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schaeferanjon

lecture-notes-master

by SchaeferAnjon · GitHub ↗ · v1.0.0 · MIT-0
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/install lecture-notes-master
Description
Obsidian lecture notes with recursive atomic decomposition. Generates main note (hub), atomic notes (3+ layers deep, rich structure each), and unlimited glos...
README (SKILL.md)

Lecture Notes Master

Generate structured Obsidian lecture notes with recursive atomic decomposition:

  • 主笔记 (Main Note): Hub note with overview, core sections, summary, review questions
  • 原子笔记 (Atomic Notes): Deep concept notes in ≥3 layers, each layer with rich structure
  • 原子概念 (Glossary): Unlimited bilingual term definitions

When to Apply

Triggers:

  • User provides: URL, video link, PDF, transcript, PPT, slides, article
  • User says: "总结", "summarize", "create notes", "lecture notes", "笔记", "做笔记"
  • User mentions: Obsidian, atomic notes, wikilinks, PARA, MOC
  • User provides content for note-taking or analysis
  • Exam prep / study materials

Example prompts:


User Profile

Configured in config.json:

  • Name: Schaefer (Zonghan Jia)
  • University: Heidelberg University (ZITI), Computer Engineering
  • Obsidian Vault: See config.jsonobsidian.vault_path
  • Output Directory: 00-Inbox/{Topic}/ (main note + glossary + numbered L1 subdirectories)
  • Language: Bilingual — English primary, Chinese secondary
  • Term format: English Term(中文术语)

Core Principles

Recursive Atomic Decomposition

Every source material is decomposed into a tree of notes, organized into numbered subdirectories by L1 topic:

{Topic}/
├── 主笔记: {Topic}-Notes.md
│   Hub note linking to all L1 atomic notes
│
├── 01-{L1-Concept-A}/
│   ├── {L1-Concept-A}.md              (L1 顶层概念)
│   ├── {L2-Sub-Concept-A1}.md         (L2 子概念)
│   ├── {L2-Sub-Concept-A2}.md         (L2 子概念)
│   ├── {L3-Detail-A1a}.md             (L3 细分解)
│   └── {L3-Detail-A1b}.md             (L3 细分解)
│
├── 02-{L1-Concept-B}/
│   ├── {L1-Concept-B}.md              (L1 顶层概念)
│   ├── {L2-Sub-Concept-B1}.md         (L2 子概念)
│   └── {L3-Detail-B1a}.md             (L3 细分解)
│
├── 03-{L1-Concept-C}/
│   └── {L1-Concept-C}.md              (L1 顶层概念)
│
├── glossary/(原子概念 — 术语定义,不限量)
│   ├── English Term(中文术语).md
│   └── ... 每个术语一个文件
│
└── assets/(图表资源)
    └── *.png

Directory Organization Rules

Location Contains Example
{Topic}/ root Main note only Lazygit-Notes.md
NN-{L1-Concept}/ L1 note + its L2 children + their L3 children 01-Installation-and-Setup/
glossary/ All glossary entries glossary/TUI(终端用户界面).md
assets/ Generated charts/images assets/performance-chart.png

Numbering Rules:

  1. L1 subdirectories use two-digit prefix: 01-, 02-, 03-, ...
  2. No atomic notes in the topic root — only the main hub note lives there
  3. Within each L1 folder, all notes are flat (L1 + L2 + L3 together, no further nesting)
  4. Numbering follows the order of L1 concepts as they appear in the main note
  5. Wiki-links use filename only (no path prefix) — Obsidian resolves them automatically

Decomposition Rules

Layer 1 (顶层概念):

  • Source material's major themes, sections, or arguments
  • Each L1 note covers ONE major concept branch
  • Number: determined by content (typically 3-7, NOT fixed)

Layer 2 (子概念):

  • Sub-concepts, mechanisms, or components within each L1 concept
  • Each L2 explains a specific aspect of its parent L1
  • Number: typically 2-4 per L1 parent

Layer 3+ (细分解):

  • Specific mechanisms, case studies, comparisons, or evidence
  • Finest granularity of analysis
  • Number: as many as the content demands

Stop Decomposing When:

  • A concept can be fully explained in ≤500 words with one diagram
  • Further splitting would break logical coherence
  • The concept is better served as a glossary entry (pure definition)

Glossary vs Atomic Note:

Use Glossary Use Atomic Note
Term needing a bilingual definition (1-3 sentences) Concept requiring explanation, examples, diagrams
No deep analysis needed Has sub-components worth exploring
Pure noun/term Has "why", "how", comparison dimensions

Minimum Requirement

Every set of notes MUST produce:

  • 1 main note (主笔记)
  • ≥3 layers of atomic notes (L1 → L2 → L3 minimum)
  • Glossary entries for ALL technical terms mentioned

Every Atomic Note Must Be Rich

ALL atomic notes (L1, L2, L3) use the same rich template structure:

  1. 定义 — One-paragraph definition with inline wikilinks
  2. Why Do We Need This? — Motivation with concrete scenario
  3. Core Concept — Idea + example + Mermaid diagram + step-by-step breakdown
  4. Comparison — Table comparing with/without, or vs alternatives
  5. Common Pitfalls — 2-3 mistakes and fixes (omit only if truly not applicable)
  6. Key Takeaway — One flashcard-worthy sentence
  7. Review Questions — 3 levels: recall, understanding, application
  8. Related Notes — Parent (UP), children (DOWN), siblings (ACROSS)

See templates/atomic-note.md for the full template.

Writing Style: Runoob Tutorial(菜鸟教程风格)

Rule Description
Step-by-step "Why do we need this?" → "What is it?" → "How does it work?" → "Watch out for..."
Example-driven Example FIRST, then explain the principle. Never start with pure theory
Visual-rich Every concept gets at least one Mermaid diagram OR table
Table comparison Similar concepts → comparison table
Bilingual terms English Term(中文), English is primary
Atomic Each note covers exactly ONE concept
Review questions 3 per note: recall, understanding, application

Prerequisites

python3 --version || python --version
pip3 install matplotlib numpy  # For visualization scripts (optional)

Optional: summarize CLI for URL/video content extraction.


Workflow

Step 1: Analyze User Input

Identify: input type (URL, video, PDF, transcript, raw text), topic name, key themes.

URL & Video Content Extraction

When user provides a URL or video link, use summarize CLI:

# Extract text from URL
summarize "\x3CURL>" --extract-only --model google/gemini-3-flash-preview

# Extract YouTube transcript
summarize "\x3CYouTube-URL>" --youtube auto --extract-only

# Pre-summary for screening
summarize "\x3CURL>" --length medium --model google/gemini-3-flash-preview

Useful flags: --extract-only (raw text), --youtube auto, --firecrawl auto (JS-heavy sites), --json

If summarize is not available: Use the agent's built-in web fetching tools as fallback.

Step 2: Plan Decomposition Tree

⚠️ MANDATORY: You MUST complete this step BEFORE writing ANY content. ⚠️ MANDATORY: You MUST create ALL directories with mkdir -p BEFORE writing ANY files. ⚠️ ZERO TOLERANCE: No atomic notes are allowed in the topic root directory. ONLY the main hub note lives there.

Plan the COMPLETE file tree with numbered directories, then create them immediately.

2a. Plan the tree (output to user for confirmation)

Topic: 2028 Global Intelligence Crisis

主笔记: 2028-Global-Intelligence-Crisis-Notes.md  (in topic root)

01-Intelligence-Displacement-Spiral/
  L1: Intelligence-Displacement-Spiral.md
  L2: OpEx-Substitution-Mechanism.md
  L2: Ghost-GDP-Phenomenon.md
  L3: OpEx-vs-CapEx-AI-Spending.md               ← parent: OpEx-Substitution
  L3: Why-No-Natural-Brake.md                     ← parent: OpEx-Substitution

02-SaaS-Collapse-and-Intermediation-Death/
  L1: SaaS-Collapse-and-Intermediation-Death.md
  L2: Agentic-Coding-Disruption.md
  L2: Habitual-Intermediation-Collapse.md
  L3: Friction-Zero-Disruption.md                 ← parent: Habitual-Intermediation

03-White-Collar-Displacement-Asymmetry/
  L1: White-Collar-Displacement-Asymmetry.md
  L2: Downshifting-Effect.md
  L2: Labor-Share-Decline.md

04-Financial-Contagion-Chain/
  L1: Financial-Contagion-Chain.md
  L2: Private-Credit-SaaS-Crisis.md
  L2: Permanent-Capital-Trap.md
  L2: Mortgage-Market-Structural-Threat.md
  L3: Zendesk-Case-Study.md                       ← parent: Private-Credit
  L3: Insurance-Asset-Impairment.md               ← parent: Permanent-Capital

glossary/ (42 entries):
  glossary/Ghost GDP(幽灵GDP).md
  glossary/Intelligence Displacement Spiral(智能替代螺旋).md
  glossary/Private Credit(私募信贷).md
  ... (one file per term)

2b. Create ALL directories FIRST (before writing any files)

# MANDATORY: Run this BEFORE writing any notes
TOPIC_DIR="\x3Cvault>/00-Inbox/{Topic}"
mkdir -p "$TOPIC_DIR"
mkdir -p "$TOPIC_DIR/01-{L1-Concept-A}"
mkdir -p "$TOPIC_DIR/02-{L1-Concept-B}"
mkdir -p "$TOPIC_DIR/03-{L1-Concept-C}"
# ... one mkdir per L1 concept
mkdir -p "$TOPIC_DIR/glossary"
mkdir -p "$TOPIC_DIR/assets"

2c. Verify directory structure before proceeding

# Verify: must show numbered subdirectories + glossary + assets
find "$TOPIC_DIR" -type d | sort

Only proceed to Step 3 after directories exist.

Step 3: Search Existing Resources

# Search glossary for existing bilingual terms
python3 \x3CSKILL_DIR>/scripts/search.py "\x3Ctopic keywords>" --domain glossary

# Search Mermaid templates for diagram ideas
python3 \x3CSKILL_DIR>/scripts/search.py "\x3Cconcept type>" --domain mermaid

# Search writing rules for style guidance
python3 \x3CSKILL_DIR>/scripts/search.py "\x3Ccontent type>" --domain writing

Step 4: Generate Notes (in order)

⚠️ CRITICAL: Every file MUST be written to its correct subdirectory. Double-check the output path before every Write/Edit call.

Order Note Type Write To Example Path
1 Main note (hub) {Topic}/ root — ONLY file here Lazygit/Lazygit-Notes.md
2 L1 atomic notes {Topic}/NN-{L1-Concept}/ Lazygit/01-Installation-and-Setup/Installation-and-Setup.md
3 L2 atomic notes Same dir as parent L1 Lazygit/03-Basic-Git-Operations/Staging-and-Committing.md
4 L3 atomic notes Same dir as parent L1 Lazygit/03-Basic-Git-Operations/Staging-Modes.md
5 Glossary entries {Topic}/glossary/ Lazygit/glossary/Stage(暂存).md
6 Visualizations {Topic}/assets/ Lazygit/assets/workflow-chart.png

Self-check before each file write:

  • Is this the main hub note? → Write to {Topic}/ root
  • Is this an atomic note (L1/L2/L3)? → Write to {Topic}/NN-{L1-Parent}/
  • Is this a glossary entry? → Write to {Topic}/glossary/
  • NEVER write atomic notes directly to {Topic}/ root

Script Usage

# Main note (in topic root)
python3 \x3CSKILL_DIR>/scripts/generate.py \
  --type lecture --title "\x3Ctitle>" \
  --concepts "C1,C2,C3,C4" \
  --output "\x3Cvault>/00-Inbox/{Topic}/"

# Atomic note (any layer — output to its L1 parent subdirectory)
python3 \x3CSKILL_DIR>/scripts/generate.py \
  --type atomic --concept "\x3Cname>" \
  --parent "\x3Cparent-note-stem>" \
  --children "Child1,Child2" \
  --siblings "Sibling1,Sibling2" \
  --output "\x3Cvault>/00-Inbox/{Topic}/NN-{L1-Concept}/"

# Glossary entry
python3 \x3CSKILL_DIR>/scripts/generate.py \
  --type glossary --term-en "\x3CEnglish Term>" --term-cn "\x3C中文术语>" \
  --definition "\x3Cone-line definition>" \
  --output "\x3Cvault>/00-Inbox/{Topic}/glossary/"

# Course MOC
python3 \x3CSKILL_DIR>/scripts/generate.py \
  --type moc --course "\x3Ccourse>" --semester "\x3Csemester>"

Note: \x3CSKILL_DIR> = the directory where this skill is installed. NN = two-digit L1 index (01, 02, ...).

Step 5: Generate Visualizations (as needed)

For data-driven charts that Mermaid cannot handle:

python3 \x3CSKILL_DIR>/scripts/visualize.py \
  --type "\x3Cchart_type>" \
  --data "\x3Cdata_json>" \
  --output "\x3Coutput_path>" \
  --title "\x3Cchart title>"

Available chart types: bar, line, scatter, timeline, heatmap, comparison, pie, radar


Output Format Templates

All templates are in the templates/ directory:

Type Template File Description
Main Note (主笔记) templates/lecture-note.md Hub note with overview, N sections, summary, review questions
Atomic Note (原子笔记) templates/atomic-note.md Rich concept note — used for ALL layers (L1, L2, L3)
Glossary Entry (原子概念) templates/glossary-entry.md Short bilingual term definition
Course MOC templates/course-moc.md Map of Content with lecture index, concept clusters

Main Note (主笔记) Key Requirements

  • YAML frontmatter: title, date, course/topic, tags, source_files, status, aliases
  • Core Idea(核心思想)blockquote with inline [[glossary-wikilinks]]
  • Source links
  • Overview section (with image embed if available)
  • N numbered sections (one per L1 concept, NOT limited to 3), each with:
    • → 详见 [[L1-Atomic-Note]] link
    • Sub-sections: Why → What → How
    • Mermaid diagrams, comparison tables, key insight blockquotes
  • Summary section: concept map (Mermaid) + data summary table + key quote
  • Review Questions: recall + understanding + application + critical thinking
  • Related Notes linking to all L1 atomic notes

Atomic Note (原子笔记) Key Requirements

Same rich structure for ALL layers (L1, L2, L3):

  • YAML frontmatter: title (bilingual), date, tags, aliases
  • One-paragraph 定义 with inline wikilinks
  • Why Do We Need This? — motivation + concrete example
  • Core Concept — idea + example/code + Mermaid diagram + step-by-step
  • Comparison — table comparing alternatives or with/without
  • Common Pitfalls — mistakes and how to avoid
  • Key Takeaway — one flashcard-worthy sentence
  • Review Questions — recall, understanding, application
  • Related Notes — parent (UP) + children (DOWN) + siblings (ACROSS)

Glossary Entry (原子概念) Key Requirements

  • YAML frontmatter: title (bilingual), date, tags: [glossary, {topic-tag}], aliases
  • One blockquote 定义 (1-3 sentences, bilingual)
  • Related Notes linking to relevant atomic notes + main note

Cross-Reference Rules (CRITICAL)

All notes link in THREE directions:

Note Type Links UP to Links DOWN to Links ACROSS to
Main Note All L1 notes
L1 Atomic Main Note Its L2 children Sibling L1 notes
L2 Atomic Parent L1 Its L3 children Sibling L2 notes
L3 Atomic Parent L2 Sibling L3 notes
Glossary Related atomic notes Related glossary terms

Inline wikilinks: All glossary terms MUST be linked inline on first mention in every note using [[Term(术语)]] syntax.


Wiki-Link Naming Convention (CRITICAL)

Wiki-link targets MUST match filenames (without .md). Spaces → hyphens in atomic notes, but glossary keeps parenthetical Chinese.

Note Type File Name Pattern Wiki-Link
Main Note {Topic}-Notes.md [[{Topic}-Notes]]
Atomic Note {Concept-Name}.md [[{Concept-Name}]]
Glossary Entry {English Term(中文术语)}.md [[{English Term(中文术语)}]]

Rules:

  1. Before generating any notes, list ALL file names AND their target directories
  2. NEVER leave wiki-links empty ([[]]) or with placeholder text ([[TODO]])
  3. If a target note doesn't exist yet, use the correct future filename — Obsidian creates it on click
  4. Glossary filenames include both English and Chinese: English Term(中文术语).md
  5. Wiki-links use filename only (no path) — Obsidian resolves across subdirectories automatically

Mermaid Diagram Guidelines

Diagram Type Use For Example
graph TB/LR Hierarchies, flows, architectures Concept trees, process chains
sequenceDiagram Time-ordered interactions Data transfers, API calls
stateDiagram-v2 State transitions Lifecycle, mode changes
classDiagram Object relationships Class hierarchy
gantt Timelines, parallel tasks Project phases, pipeline
pie Proportions Distribution breakdown
xychart-beta Data trends Performance scaling
mindmap Topic overview Concept clustering

Rules:

  1. NEVER use ASCII art — always Mermaid
  2. Every diagram must have a title and labeled edges
  3. Max 15-20 nodes per diagram
  4. Use classDef for consistent colors
  5. Use subgraphs for grouping

Color theme:

%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#4CAF50', 'primaryTextColor': '#fff', 'primaryBorderColor': '#388E3C', 'lineColor': '#666', 'secondaryColor': '#FF9800', 'tertiaryColor': '#2196F3'}}}%%

Matplotlib Visualization

Use when Mermaid cannot express the data: performance bars, scaling curves, heatmaps, radar charts.

Rules: PNG at dpi=150, consistent colors (#4CAF50, #FF9800, #2196F3, #F44336), always include axis labels and title, reference as ![[filename.png]].


Search Reference

Domain Use For Example Keywords
glossary Find existing bilingual terms cuda, memory, 内存, 术语
mermaid Find diagram templates flow, sequence, hierarchy, 图表
writing Get writing style rules introduction, comparison, 规则
questions Get review question templates recall, application, 考试

Quality Checklist

Content

  • WHY → WHAT → HOW structure for every concept (all layers)
  • Example appears BEFORE theory in every concept note
  • All terms bilingual: English(中文)
  • Each atomic note covers exactly ONE concept

Decomposition

  • Decomposition tree planned BEFORE writing
  • ALL file names AND target directories listed before generation
  • ≥3 layers of atomic notes produced (L1 → L2 → L3 minimum)
  • L1 covers major themes, L2 breaks down sub-concepts, L3 provides finest analysis
  • Glossary entries for ALL technical terms mentioned

Visualization

  • NO ASCII art — use Mermaid
  • Every concept has at least one diagram or table
  • Similar concepts have comparison tables
  • Diagrams have titles and labeled edges

Structure

  • YAML frontmatter complete (title, date, tags, aliases)
  • [[wiki-links]] for all cross-references (inline + Related Notes)
  • 3 review questions per atomic note: recall, understanding, application
  • Related Notes with UP / DOWN / ACROSS links
  • File naming: Concept-Name.md (hyphens for atomic notes)

Directory Organization

  • Main note in topic root (00-Inbox/{Topic}/)
  • Atomic notes organized into numbered L1 subdirectories (01-xxx/, 02-xxx/)
  • L2 and L3 notes placed inside their parent L1 subdirectory (flat, no further nesting)
  • Glossary entries in glossary/ subdirectory
  • No atomic notes left loose in topic root

Obsidian Compatibility

  • [[wiki-links]] syntax (not markdown links)
  • Standard ```mermaid fencing
  • Images: ![[image.png]]
  • Tags: lowercase with hyphens

Tips

  1. Provide source material: URL, video link, PDF, transcript → better notes
  2. Video links: Paste YouTube/Bilibili URLs directly; transcript auto-extracted
  3. Be specific about topic: "这个视频的AI经济分析" > "总结一下"
  4. Search glossary first: Reuse existing terms for cross-course consistency
  5. Iterate: Generate base notes, then ask for deeper decomposition on specific branches
  6. Cross-reference: Link related concepts across different note sets
Usage Guidance
This skill is internally consistent with its purpose (creating Obsidian notes) and does not ask for credentials or remote installs, but it will write files to a local Obsidian vault path taken from the bundled config.json. Before installing or running: 1) Inspect and edit config.json to point to a directory you control (or leave it blank so the tool falls back to ~/00-Inbox): do not rely on the hard-coded /Users/apple/... path. 2) Review the included scripts (generate.py) so you understand where files will be created and how names are derived. 3) Run the generator first in a sandbox or temporary folder (pass --output) to verify outputs and avoid cluttering or overwriting your real vault. 4) If you will provide URLs, transcripts, or PDFs, note that the visible code does not show automatic downloading — you may need to supply the content or confirm any fetch behavior before allowing network access. If any of these points are concerning, do not run the skill until you can safely review/modify the configuration and run it in a controlled environment.
Capability Analysis
Type: OpenClaw Skill Name: lecture-notes-master Version: 1.0.0 The skill bundle is a legitimate and highly structured tool designed to assist an AI agent in generating complex, bilingual Obsidian lecture notes. It utilizes a 'Recursive Atomic Decomposition' workflow supported by Python scripts for note generation (generate.py), BM25-based searching of local reference data (search.py), and Matplotlib-based visualization (visualize.py). While the instructions in SKILL.md direct the agent to perform file system operations (mkdir) and call external tools (summarize CLI), these actions are strictly aligned with the stated purpose of organizing academic content. No evidence of data exfiltration, malicious code execution, or harmful prompt injection was found; the bundle appears to be a productivity tool for students or researchers.
Capability Assessment
Purpose & Capability
Name, description, templates and Python scripts align with the stated purpose: generating Obsidian markdown notes (main hub, atomic notes, glossary, assets). The included scripts/templates produce the described rich structure and frontmatter. One notable oddity: config.json contains a specific absolute vault_path (/Users/apple/...Schaefer_Master/) that looks like the original author's local path. Using that default as-is on another machine could cause files to be written to an unexpected location. The presence of that path is coherent with the skill's purpose (default output target) but should be reviewed/edited by the installer.
Instruction Scope
SKILL.md and templates instruct generation of many markdown files, with strict rules (glossary for all technical terms, 3+ layers, diagrams, etc.). The runtime instructions and bundled generate.py read config.json and write files into an Obsidian vault/inbox path. The skill does not appear to instruct reading unrelated system files or exfiltrating data, nor does the provided code show network fetches of external content in the visible portions. However, because it will create many files and expects to place them into an Obsidian vault, users should be aware it performs local filesystem writes and will use the configured vault path unless overridden.
Install Mechanism
No install spec; instruction-only plus included Python scripts. There are no downloads from external URLs or package installs in the manifest. This is low-risk from an install/download perspective.
Credentials
The skill requests no environment variables, no secrets, and no external credentials. The only configuration is config.json (included) with user/name/vault_path defaults. The requested permissions (filesystem write to Obsidian inbox path) are proportionate to the stated functionality of producing note files.
Persistence & Privilege
The skill does not request 'always: true' or elevated platform privileges. It does, however, include code that writes files to disk (write_file creates directories and writes markdown). That file-write capability is expected for a note generator but is significant: confirm the configured output path in config.json (or pass an explicit --output) to avoid unintentionally populating or overwriting an existing vault. The skill does not appear to modify other skills or global agent settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install lecture-notes-master
  3. After installation, invoke the skill by name or use /lecture-notes-master
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Lecture Notes Master — structured Obsidian note generator with recursive atomic decomposition. - Generates main hub note, deeply layered atomic notes (≥3 levels), and unlimited glossary entries from lectures, articles, videos, transcripts, or PDFs. - Organizes all notes into numbered topic subdirectories and a dedicated glossary folder, following strict atomic/note structure rules. - Ensures every atomic note follows a rich template: definition, motivation, diagrams, comparison tables, pitfalls, takeaways, review questions, and related links. - Supports bilingual term entries and outputs in Obsidian markdown with built-in Mermaid diagrams and wikilinks. - Designed for easy import into user-defined Obsidian vaults; customizable for study, exam prep, and knowledge management.
Metadata
Slug lecture-notes-master
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is lecture-notes-master?

Obsidian lecture notes with recursive atomic decomposition. Generates main note (hub), atomic notes (3+ layers deep, rich structure each), and unlimited glos... It is an AI Agent Skill for Claude Code / OpenClaw, with 255 downloads so far.

How do I install lecture-notes-master?

Run "/install lecture-notes-master" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is lecture-notes-master free?

Yes, lecture-notes-master is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does lecture-notes-master support?

lecture-notes-master is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created lecture-notes-master?

It is built and maintained by SchaeferAnjon (@schaeferanjon); the current version is v1.0.0.

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