Coding-Academy Lecture Manager - A course content processing system
Project description
CLM - Coding-Academy Lecture Manager
Version: 1.1.7 | 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 summarizeusing 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--amendand--force-with-leasefor 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
Release history Release notifications | RSS feed
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