--- name: pedrohcgs/scaffold-exercises source: https://app.decimal.ai/s/pedrohcgs-scaffold-exercises@1/SKILL.md source_sha256: 50d5aae4604c --- # `/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", "staggered DiD") and optional flags. See [Flags](#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: ```markdown ## Pre-Flight Report — Problem Set **Topic:** [topic] **Source(s) read:** [lecture/paper/dataset — 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** — one sentence on why the problem is worth solving, matching `create-lecture`'s pedagogy. - **Notation reuse** — match symbols to the source lecture; never introduce a clashing symbol for an already-defined object. - **Difficulty calibration** — *intro* checks one concept; *core* chains 2-3 steps; *advanced* requires a non-obvious insight or identification argument. - **Self-contained** — each problem states its own assumptions; no "as in lecture 4" dangling references. ### Phase 2: Generate worked solutions + explainers For every problem, write: 1. A **worked solution** — 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. 2. A **"why this matters" explainer** — 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/_problems.md` — the **student set**: sections, problems, any data, NO answers. - `exercises/_solutions.md` — 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: ```markdown # Problem Set: [Topic] (Difficulty: core) ## Section 1 — Analytical **1.** [Motivation sentence.] [Prompt.] ## Section 2 — Empirical **2.** Using `data/` (vars: ...), [estimate + interpret prompt]. ## Section 3 — 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 — 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` — `intro` | `core` | `advanced` (default `core`); calibrates step depth as in Phase 1. - `--count` — total number of problems (default 6); split across types per the Pre-Flight counts. - `--types` — comma-separated subset of `analytical,empirical,coding` (default all three). - `--dataset` — path to a real dataset for empirical problems; omit to simulate one with a seeded DGP. - `--no-solutions` — write only the student set; skip the solution key (Phase 2/3 key file). --- ## Cross-references - [`.claude/skills/create-lecture/SKILL.md`](../create-lecture/SKILL.md) — build the lecture these exercises practice; shares notation-reuse + motivation-first conventions. - [`.claude/skills/data-analysis/SKILL.md`](../data-analysis/SKILL.md) — for empirical problems whose reference solution needs a full R estimation pipeline. - [`.claude/skills/simulation-study/SKILL.md`](../simulation-study/SKILL.md) — when a problem demonstrates an estimator's finite-sample behavior; reuse its seeded-DGP discipline. - [`.claude/skills/lit-review/SKILL.md`](../lit-review/SKILL.md) — source advanced problems from current papers on the topic. - [`.claude/skills/interview-me/SKILL.md`](../interview-me/SKILL.md) — turn a fuzzy "I want a set on…" into concrete learning objectives first. - [`templates/skill-template.md`](../../../templates/skill-template.md) — house style for authoring/extending this skill. --- ## 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** — 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 — it references siblings; it does not call them.