lecture exam notes

Sifting through hours of lecture transcripts to find exam-relevant content is a massive time sink for students. This skill automates the transition from messy, raw audio transcripts to structured study materials and comprehensive exam syntheses. It eliminates the need for manual note-taking during class, ensuring that even unclear audio or technical terminology is captured, verified, and organized for long-term retention.

What it does

• Transcript normalization cleans .vtt, .srt, .docx, and .txt files while preserving time stamps for citation.
• Exam signal extraction identifies assessment language and emphasis directly from the lecturer's words.
• Fact-checked note generation produces structured lecture notes verified against transcripts for numbers, quotes, and names.
• Dynamic synthesis rebuilding updates a course-wide exam prep file after every class, linking concepts across the entire term.
• Study asset creation generates mock papers, cheat sheets, and Anki-compatible flashcards from existing notes.

How it works

1. Initialize course by providing a transcript file or text, specifying the course name and lecture date.
2. Clean and extract using internal scripts to normalize audio artifacts and pull direct exam signals.
3. Draft and verify notes against the transcript, ensuring every figure and quote is sourced and accurate.
4. Rebuild the SYNTHESIS.md file to update the concept index, likely exam questions, and cross-lecture connections.

Frameworks & tools

This skill uses Python 3 standard library scripts for transcript processing and verification. It handles standard subtitle formats and office documents without external API dependencies.

Why this beats prompting it yourself

Generic prompts often hallucinate facts or miss subtle exam hints buried in lecturer emphasis. This skill uses a multi-step verification pipeline that flags unverified numbers and builds a persistent, evolving synthesis file that grows more valuable as the semester progresses.

Use cases

• Exam preparation by generating a mock paper based on ranked signals across ten weeks of lectures.
• Missed class recovery by processing a recording transcript into a note that identifies open questions and new deadlines.
• Active recall by exporting the concept index into Anki flashcards for daily study sessions.

Known limitations

Verification flags figures from slides that aren't in transcripts, requiring manual confirmation. Transcription notes may contain gaps if the source audio has significant loss or ASR loops.