/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 always hides the jurisdictional issue in an otherwise-clean fact pattern; professor always asks about the exception rather than the rule)
• Policy vs. doctrine ratio
• Unusual structures (essays + MBE hybrid, moot court scenario, etc.)

Step 3: Cross-exam pattern analysis

Roll up what's consistent across exams:

Stable patterns (appeared in most/all past exams):

• Subject weights (e.g., "consideration and modification account for 30% of exam points consistently")
• Question style (e.g., "always one long issue-spotter + two short-answer hypos")
• Professor hobby horses (e.g., "always tests third-party beneficiaries even when it's a minor topic in class")

Variable patterns (appeared in some but not all):

• Policy essays (e.g., "appeared in 2 of 4 past exams — usually when the semester covered a policy-heavy topic late")
• Open-book vs. closed-book differences
• Take-home vs. in-class differences

Absent patterns worth noting:

• Topics covered in class that have NEVER been tested in past exams — don't skip these, but don't weight them heavily either
• Topics tested in past exams that aren't in your current syllabus — probably not coming back

Step 4: Forecast for the upcoming exam

Header — required, first line of the forecast, both in-chat and in the saved file. Per plugin config ## Outputs, every study output carries the verbatim study-notes header. The forecast is a study output. Do not omit, rephrase, or relocate the header. The header is not a disclaimer the student can ask to drop; it is the output's identity and prevents the forecast from being mistaken for a predicted exam or for legal advice:

STUDY NOTES — NOT LEGAL ADVICE

Combine pattern analysis with current syllabus:

STUDY NOTES — NOT LEGAL ADVICE

# Exam Forecast — [class / professor] — [date]

**Past exams analyzed:** [N]
**Sample confidence:** [thin (<3) / moderate (3-5) / strong (6+)]
**Caveats:** [e.g., "one of the past exams was an open-book final; your upcoming is closed-book. Pattern transfer is partial."]

---

## Subject weighting (historical)

| Topic | Past exam weight (avg) | In current syllabus? | Forecast weight |
|---|---|---|---|
| [topic 1] | [%] | [yes/partial/no] | [heavier / stable / lighter] |

## Question-style forecast

- **Format likely:** [X issue-spotters + Y short answers + Z policy, or similar]
- **Fact-pattern density:** [fact-heavy / sparse / mixed]
- **Call style:** [one broad call / multiple specific calls / bullet sub-parts]

## Professor hobby horses to watch

- [topic A] — appeared in [M of N] past exams. Weighted 3-5x its syllabus share.
- [topic B] — [pattern]
- [trap pattern] — e.g., "hides jurisdictional issue in otherwise-clean facts"

## Topics covered this semester but rarely tested

[list — don't skip, but don't over-weight]

## Study emphasis recommendation

Based on past exam patterns AND current syllabus coverage:

**Heavy:** [topics likely to anchor the exam — 40-50% of study time]
**Moderate:** [supporting topics — 30-40%]
**Sanity check:** [topics covered but historically under-represented — 10-20%, just in case]

## [UNCERTAIN — framing]

This forecast is derived from [N] past exams. Professors vary. Professors rotate. Topics that were emphasized in past years can be de-emphasized when the syllabus shifts. Treat this as a weighting heuristic for study time, not a prediction. The exam will include surprises.

Step 5: Output location

Write to ~/.claude/plugins/config/claude-for-legal/law-student/exam-forecasts/[class]/forecast-[YYYY-MM-DD].md. Versioned — if the student gets another past exam mid-semester, re-run and append.

Integration

• outline-builder: forecast weights feed into outline depth decisions — weight depth on heavy topics
• flashcards: forecast-heavy topics get more cards generated
• bar-prep-questions: irrelevant for bar prep (that has its own forecast model); exam-forecast is for class-specific finals
• irac-practice: use forecast topics as the subject areas for IRAC practice hypos

Close with the next-steps decision tree

End with the next-steps decision tree per CLAUDE.md ## Outputs. Customize the options to what this skill just produced — the five default branches (draft the X, escalate, get more facts, watch and wait, something else) are a starting point, not a lock-in. The tree is the output; the lawyer picks.

What this skill does not do

• Predict specific questions. Past exams show patterns; they don't show you tomorrow's prompt.
• Work without past exams. If you don't have prior exams from this professor, the skill can't forecast — it falls back to "here's what the syllabus covers, study that."
• Replace studying everything on the syllabus. Forecast is weighting, not elimination. Skipping a topic because it's historically under-represented is how students get burned.
• Account for changes you don't know about. If the professor has shifted focus this year (e.g., emphasized a new case in class lectures), the skill doesn't see that unless you tell it.
• Work reliably with 1-2 past exams. Thin sample. Flag as such.