Scaffold Exercises
Scaffold a graded problem set with sections, problems, worked solutions, and short "why...
Install
npx skills add pedrohcgs/claude-code-my-workflow --skill scaffold-exercises -g -a claude-code -y- Repository
- pedrohcgs/claude-code-my-workflow1.7k
- Category
- Engineering
- Updated
- 1mo ago
- License
- MIT
- Context cost
- Mediumabout 1,824 tokens
- Runs scripts
- No, instructions only
What it does
Scaffold a graded problem set with sections, problems, worked solutions, and short "why this matters" explainers across analytical, empirical, and coding types. Use when user says "make a problem set on X", "scaffold exercises for this lecture", "create practice problems", "generate homework with a solution key", "build a graded assignment on topic Y". Emits a clean student set plus a separate solution key, NOT for grading submissions or auto-checking student answers.
/scaffold-exercises, Problem Set Scaffolder
Generate a graded problem set as two files: a clean student set (problems only) and a solution key (worked solutions + a one-line explainer per problem). Pattern imported from mattpocock/skills, adapted for economics teaching, the primary lens is graded coursework that mixes derivation, estimation, and code.
Input: $ARGUMENTS, a topic (e.g., "instrumental variables", "consumer theory", "quantile regression") and optional flags. See Flags.
When to use
- You have a lecture or reading and want a matching assignment with an answer key.
- You want a mix of problem types (derive, estimate, code) at a controlled difficulty, with solutions emitted separately so the student file stays clean.
Do not use this to grade submissions, auto-check answers, or build a timed exam, it scaffolds practice/graded material, not assessment infrastructure.
Problem types
| Type | What the student does | Solution artifact |
|---|---|---|
| analytical | Derive / prove / characterize (theory: optimization, identification, comparative statics) | Step-by-step derivation with the key lemma named |
| empirical | Estimate + interpret on a provided or simulated dataset | Expected estimate, sign/magnitude reasoning, common-mistake note |
| coding | Implement an estimator or simulation in R or Stata | Runnable reference snippet + expected output shape |
If no dataset is supplied for an empirical problem, generate a small simulated one with a fixed seed (YYYYMMDD) so the answer key is deterministic and reproducible.
Workflow
Phase 0: Set topic, difficulty, counts, types (Pre-Flight)
Read any source material the user points at (lecture .tex/.qmd, a paper, a dataset header) and produce a Pre-Flight Report before generating problems:
## Pre-Flight Report, Problem Set
