Open Course Notes Generator

Problem

Open lectures, recorded courses, and teaching videos often contain dense information, but raw transcripts or in-class notes remain fragmented. Asking a model to summarize a video link is unreliable here: the skill cannot access or play the link, and it does not infer unseen video content. The practical task is to turn available artifacts such as an outline, transcript, personal notes, or spoken summary into structured notes that can be reviewed, with clear cues for which sections are worth rewatching.

How It Works and Core Capabilities

It organizes the material by course structure rather than producing a verbatim transcript:

• Chapterization: splits the course into introduction, core explanation, examples, and summary, then generates summaries, key points, and definitions.
• Key extraction: identifies core concepts, definitions, formulas, and notable statements, and ranks them as core, important, or supplementary.
• Time cues: marks key timestamps when the user provides transcript timestamps or outline hints, making rewatching easier.
• Review output: produces Q&A cards, checklists, learning roadmaps, and cross-lesson knowledge connections.

A typical flow is: receive course title, outline, transcript, or note fragments → identify logical structure and split chapters → generate structured notes → add self-test questions and follow-up learning suggestions. For lectures longer than two hours, segmented processing is the better fit.

Boundaries and Notes

This skill does not download video, translate subtitles, write original course scripts, or perform literature review. Note quality depends on the completeness of the supplied material; a bare URL is not enough to generate useful output. For highly specialized content, it only structures the provided information and should not be treated as an academic verification tool.