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Fill gaps in a lecture-notetaker .md using the corresponding slide PDF — detect weak explanations, research enhancements, compress to minimum viable insertions, and add cited callouts. Runs end-to-end through all phases with no approval gates once invoked.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | lecture-ftg |
| description | Fill gaps in a lecture-notetaker .md using the corresponding slide PDF — detect weak explanations, research enhancements, compress to minimum viable insertions, and add cited callouts. Runs end-to-end through all phases with no approval gates once invoked. |
| disable-model-invocation | true |
Manual only. Apply this skill only when the user invokes /lecture-ftg or explicitly selects it. Do not load or run it based on conversation context alone.
Identify weak points in a lecture .md, research enhancements, compress to minimum viable insertion, deliver enhanced .md. Auto-proceeds end-to-end — no approval gates.
/mnt/skills/public/pdf-reading/SKILL.md for slide handling.| Input | Format | Required? |
|---|---|---|
| Lecture note | 1 .md from lecture-notetaker | Yes |
| Slides | .pdf of the lecture | Yes |
Multiple .md files: run the skill once per file. Gap detection is local to a single lecture.
| Phase | What happens | Reference |
|---|---|---|
| 1. Identify | Run gap detector across .md + slides; produce internal flag list | references/gap-detector.md |
| 2. Plan | Pick category for each flag; research with web_search; collect candidates with sources | references/enhancement-palette.md |
| 3. Compress | Apply four-clause-cut test, then drop test, to each candidate | references/compression-rules.md |
| 4. Insert | Add surviving enhancements as [!info]+ 🤖 callouts at appropriate locations | references/callout-format.md |
Phase 3 is load-bearing — there is no hard cap on enhancement count, so compression must self-limit. If after compression an enhancement is generic with no specific anchor, drop it.
lecture-notetaker deliberately drops content the slide already covers. If the .md is thin in a section but the slide is rich, the gap is intentional. Always check the slide before flagging. Re-introducing slide-redundant content reverses the notetaker's filter and is the most common failure mode of this skill.
Every enhancement is a [!info]+ 🤖 Enhancement — <category> callout with a mandatory italicised source line. Format and source-line variants in references/callout-format.md. Do not insert AI content as inline prose; do not modify source content; only add callouts.
A .md that's already comprehensive will produce few or no enhancements. That is correct behaviour, not a failure. Present the .md mostly unchanged and note in delivery.
| File | When to read | Governs |
|---|---|---|
references/gap-detector.md | Phase 1 | Six gap signals, anti-patterns, user-disposition defaults, edge cases |
references/enhancement-palette.md | Phase 2 | Eight categories, when each fits, decision rule for overlapping fits |
references/compression-rules.md | Phase 3 | Four-clause-cut test, drop test, worked compression cycle |
references/callout-format.md | Phase 4 | Exact callout format, sourcing rule variants, insertion location |
Condense exhaustive university lecture material (notes, transcripts, slide PDFs) into a prioritised, exam-weighted study sheet. Two modes: revision (default) for fast review of already-learned material, and first-pass for learning cold when short on time. Trigger on "cram notes", "revision sheet", "high-yield/condensed study guide", "cut the fluff", "what's examinable", or turning a folder of lecture files into revision material; pick first-pass mode when the user says "first pass", "haven't learned this yet", or "teach me". Not for transcribing a live lecture (use lecture-notetaker) or a flat unprioritised recap (use lecture-condenser).
Generate ADHD-optimized Obsidian markdown study notes from lecture slides and transcripts — short capped chunks, per-section time estimates, static lecture-through percentages, session/break maps, micro-checks, and rotating interaction styles. Same learning techniques as lecture-notetaker-abbr with delivery tuned for sustained attention.
Condense multiple lecture-notetaker .md files from one series into a flat pre-exam priming document of recognisable key points, grouped by lecture and topic. Recognition tool for fast scan before exams — not full learning notes or flashcards.
Generate Obsidian markdown study notes from lecture slides and transcripts, organized by learning objective, with a hoverable abbreviation glossary. Each slide is embedded via PDF++ with non-redundant transcript content, structured around evidence-based learning techniques (pre-questions, retrieval practice, elaborative interrogation, interleaved self-testing, spaced review).
Generate Obsidian markdown study notes from lecture slides and transcripts, organized by learning objective. Each slide is embedded via PDF++ with non-redundant transcript content, structured around evidence-based learning techniques (pre-questions, retrieval practice, elaborative interrogation, interleaved self-testing, spaced review).