Lecture Digestor — Bundle

The problem

Standard AI summaries often smooth over missing information or replace specific course terminology with generic prose. Students lose the distinction between what the professor said and their own thoughts, leading to notes that are useless for exam retrieval.

What it does

• Converts fragmented lecture notes and slides into structured systems like Cornell notes or concept maps.
• Identifies and explicitly marks [gaps] or [check] points where information is missing or contradictory.
• Separates primary lecture claims from student reactions and internal thoughts using specific tags.
• Generates retrieval hooks and study questions based strictly on the provided material.
• Flags contradictions between student notes and textbook data for manual verification.

Why this beats prompting it yourself

Generic prompts often hallucinate missing details to make a summary feel complete. This skill is programmed to be gap-honest, ensuring you know exactly what you missed in class, while maintaining a strict boundary against generating non-verifiable content.

Use cases

• Transforming messy, rapid-fire lecture transcripts into hierarchical outlines for finals.
• Identifying specific questions to ask a TA by surfacing gaps in existing notes.
• Creating active recall sheets by extracting the questions that the lecture material answers.

Known limitations

Does not invent lecture content from general knowledge unless specifically asked. Requires user confirmation before performing file actions like saving or deleting.