podcast-marketing

Repurpose podcast content by processing episode transcripts to automatically generate social media posts, show notes, clips, and quote cards. The system extracts key content atoms from the audio or video to produce engaging marketing materials.

Content generation works by extracting key content atoms from podcast transcripts and scoring them for relevance and engagement. An AI model processes these transcripts to output customized social media posts, blog articles, and newsletters.

Yes, generating podcast clips and marketing assets requires Python 3.10+ along with libraries like youtube-transcript-api, yt-dlp, Pillow, ffmpeg, and an AI model for content generation. These dependencies handle video processing and image generation.

Yes, you can customize generated podcast show notes and social media content based on brand voice, audience, and platform preferences. The system tailors the output marketing formats to match your specific platform requirements.

The best way to convert YouTube podcast episodes into blog articles is to use a skill that accepts a YouTube link, fetches the transcript via youtube-transcript-api, and uses an AI model to generate textual content automatically from the episode.

Video clips may not generate if ffmpeg or yt-dlp dependencies are missing from your Python 3.10+ environment. Proper setup of these specific video processing libraries is required to extract and generate visual podcast clips successfully.