> 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.
npx skills add https://github.com/anthropics/claude-for-legal --skill exam-forecast
~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md → class, professor, exam format, syllabus.~/.claude/plugins/config/claude-for-legal/law-student/exam-forecasts/[class]/forecast-[YYYY-MM-DD].md. Framed as weighting heuristic, not prediction.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.
[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."~/.claude/plugins/config/claude-for-legal/law-student/CLAUDE.md → current classes, exam formats, syllabus if capturedIf 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.
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.
For each past exam:
Roll up what's consistent across exams:
Stable patterns (appeared in most/all past exams):
Variable patterns (appeared in some but not all):
Absent patterns worth noting:
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.
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.
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.
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Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
Build and distribute Expo development clients locally or via TestFlight
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
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Take anthropics/exam-forecast from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.