What problem does it solve?
The post-production workflow for podcasts—including audio conversion, transcription, and creating chapter markers—is a multi-step, time-consuming, and often manual process. This skill automates the entire audio processing pipeline, saving hours of repetitive work.
Core Features & Use Cases
- Audio Conversion: Automatically converts
.m4a audio files to .mp3 (128kbps) for optimal size and compatibility.
- Local Transcription: Utilizes OpenAI Whisper (base model) for fast, private, and accurate transcription, generating a full JSON transcript.
- Automated Chaptering: Analyzes the generated transcript to identify natural topic transitions and creates 10-15 descriptive chapter markers in both FFmpeg and Podcasting 2.0 formats.
- Chapter Embedding: Embeds the generated chapter metadata directly into the final
.mp3 file, enhancing listener experience in supported podcast apps.
- Use Case: Take a raw audio file from NotebookLM, automatically convert it, transcribe it, generate intelligent chapter markers based on content, and embed them, producing a fully podcast-ready
.mp3 and transcript for publishing.
Quick Start
Use the podcast-audio-processing skill to process the audio file 'Original_Audio.m4a' for the episode at 'podcast/episodes/2025-12-01-topic-slug' with episode slug '2025-12-01-topic-slug'.
