# Exam Intelligence

> Analyze historical university exams, solutions, notes, and course material to discover recurring exam patterns, historical frequencies, representative solution methods, high-yield study topics, and target-grade study plans.

- Skill: `instinctex/exam-intelligence` (Agent Skill, multi-file: 9 files)
- Install (CLI): `npx skillmds@latest add instinctex/exam-intelligence`
- Raw SKILL.md: https://api.skillmd.com/api/skills/instinctex/exam-intelligence/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: InstinctEx (https://skillmd.com/u/instinctex)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/instinctex/exam-intelligence

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# Exam Intelligence

Use this skill when a student asks to analyze past exams, identify SOS material,
find repeated exercises or solution patterns, compare exam years, decide what to
study first, build a target-grade or cram plan, or generate pattern-based practice.

Historical frequency is not a prediction probability. Say, for example, “Taylor
appeared in 9 of 11 analyzed exam variants (81.8% historical occurrence),” never
“Taylor has an 81.8% chance of appearing.”

## Operating boundary

The agent performs semantic work: inspect source files, preserve exact problem
text, split every subquestion, understand notation, select topic/pattern/method,
estimate qualitative difficulty and confidence, and link relevant solutions.
Python performs deterministic work: validate, count, group, rank, cache, detect
duplicate candidates, create plans, and generate reports. The core needs no API
key and course material stays local unless the user explicitly configures an
external service.

Read only the material needed for the current stage. Do not load an entire large
course into context. Prefer native or parsed text, inspect pages/images when
needed, and use OCR only as a fallback. Never silently repair an unreadable symbol;
lower confidence and mark it for review.

## Workflow

1. Inventory sources with `exam-intelligence add PATH --type exam|notes|solution|book`.
2. Inspect `.exam-intelligence/sources.json` and cached text under
   `.exam-intelligence/cache/text/` progressively.
3. Extract exams and independent subquestions into the contract documented in
   `references/extraction.md`. Preserve source file, page and raw text.
4. Classify each question using existing patterns first. Pattern IDs describe a
   transferable solution strategy, not constants or a particular exam year.
5. Validate low-confidence assignments and alternatives. Set `needs_review`
   through confidence below 0.70; never invent marks.
6. Write `{ "exams": [...], "questions": [...], "patterns": [...] }` JSON and run
   `exam-intelligence import-data FILE`.
7. Run `exam-intelligence doctor`, then `exam-intelligence analyze` and
   `exam-intelligence report`.
8. Inspect solutions/notes only for the highest-value patterns. Prefer professor
   solutions, solved exams and course notes over generic knowledge. Surface
   multiple solution methods when supplied sources conflict.
9. Use `exam-intelligence plan --target GRADE --hours HOURS` or `cram`.
10. Explain uncertainty and trace recommendations back to historical questions.

Manual corrections made with `set-pattern` must not be overwritten by a later
agent import. Add genuinely new course patterns centrally only after checking for
semantic equivalence. Generated exercises must be labelled “Generated practice
question” and must never masquerade as historical material.

Consult `references/methodology.md`, `references/taxonomy.md`,
`references/scoring.md`, `references/extraction.md`, and
`references/output-format.md` as needed.