create-more-exercise

name	create-more-exercise
description	Create deeper follow-up exercises for a chapter or subsection in this course. Use when asked to make additional, more advanced, or more probing exercises for the lecture material.

Create More Exercise

Use this skill to create additional exercises that help students understand a course chapter or subsection more deeply than the existing exercise.

First Step

Start by confirming the target chapter, subsection, or source file. If it is not provided in the user's request, ask for it before creating exercises.

Accept targets such as:

• 5.1
• Section 6
• Variables and state
• a source file under book/

Workflow

1. Read AGENTS.md.

2. Locate the relevant .qmd file under book/.

3. Read the page's explanation, things to look up, exercise, and notes.

4. Create exercises that go deeper than the existing exercise. Do not merely rephrase the current task.

5. Do not provide full solutions unless the user explicitly asks for them.

6. Keep the style consistent with the course:

   • short concept-map explanations,
   • hand-written reasoning before coding,
   • Rust as the default language for small checks,
   • slices for 1D numerical data,
   • tenferro typed tensors, currently tenferro_tensor::TypedTensor, for 2D and higher numerical arrays,
   • no nested vectors such as Vec<Vec<f64>> as the normal numerical-array representation,
   • no long syntax lists or installation procedures,
   • Cargo-testable code when a coding check is needed,
   • validation, boundary cases, and reproducibility when relevant,
   • separated computation and plotting scripts when plots are generated,
   • result file formats that fit the data size and shape.

Output

Provide 2-4 additional exercises. For each exercise, include:

• a short title,
• the task,
• what the student should learn or check,
• brief notes for the exercise.

When the topic involves Rust or scientific coding, prefer exercises that make students reason about inputs, outputs, boundary cases, tests, functions, unnecessary global state, ownership and borrowing, numerical validation, or reproducibility.

For exercises that generate numerical results, prefer a workflow where the calculation writes results and metadata to a file before plotting. Small scalar or tabular outputs may use JSON or plain text; large or multidimensional arrays should use an array or container format such as .npy, .npz, or HDF5.

If the user asks to edit the teaching material, insert the exercise using the page's existing structure and keep the change concise.