exam-forecast

The saved excerpt is a snapshot from review. The external source remains the complete and most current version.

---
name: exam-forecast
description: >
  Analyze past exams from the same professor to surface patterns — subject
  weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine
  mix — and forecast likely emphases for the upcoming exam. Use when the user
  says "what's on the exam", "analyze past exams", "predict the exam", or
  shares past exams.
argument-hint: "[class name, with past exams shared or paths to them]"
---

# /exam-forecast

1. Load `~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md` → class, professor, exam format, syllabus.
2. Apply the workflow below.
3. Intake past exams (PDF, paste, or paths). Confirm sample size.
4. Analyze each past exam: format, subject coverage, question style, fact-pattern density, recurring traps.
5. Cross-exam pattern analysis — what's stable, what varies.
6. Combine with current syllabus to produce forecast: subject weights, format, hobby horses, study emphasis.
7. Write `~/.claude/plugins/config/claude-for-legal/law-student/exam-forecasts/[class]/forecast-[YYYY-MM-DD].md`. Framed as weighting heuristic, not prediction.

---

## Purpose

Every professor's exam has fingerprints. The same hypo structures recur. The same traps come back. The same subject ratios repeat. Students who have prior exams study smarter; students who don't, study harder. This skill analyzes the prior exams you have and surfaces the patterns.

Not magic. A forecast, not a prediction. The skill cannot tell you what's on the exam — it can tell you what's been on past exams and what's likely to recur based on syllabus coverage.

## Confidence discipline

- Pattern analysis (what subjects appeared, how many questions per topic, how often policy vs. rule-application) — confident where the exams are clearly in front of me.
- Inference about likely emphasis on upcoming exam — `[UNCERTAIN]` is the default; these are forecasts, not certainties. Explicitly frame as "based on the [N] past exams you shared, [topic] appeared in [M]. Your upcoming exam may emphasize it, or the professor may rotate — use this as a weighting for review time, not a prediction."
- If only 1-2 past exams are available, say so explicitly — any pattern inferred from 1 exam is noise.
- If the professor is new (no past exams available), skill can't forecast. Say so; fall back to syllabus-based "these are the subjects covered" only.

## Load context

- `~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md` → current classes, exam formats, syllabus if captured
- User-provided past exams (PDF, pasted text, paths)
- Optional: syllabus for the current class (for "what's been covered to date")

**If the uploaded past exams have a professor's name, use it to match patterns** (same-professor exams are the highest-signal input). **If not, match on subject and structure.** Don't ask the user to type in the professor's name — use what's in the materials. If the user volunteers it in conversation that's fine; don't prompt for it.

## Workflow

### Step 1: Intake

- Which class are we forecasting for?
- How many past exams from this professor are available?
- Are they from the same course, or different courses by the same professor?
- Are any of them the take-home / open-book / different-format variants, vs. the typical format for your upcoming exam?
- Syllabus for your current class?

If fewer than 3 past exams: flag as thin sample. Pattern inference is weaker.
If exams are across different courses: some patterns transfer (question style, policy vs. doctrine ratio); subject-specific patterns don't.

### Step 2: Read each past exam

For each past exam:

- Format (number of questions, length, time limit, open/closed book)
- Subject coverage (which topics tested, in what proportion)
- Question style (issue-spotter, single-issue deep, policy essay, short-answer MBE-style, mix)
- Fact pattern density (fact-heavy hypos, sparse facts with doctrinal focus, or policy prompts with no facts)
- Recurring traps (e.g., professor alway