List all modules in the dataflowr Deep Learning DIY course.

Can be filtered by session number, tag, or GPU requirement. Returns module IDs, titles, descriptions, and tags.

Get full details for a specific module.

Includes description, notebooks with GitHub and Colab links, tags, and prerequisites. Use this when a student asks about a specific topic or module. Module IDs: '12' (Attention/Transformers), '2a' (PyTorch tensors), '18b' (diffusion), etc.

Search modules by keyword across titles, descriptions, and tags.

Use this to find relevant modules for a student's question or topic. E.g. query='attention' finds the Transformer module; 'generative' finds GANs, autoencoders, flows, diffusion.

List all course sessions with their titles and module IDs.

Sessions group modules into ~2-3 hour teaching blocks.

Get full details for a session, including all its modules, notebooks, and key takeaways.

Use this when a student wants to understand what a session covers.

Get the GitHub and Colab URL for a specific notebook.

Use this when a student wants to open or run a specific exercise. kind: 'intro' | 'practical' | 'solution' | 'bonus' | 'homework' (default: practical)

List all graded homeworks with descriptions and notebook links.

Get full details for a specific homework, including description, website URL, and notebook links.

Fetch the lecture slide content for a module from the dataflowr/slides GitHub repo.

Returns the slide text (Remark.js markdown, cleaned). Use this when a student wants to review lecture slides or study the theory for a module.

Fetch the quiz questions for a module from the dataflowr/quiz GitHub repo.

Returns multiple-choice questions with choices, correct answers, and explanations. Use this when a student wants to self-test their understanding of a module. Modules with quizzes: '2a' (tensors), '2b' (autograd), '3' (loss functions).

Fetch the actual content of a course notebook from GitHub.

Returns markdown explanations and (optionally) code cells. Use this when a student wants to understand what a notebook covers, needs help with an exercise, or asks about specific code. kind: 'intro' | 'practical' | 'solution' | 'bonus' | 'homework' (default: practical) include_code: set False for explanations only (default: True)

Fetch only the exercise cells from a module's notebook.

Returns the exercise prompt (markdown) and the skeleton code the student must fill in. Skips all expository text, imports, and solution code. Prefer this over get_notebook_content when helping a student with an exercise — it gives the task without spoiling surrounding context. kind: 'intro' | 'practical' | 'solution' | 'bonus' | 'homework' (default: practical)

Fetch the text content of the course website page for a module.

Returns the lecture notes, explanations, and learning objectives as plain text. Use this for conceptual questions about a topic.

Get a complete overview of the dataflowr course: all sessions, modules, and their relationships.

Use this to understand the full structure or to give a student a learning path.

Compare the local catalog against the dataflowr/website, /slides, and /quiz GitHub repos.

Lists modules/slides present in the repos but missing from the catalog, and catalog entries that have no corresponding source file.

Get the prerequisite modules for a given module, with full details for each.

Use this when a student asks 'what should I know before studying X?' or seems to be missing background knowledge.

Check whether a student's answer to a quiz question is correct.

Returns: correct (bool), the right answer with its text, and an explanation. Call get_quiz_content first to see the questions and choices, then use this tool to validate the student's response. question_number and answer_number are 1-based.

Suggest what to study after completing a given module.

Returns: (1) modules that directly list this one as a prerequisite, (2) the next modules in the same session, and (3) the start of the next session. Use this when a student finishes a module and asks 'what should I do next?'