content-digest

To summarize a YouTube transcript and PDF into one document, you provide the URLs or file paths to generate a single Markdown digest. The output is saved under research/digests categorized by content type.

A quiz-first learning flow for content summarization generates a Markdown digest structured with a pre-quiz, main quiz, and elaboration phases. This mechanism reinforces understanding by testing knowledge directly within the extracted summary.

Yes, you can extract and clean web page content using bash and python3. The process relies on common scripting tools like sed, grep, and tr to process webpages, PDFs, and YouTube transcripts into structured Markdown.

Yes, you need yt-dlp to process YouTube videos for Markdown digests. The extraction workflow depends on yt-dlp alongside python3 and bash to pull transcripts and format them into a self-contained MD file.

You connect new PDF summaries with prior learning research through foundation expansion and schema linking. This builds a knowledge network within the digest, linking the newly extracted content with your existing research foundation.

Limitations of using scripting tools for web content extraction include dependency on python3, bash, sed, grep, and tr environments. The process requires a Task Agent framework to execute the multi-phase workflow and output the final Markdown file.