coding-academy-lecture-manager 1.1.5

Coding-Academy Lecture Manager - A course content processing system

CLM - Coding-Academy Lecture Manager

Version: 1.1.5 | License: MIT | Python: 3.11, 3.12, 3.13, 3.14

CLM is a course content processing system that converts educational materials (Jupyter notebooks, PlantUML diagrams, Draw.io diagrams) into multiple output formats.

Quick Start

Installation

# Install from PyPI
pip install coding-academy-lecture-manager

# Or with all optional dependencies (workers, TUI, web dashboard)
pip install "coding-academy-lecture-manager[all]"

For development, clone the repository and install in editable mode:

git clone https://github.com/hoelzl/clm.git
cd clm
pip install -e ".[all]"

Basic Usage

# Convert a course
clm build /path/to/course.xml

# Watch for changes and auto-rebuild
clm build /path/to/course.xml --watch

# Show help
clm --help

Features

• Multiple Output Formats: HTML slides, Jupyter notebooks, extracted code
• Multi-Language Notebooks: Python, C++, C#, Java, TypeScript, Markdown
• Diagram Support: PlantUML and Draw.io conversion
• Multiple Output Targets: Separate student/solution/instructor outputs
• Watch Mode: Auto-rebuild on file changes
• Incremental Builds: Content-based caching
• LLM Summaries: Generate course summaries with clm summarize using any LLM via litellm
• Voiceover Sync: Synchronize video recordings with slides to auto-generate speaker notes (clm voiceover sync)
• LLM Polish: Clean up speaker notes with LLM-powered text polishing (clm polish)
• Git Integration: Manage output repos with clm git init/sync/status, including --amend and --force-with-lease for iterative workflows
• Flexible Remote URLs: Configurable git remote URL templates for SSH, custom hosts, etc.

Documentation

For Users:

• User Guide - Complete usage guide
• Quick Start - Build your first course
• Spec File Reference - Course XML format
• Configuration - Configuration options
• Changelog - Version history

For Developers:

• Contributing Guide - How to contribute
• Developer Guide - Development documentation
• Architecture - System design
• CLAUDE.md - AI assistant reference

Development Setup

# Install pre-commit hooks (recommended)
uv run pre-commit install

# This enables automatic linting (ruff) and type checking (mypy) on every commit

Testing

# Run unit tests
pytest

# Run all tests (unit, integration, e2e)
pytest -m ""

# Run with coverage
pytest --cov=src/clm

License

MIT License - see LICENSE for details.

Links

• Repository: https://github.com/hoelzl/clm/
• Issues: https://github.com/hoelzl/clm/issues