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atomercore/exam2knowledge-skill

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Exam2Knowledge is a skill that helps students, teachers, and tutors convert exam questions into structured, review-ready knowledge summaries.

개요

"See question → Recognize pattern → Apply method → Get answer" Transforms exam questions into . Not systematic learning — this builds a for instant problem-solving. Past exam papers, practice questions, course materials → High-frequency test points → Strategic study plan 1. — Identify question type & cognitive level (Bloom's Taxonomy) 2. — Extract & tier knowledge points ([**Solution Template**Error Pattern Library**Cross-Question Intelligence**Exam-Ready Output** — Prioritized action framework (Output Template) - 🎯 with strict execution order - 🔀 — output adapts to input type & time constraint - ✅ — format, content, consistency, boundary verification - 📚 — [] / [*] frequency-based ranking - 🛡️ — explicit "can do / cannot do" declaration - ✅ Single/multiple questions or full exam papers (text format) - ✅ OCR results from handwritten exams - ✅ All subjects: Math, Physics, Chemistry, Biology, History, Languages... - ❌ Raw image input (require OCR text first)

README

원문 README의 일부입니다. 원문 보기.

🎓 Exam2Knowledge v1.0.0

中文 | English

“See question → Recognize pattern → Apply method → Get answer”


✨ What It Does

Transforms exam questions into structured, exam-focused knowledge systems. Not systematic learning — this builds a pattern-recognition reflex for instant problem-solving.

Perfect for: Past exam papers, practice questions, course materials → High-frequency test points → Strategic study plan

📁 Structure

.agents/skills/Exam2Knowledge/
├── SKILL.md                  # Core definition + processing pipeline
├── assets/
│   └── output-template.md    # Standardized output structure
└── references/
    ├── blooms-taxonomy.md    # 6-level cognitive hierarchy
    ├── diagnostic-rubric.md  # 5-category error classification
    └── quantified-rubric.md  # Quantified self-check standards

⚙️ How It Works

  1. Pattern Recognition — Identify question type & cognitive level (Bloom’s Taxonomy)
  2. Reverse Engineering — Extract & tier knowledge points ([**] Must-Master to [] Optional)
  3. Solution Template — Build universal steps + decision rules
  4. Error Pattern Library — Catalog mistakes by category (Diagnostic Rubric)
  5. Cross-Question Intelligence — Frequency ranking + clusters + trends (batch mode; self-check standards in Quantified Rubric)
  6. Exam-Ready Output — Prioritized action framework (Output Template)

🎯 Decision Rules

Scenario Action
Single question Full 5-step analysis + solution template
Multiple questions (≥5) + frequency statistics + clusters
Weak spot diagnosis Strengthen error pattern analysis
Image/OCR input Verify OCR accuracy first
Humanities subject Switch to humanities analysis mode

✨ Key Features

  • 🎯 5-step processing pipeline with strict execution order
  • 🔀 Conditional logic — output adapts to input type & time constraint
  • ✅ Built-in self-check — format, content, consistency, boundary verification
  • 📚 Tiered knowledge — [] / [] / [] frequency-based ranking
  • 🛡️ Capability boundaries — explicit “can do / cannot do” declaration

📥 Supported Input

  • ✅ Single/multiple questions or full exam papers (text format)
  • ✅ OCR results from handwritten exams
GitHub에서 전체 README 보기

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