Skill 详情
exam-forecast
Supports exam preparation, but only for law courses with past professor exams.
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SKILL.md
这段内容是审核时保存的快照。外部来源才是完整且最新的版本。
--- 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在 GitHub 阅读完整来源 (打开外部页面)