# Transcript To Notes > Transform a raw Udemy/course transcript (.txt) into ADHD-friendly GoodNotes-style handwritten study notes, an A4 PDF notebook, and an XMind-compatible mindmap. Use when: converting transcript to notes; creating study notes from transcript; generating mindmap from course; making PDF notebook from udemy transcript; 'make notes from this transcript'; 'create mindmap from course'; 'study notes from video'. - Skill: `arn-nj/transcript-to-notes` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add arn-nj/transcript-to-notes` - Raw SKILL.md: https://api.skillmd.com/api/skills/arn-nj/transcript-to-notes/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: arn-nj (https://skillmd.com/u/arn-nj) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/arn-nj/transcript-to-notes --- # Transcript → GoodNotes Notes + XMind Mindmap ## What This Skill Does Given a raw course transcript file, produce: 1. **`_transcript_normalized.txt`** — normalized transcript copy used as the semantic source of truth 2. **`_concept_inventory.json`** — semantic concept inventory extracted from transcript + lesson map 3. **`_visual_briefs.json`** — mandatory per-lesson semantic visual briefs used by the Art Director phase before any sketch is drawn 4. **`_Flashcards.csv`** — mandatory active-recall export with header `Front, Back, Tags` (generated semantically from transcript + concepts) 4. **`_GoodNotes_detailed.md`** — ADHD-friendly handwritten study notes, chunked by video/lesson or micro-lesson when needed 5. **`_GoodNotes_detailed_A4.pdf`** — A4 PDF for GoodNotes import 6. **`_course_videos_clean.mm`** — semantic Freemind mindmap 7. **`_course_videos_fixed.xmind`** — semantic XMind mindmap (concept-enriched) 8. **`_narrative_manifest.json`** — semantic narrative manifest for v2 deck generation 9. **`_pitch_deck.html`** — mandatory v2 cinematic Reveal.js deck generated from the narrative manifest 10. **`_Teleprompter_Script_.md`** — teleprompter script generated by `xmind-notes-to-deck-v2` 11. **`assets/` folder** — Excalidraw source scenes (`.excalidraw`) plus compiled PNG sketches for key concepts (auto-generated via content-to-sketch + compiler) 12. **`assets/INDEX.md`** — Visual reference guide with sketch metadata 13. **`_INDEX.md`** — Master topic index with all pages and key concepts 14. **`_AI_Elaboration.json` or `_AI_Elaboration.md`** — optional external AI synthesis for page-level elaboration used by the XMind enrichment step All outputs go into a `/` subfolder alongside the source transcript. ## Systemic Execution Contract ### Execution Modes - `--standard`: output clean, hierarchical, engineering-focused outlines that map the exact technical architecture. - `--adhd`: prohibit introductory summaries, conversational text, and sections titled `Intro`; require immediate visual memory hooks (ASCII tables/diagrams), bold technical syntax anchors, and real-world-use-first ordering. ### Balanced Stack Multi-Model Orchestration Pipeline Every run must execute this strict routed backend architecture: 1. Phase 1 (Ingestion and Bulk Extraction): - Route raw transcript input to a primary mid-tier extractor model (for example GPT-5.4 or another 1M+ context model). - Perform comprehensive fact extraction and archetype classification without context loss. 2. Phase 2 (Markdown and Schema Formatting): - Route the extracted conceptual payload to a strict coding/schema model (for example Claude Sonnet 4.6). - This phase is solely responsible for GoodNotes markdown formatting, Excalidraw scene orchestration, PNG compilation linkage, flashcard CSV compilation, and flawless XML serialization for XMind. - Run the Semantic Depth Quality Gate on every Level 1 and Level 2 concept before finalizing output. 3. Phase 3 (AI Elaboration and Lateral Analysis): - Fork clean conceptual outputs to an advanced reasoning model (for example GPT-5 or equivalent). - Generate `_AI_Elaboration.json` by extrapolating edge cases and downstream implications without hallucination or source-text repetition. ### Semantic Depth Quality Gate (Validation Step) Apply this gate inside Phase 2 for all Level 1 and Level 2 concepts. Triad Test (mandatory): - What: clear definition of the concept. - How: functional mechanism or execution behavior. - Why: rationale, trade-off, or impact. If any concept contains only definition-level content and misses mechanism or rationale, the gate fails. When the gate fails, re-process transcript extraction for that concept and regenerate until all three triad dimensions are explicitly present. ### Full-Course Coverage Gate (Hard Stop) Never ship a partial smoke output. Mandatory minimums before completion: - Detect total lesson count from transcript markers (for example `--- Lesson ---`). - Notes page count must be greater than or equal to detected lesson count. - Mindmap Level 1 lesson nodes must be greater than or equal to detected lesson count. - Flashcards must include at least one card per lesson. - PNG coverage must be at least one visual per lesson by default. If lesson boundaries are ambiguous: - Use section-level chunks, and explicitly document the reason. - Still generate a minimum of 12 PNGs for medium/large courses. Completion must fail if: - Page count is materially below lesson count. - Output only covers intro/first module while transcript includes additional modules. - PNG count is trivially low relative to detected course breadth. ### Universal Cognitive Hooks (Required In All Modes) Apply these hooks in both `--standard` and `--adhd` modes, regardless of archetype. 1. Real-World Anchoring: - Every core concept must start with a concrete 1-2 sentence industry scenario before abstract theory. 2. Mental Models and Analogies: - Complex architectural or procedural concepts must include a short, relatable analogy. 3. Visual Memory Hooks: - Do not rely only on ASCII. - Generate rich descriptive image prompts optimized for image generation models (for example Gemini or Midjourney) or map directly to the content-to-sketch PNG pipeline. - Embed the visual placeholder inline with the concept flow in markdown. ### Dynamic Content Archetype Classification Classify each lesson into one archetype before generating markdown pages or mindmap nodes. Use only the associated mandatory node set. - Type A: Conceptual Architecture or Frameworks - `Core Architecture Blueprint` - `Core Components & Governance` - `Key Integration Points` - `Systemic Trade-offs` - Type B: Procedural Workflows or Pipelines - `Execution Pipeline / Steps` - `Environment Prerequisites & Setup` - `Failure Recovery & Verification` - `Performance Optimizations` - Type C: Discrete Technical Components or Tools - `Functional Mechanics` - `Concrete Configuration & Code Snippets` - `Edge Cases & System Constraints` Never mix node sets across archetypes for the same lesson. ## ADHD-Friendly Handwritten GoodNotes Design ### What's Included for ADHD Brains - **Micro-chunking**: Keep each page to a 3-5 minute read; split long lessons into multiple micro-pages - **Handwritten aesthetic**: GoodNotes-style notes that feel personal and approachable - **Sepia paper background**: Warm aged-paper tone with handwritten fonts for authentic GoodNotes import - **Frequent visual breaks**: ASCII diagrams, tables, icons, and sketches instead of dense prose - **Index-first navigation**: begin the notebook with a quick index page so readers can jump directly to the needed lesson - **Progressive challenge ordering**: group foundational pages before advanced pages to reduce overwhelm without adding metadata headers to every page - **Action-first structure**: Put practical use before abstract explanation - **Active recall built in**: Quick recall questions on every lesson, exported to CSV - **Whitespace heavy formatting**: Keep pages breathable and scannable - **Concrete examples first**: Use real scenarios before theory - **Progress signals**: Checkboxes and small completion markers for momentum ### What's Avoided - Long paragraphs or lecture-style dumps - Multiple new ideas packed into one bullet - Abstract wording without examples - Open-ended review advice like "revise later" - Large blocks of uninterrupted text - Passive explanations that hide the point ## Visual Style Default - Use clean whiteboard-style visual backgrounds for generated sketches (no grid). - Generated sketch visuals embedded in markdown/PDF should not include grid backgrounds. ## Workspace Layout ``` smart-study-notes/ ├── / │ ├── .txt ← input │ ├── _Flashcards.csv │ ├── _GoodNotes_detailed.md │ ├── _GoodNotes_detailed_A4.pdf │ ├── _course_videos.mm │ ├── _course_videos_clean.mm │ ├── _course_videos_fixed.xmind │ ├── _INDEX.md ← master topic index │ └── assets/ ← auto-generated sketches │ ├── _ProcessFlow_sketch.png │ ├── _KeyConcept1_sketch.png │ ├── _KeyConcept2_sketch.png │ ├── INDEX.md ← visual reference guide │ ├── validate_pngs.ps1 ← validation script │ └── validation_report.txt ← quality report ├── scripts/ │ ├── md_to_pdf_a4.py ← reusable: Markdown → A4 PDF │ └── generate_valid_xmind.py ← semantic .mm → .xmind converter └── copilot-skills/skills/transcript-to-notes/ ``` ## Procedure ### Mandatory Checkpoint Protocol (Fail-Fast) Apply this protocol after each procedure step. Do not continue when a checkpoint fails. Checkpoint format (required): - `CHECKPOINT : PASS - ` - `CHECKPOINT : FAIL - ` Fail-fast rule (required): - If any checkpoint is `FAIL`, stop immediately. - Do not proceed to the next step. - Do not attempt downstream regeneration until the failed step is fixed and re-validated. Checkpoint map: - `P` before Step 1: workflow plan exists and is approved before any generation starts. - `A` after Step 1: lesson inventory must be present and non-empty. - `B` after Step 2 preflight: each lesson must include mechanism, archetype, analogy, label-fusion intent, and composition intent. - `C` after Step 2 generation: visuals must not be template clones across lessons (concept-specific variation required). - `D` after Step 2 validation: PNG count must be >= lesson count and all PNGs must pass integrity checks. - `E` after Step 3: notes must be non-empty, page count >= lesson count, and required sections must be present. - `F` after Step 4: `.mm` must parse and Level 1 lesson nodes must be >= lesson count. - `G` after Step 5: `.xmind` must exist and be valid. - `H` after Step 6.6: flashcards CSV header and minimum lesson coverage rules must pass. - `I` after Step 7: index TOC row count must be >= lesson count. - `J` after Step 8: PDF must exist and be non-trivial (not blank output). - `K` final: all checkpoints must be PASS. ### Step 0 — Plan the Workflow Before Running Create a short execution plan before any generation step runs. Plan requirements: - Include `Inputs`, `Outputs`, `Dependencies`, and `Execution order`. - Include a per-step `checkpoint expectation` (what constitutes PASS/FAIL). - Include `rollback/retry strategy` for the first failed checkpoint. - Record the plan in `/checkpoints/workflow-plan.md`. Checkpoint `P`: - PASS when `workflow-plan.md` exists and covers all procedure steps with checkpoint criteria. - FAIL when planning is missing, incomplete, or execution starts before plan approval. Fail-fast enforcement for Step 0: - If `P` fails, do not run Step 1. - Stop immediately and fix the plan first. ### Step 1 — Read the Transcript - Read the `.txt` file in full - Identify video/lesson boundaries (look for "Video N:", timestamps, or heading markers) - Note course name, module structure, and key themes - Flag dense lessons that should be split into multiple micro-pages instead of one long page - Identify practical examples, repeated terms, and likely memory bottlenecks Coverage requirement: - Build an explicit lesson inventory table during ingestion: `Section`, `Lesson`, `Order`. - Use this inventory as the source of truth for page counts, mindmap Level 1 nodes, flashcards, and PNG counts. Checkpoint `A`: - PASS when lesson inventory exists and contains at least 1 detected lesson. - FAIL when lesson boundaries are missing/ambiguous and inventory is empty. ### Step 1.5 — Generate Semantic Lesson Briefs Before Any Sketching Create `_visual_briefs.json` from the transcript + lesson inventory. This is the mandatory semantic bridge between source ingestion and image generation. Do **not** jump directly from lesson titles into sketch rendering. Every lesson brief must include: - `lessonTitle` - `lessonSlug` - `archetype` - `coreMechanism` - `what` - `how` - `why` - `realWorldAnchor` - `mentalModel` - `edgeCase` - `primaryEntities` - `primaryFlow` - `mainConstraint` - `tradeoff` - `physicalAnalogy` - `labelFusion` - `compositionIntent` - `hotPath` - `secondaryPaths` - `mustShow` - `mustNotShow` - `visualSceneType` - `sceneObjects` - `objectToConceptMap` - `spatialLayout` - `entryPoint` - `exitPoint` - `failureBranch` - `comparisonAxis` - `evidenceArtifact` - `dominantMotion` - `forbiddenTemplate` - `objectPalette` - `shapeQuota` - `forbiddenGeometry` - `labelBudget` - `sceneTest` - `silhouettePlan` - `propDensity` - `asymmetryPlan` - `antiCardFraming` - `sceneTexture` - `premiumSceneTest` - `diagramIntent` - `formalFramework` - `sceneFamilyCandidates` - `selectedSceneFamily` - `selectionReason` - `formalCompanionSuggested` - `salienceGoal` - `silhouetteContrastRules` - `propInventory` - `textureInventory` Hard rule: - Final notebook pages are **not** required yet. - Full semantic understanding **is** required before image generation. - If a lesson cannot produce a strong visual brief, stop and deepen extraction before sketching. - A title plus generic `what/how/why` summary is not enough; the brief must drive geometry, motion, object choice, visual salience, and scene-object diversity. - Resolve `diagramIntent`, `formalFramework`, `sceneFamilyCandidates`, `selectedSceneFamily`, and `selectionReason` before finalizing `visualSceneType`. - Prefer selecting `visualSceneType` and `objectPalette` from `content-to-sketch/references/canonical-scene-families.md` before inventing a new scene family. - For final learner-facing visuals, also apply `content-to-sketch/references/premium-sketch-language.md` so the brief carries silhouette, prop-density, asymmetry, anti-card-framing, and scene-texture intent. - When premium visuals are requested, structure the downstream render around `content-to-sketch/references/premium-generation-playbook.md` rather than jumping straight from brief text to final layout. - If the concept clearly maps to a formal framework (for example C4, BPMN, sequence, state, Wardley, or service blueprint), record that choice even if the final output will be a semantic sketch. - The final learner-facing image must not expose planning metadata such as scene names, mechanism lines, analogy lines, or must-show rules. Checkpoint `B`: - PASS only when every lesson has a complete semantic visual brief with mechanism, analogy, composition, and hot-path detail. - FAIL when briefs are shallow, title-only, or generic. ### Step 2 — Generate PNG Sketches (invoke content-to-sketch skill) **This step MUST run before notes, mindmap, or XMind generation.** PNG filenames produced here are embedded directly into notes (Step 3), .mm (Step 4), .xmind (Step 5), and the narrative manifest (Step 5.5). **Invoke the `content-to-sketch` skill** — do not reimplement its pipeline here. The skill owns: - Art Director metaphor extraction preflight (mandatory before any diagram is drawn) - Excalidraw JSON generation with required style contract (roughness, hachure, Virgil, palette) - Headless compilation via `scripts/compile-excalidraw.js` → `.png` - All validation gates **Brief to pass to content-to-sketch for each lesson:** - Input: the corresponding object from `_visual_briefs.json` — not just the lesson title - Output target: `/assets/_lesson_.png` - Minimum: 1 PNG per lesson - Archetype selection must be justified by the semantic visual brief and confirmed by the Art Director phase - The brief must explicitly carry the lesson mechanism, analogy, label-fusion plan, composition intent, hot path, main constraint, scene objects, scene type, object palette, shape quota, forbidden geometry, label budget, scene test, silhouette plan, prop density, asymmetry plan, anti-card-framing plan, scene texture, premium scene test, diagram intent, formal framework, scene-family candidates, selected scene family, selection reason, formal companion suggestion, salience goal, silhouette contrast rules, prop inventory, and texture inventory **After the skill completes:** 1. Confirm `assets/` folder exists with one `.excalidraw` source and one compiled `.png` per lesson. 2. Validate all PNGs: ```bash for f in /assets/*.png; do python -c "from PIL import Image; import sys; Image.open(sys.argv[1]).verify()" "$f" && echo "✓ $f"; done ``` 3. Run structural QA on `.excalidraw` sources (text containment + arrow hygiene): ```bash node /skills/content-to-sketch/scripts/validate-excalidraw-quality.js \ --dir "/assets" \ --report "/assets/validation_report.txt" ``` 4. Record the confirmed filename list — used verbatim in Steps 3, 4, 5, and 5.5. 5. `assets/INDEX.md` is created by the skill. Flow integrity rule: - Do not directly patch generated diagram files as the primary fix path. - If visuals are weak or generic, re-run `content-to-sketch` with improved semantic lesson briefs and let it regenerate the entire affected lesson set. - Treat direct, one-off diagram edits as exceptions only after the skill pipeline passes all gates. - If a sketch could plausibly fit any random lesson with only label changes, treat that as a semantic failure and regenerate. **Gate**: Do not proceed to Step 3 until the content-to-sketch skill has completed and every lesson has at least one validated PNG in `assets/`. Checkpoints `C`, `D`: - `C` PASS only when lesson visuals are not near-identical templates and visibly encode the lesson mechanism/analogy through scene-specific geometry, motion, object mapping, hot-path salience, clean connector routing, non-box object diversity, and premium sketch-language cues where required. - `C` also requires structural QA with zero text-overflow errors and zero critical arrow endpoint-inside-node errors in `assets/validation_report.txt`. - `D` PASS only when every required PNG exists and integrity validation passes. ### Step 3 — Generate ADHD-Friendly GoodNotes Notebook See [handwritten-notebook-format-adhd](../pdf-to-notes/references/handwritten-notebook-format-adhd.md). The notebook must begin with a dedicated index page, then continue with lesson pages. If a lesson is dense, split it into 2-3 micro-pages. Use this exact structure: ``` -- Page 1: Course Index ## Course Map | Page | Lesson | Core Focus | |---|---|---| | 2 | | | ## How to Use This Notebook - Start with the lesson that matches your immediate problem. - Use the visual sketch first, then the deep dive, then the try-this block. - Use `_INDEX.md` for the full cross-reference and glossary. ## Fast Lanes - Core architecture pages - Process-heavy pages - Comparison or trade-off pages -- Page N: