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CLM - Coding-Academy Lecture Manager

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Version: 1.23.0 | License: MIT | Python: 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
  • Shared-Source Includes: Declare <include source="…" as="…"/> on a <topic> or <section> to splice a canonical Python package (or any file/directory) into multiple topics at build time. clm course sync-includes materializes the same sources on disk so local notebook execution (VS Code, JupyterLab) finds them, with a .clm-include ledger for safe cleanup.
  • Output-Write Deduplication: When the same file is written to the same output path by multiple producers, CLM deduplicates the write and surfaces a output_dedup_count / output_conflicts summary so you can spot accidental cross-topic collisions.
  • Watch Mode: Auto-rebuild on file changes
  • Incremental Builds: Content-based caching
  • LLM Summaries: Generate course summaries with clm export summary using any OpenAI-compatible LLM API
  • Recording Management: Manage video recording workflows with pluggable backends — local ONNX pipeline, iZotope RX 11 external tool, or Auphonic cloud processing — plus assembly, job tracking, and per-course status (clm recordings)
  • MCP Server: Model Context Protocol server for AI-assisted slide authoring (clm mcp) with 16 tools for course navigation, validation, normalization, and bilingual editing
  • Slide Authoring Tools: Split-deck authoring sync (clm slides sync — the funnel that keeps both halves of a .de/.en pair consistent), topic resolution (clm course resolve-topic), fuzzy search (clm slides search), spec/slide validation (clm validate), normalization (clm slides normalize), bilingual language view (clm slides language-view), voiceover extraction (clm voiceover extract), LLM-driven voiceover coverage check (clm slides coverage), bilingual ↔ per-language file conversion (clm slides split / clm slides unify), and structured JSON outlines (clm export outline --format json). Lower-level plumbing (clm slides assign-ids, clm slides suggest-sync) stays available by name for scripts and agents.
  • Video Narration Harvest: Recover spoken narration from recorded videos into slide decks (clm harvest) — an agent-first report → task → accept loop over the deterministic transcribe/detect/match/align pipeline, with multi-part recordings, caching, and a legacy embedded-LLM one-shot (clm harvest autopilot)
  • 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:

For Developers:

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.

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