead air, polish audio, clean recording, transcribe and edit.

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.

Send voice notification:

curl -s -X POST http://localhost:8888/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.

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/skills/Utilities/AudioEditor/Tools/Transcribe.ts <file>`	Word-level transcription via Whisper
Analyze	`bun ~/.claude/skills/Utilities/AudioEditor/Tools/Analyze.ts <transcript.json>`	LLM-powered edit classification
Edit	`bun ~/.claude/skills/Utilities/AudioEditor/Tools/Edit.ts <file> <edits.json>`	Execute cuts with crossfades + room tone
Polish	`bun ~/.claude/skills/Utilities/AudioEditor/Tools/Polish.ts <file>`	Cleanvoice API cloud polish
Pipeline	`bun ~/.claude/skills/Utilities/AudioEditor/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