podcast-edit

Transforms raw audio files into polished podcasts via trimming, filler removal, and enhancement.

13|3|Updated May 31, 2026
One-click install
npx skills add https://github.com/enowdev/enowX-Skill --skill podcast-edit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: podcast-edit
Source: https://github.com/enowdev/enowX-Skill/tree/main/skill/skills/podcast-edit
Command: npx skills add https://github.com/enowdev/enowX-Skill --skill podcast-edit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ffmpeg, ffprobe, python3, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the tedious, time-consuming process of editing raw podcast recordings, including trimming unwanted segments, removing filler words, and normalizing audio quality to industry standards.

Core Features & Use Cases

  • Smart Trimming: Automatically detects and removes pre-show and post-show chatter using AI-driven transcription.
  • Filler Removal: Identifies and cuts verbal tics like um, uh, and repeated filler words to improve flow.
  • Audio Enhancement: Applies professional-grade processing including noise reduction, EQ, and loudness normalization to -16 LUFS.
  • Use Case: Use this to transform a raw, hour-long meeting or interview recording into a clean, broadcast-ready episode with consistent volume and no distracting silences.

Quick Start

Use the podcast-edit skill to process the raw audio file named recording.mp3 by removing all filler words and normalizing the loudness to podcast standards.

Frequently Asked Questions about podcast-edit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automatically remove filler words from podcast audio?▼

To remove filler words from podcast audio, this Skill uses AI-driven transcription to identify verbal tics like um and uh, then automatically cuts them to improve flow. It processes long-form recordings to enhance clarity without manual editing.

What is the best way to normalize loudness to podcast standards?▼

The best way to normalize loudness to podcast standards is applying professional-grade audio processing that targets -16 LUFS. This ensures consistent volume levels across the entire episode for broadcast-ready output.

Do I need an OpenAI API key to process and trim raw audio recordings?▼

Yes, you need an OpenAI API key to process and trim raw audio recordings. The Skill requires Whisper-based transcription for intelligent trimming and filler word detection, along with ffmpeg, ffprobe, and python3 dependencies.

Can ffmpeg detect and trim pre-show chatter from raw podcast files?▼

While ffmpeg handles audio processing, AI-driven transcription detects and trims pre-show chatter from raw podcast files. The Skill combines Whisper-based analysis with ffmpeg to intelligently remove unwanted segments.

What audio enhancement techniques are applied when editing podcasts?▼

Audio enhancement techniques applied when editing podcasts include noise reduction, EQ, and loudness normalization to -16 LUFS. These professional-grade processes ensure consistent volume and remove distracting silences from the recording.

Does this audio normalization approach work on hour-long interview recordings?▼

Yes, this audio normalization approach works on hour-long interview recordings. It operates on long-form audio files to improve clarity, remove silence, and ensure consistent loudness levels suitable for broadcast-ready episodes.