| name | scaffold-exercises |
| description | 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 โ NOT for grading submissions or auto-checking student answers. |
| argument-hint | [topic] [--difficulty intro|core|advanced] [--count N] [--types analytical,empirical,coding] [--dataset path] [--no-solutions] |
| allowed-tools | ["Read","Grep","Glob","Write","Bash"] |
| effort | medium |
/scaffold-exercises โ 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 โ the primary lens is graded coursework that mixes derivation, estimation, and code.
Input: $ARGUMENTS โ a topic (e.g., "instrumental variables", "consumer theory", "staggered DiD") 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 โ 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 โ Problem Set
**Topic:** [topic]
**Source(s) read:** [lecture/paper/dataset โ one-line takeaway each]
**Difficulty:** intro | core | advanced
**Counts by type:** analytical=N, empirical=N, coding=N (total = `--count`)
**Dataset:** [provided path | simulated with seed YYYYMMDD | none]
**Learning objectives:** [2-4 bullets the set should exercise]
Resolve every flag here (interactive choices are gathered before generation, not mid-run). If the topic is too vague to write objectives, ask one clarifying question and stop. Otherwise proceed.
Phase 1: Generate problems
For each problem, write a number, a section heading, the prompt, and any data/notation it needs. Conventions:
- Motivation before mechanics โ one sentence on why the problem is worth solving, matching
create-lecture's pedagogy.
- Notation reuse โ match symbols to the source lecture; never introduce a clashing symbol for an already-defined object.
- Difficulty calibration โ intro checks one concept; core chains 2-3 steps; advanced requires a non-obvious insight or identification argument.
- Self-contained โ each problem states its own assumptions; no "as in lecture 4" dangling references.
Phase 2: Generate worked solutions + explainers
For every problem, write:
- A worked solution โ full derivation, expected estimate, or runnable code (depending on type). Coding solutions must actually run; if
Bash + R/Stata are available, execute the snippet and paste real output.
- A "why this matters" explainer โ 1-2 sentences linking the answer to the broader concept (the imported pattern's signature: every problem ships with a short rationale, not just a number).
Phase 3: Write student set + solution key
Emit two files (paths configurable; default under the working directory):
exercises/<topic-slug>_problems.md โ the student set: sections, problems, any data, NO answers.exercises/<topic-slug>_solutions.md โ the solution key: each problem restated, its worked solution, and its explainer.
The split is load-bearing: never leak a solution into the student file. With --no-solutions, write only the student set and stop.
Output / Report format
Student set:
# Problem Set: [Topic] (Difficulty: core)
## Section 1 โ Analytical
**1.** [Motivation sentence.] [Prompt.]
## Section 2 โ Empirical
**2.** Using `data/<file>` (vars: ...), [estimate + interpret prompt].
## Section 3 โ Coding (R)
**3.** [Implement-X prompt.]
Solution key mirrors the numbering, adding ### Solution and > Why this matters: blocks per problem. Close your chat reply with a one-line manifest: files written, problem count by type, and whether code solutions were executed or only drafted.
Exit behavior
- Print the two output paths (absolute), the per-type counts, and the seed if a dataset was simulated.
- If a coding solution could not be executed (no R/Stata, or it errored), flag it as DRAFTED โ NOT RUN rather than implying it was verified.
- If any empirical problem references variables not present in the supplied dataset, stop and surface the mismatch instead of inventing columns.
Flags
--difficulty โ intro | core | advanced (default core); calibrates step depth as in Phase 1.--count โ total number of problems (default 6); split across types per the Pre-Flight counts.--types โ comma-separated subset of analytical,empirical,coding (default all three).--dataset โ path to a real dataset for empirical problems; omit to simulate one with a seeded DGP.--no-solutions โ write only the student set; skip the solution key (Phase 2/3 key file).
Cross-references
What this skill does NOT do
- Does not grade student submissions or auto-check answers against a key.
- Does not run a timed exam or enforce assessment policy (point weights, rubrics, proctoring).
- Does not invent data โ empirical problems use a supplied dataset or an explicitly seeded simulation, never fabricated numbers.
- Does not leak solutions into the student file, and does not deploy/publish anything (no
/deploy).
- Does not auto-invoke other skills โ it references siblings; it does not call them.
