difficulty-classifier

Classify exam question difficulty using Ragas-based metrics.

1|Updated Feb 3, 2026

Search Tags:#education #ai #classification #assessment #exam #difficulty #ragas

Ready to run

npx skills add https://github.com/u9401066/anesthesia-exam --skill difficulty-classifier

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: difficulty-classifier
Source: https://github.com/u9401066/anesthesia-exam/tree/main/.claude/skills/difficulty-classifier
Command: npx skills add https://github.com/u9401066/anesthesia-exam --skill difficulty-classifier

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Difficulty classification for exam questions using Ragas-inspired standards to determine whether a question is easy, medium, or hard.

Core Features & Use Cases

Question analysis: Evaluates hop_count, specificity, cognitive level, and distractor quality.
Automated labeling: Outputs a clear difficulty category and supports adjustment suggestions.
Use Case: Educational platforms can auto-tag questions for adaptive testing, review, and curriculum alignment.

Quick Start

Provide a question (or question_id) and receive its difficulty label instantly.

Frequently Asked Questions about difficulty-classifier

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automatically classify exam question difficulty using AI?▼

Automated exam question difficulty classification assigns easy, medium, or hard labels by evaluating hop count, specificity, cognitive level, and distractor quality using Ragas-based metrics. It processes single- and multi-hop questions from educational content or practice banks instantly.

What metrics are used to determine if an exam question is easy, medium, or hard?▼

Difficulty classification relies on Ragas-inspired metrics including hop count, specificity, cognitive level, and distractor quality. Evaluating these four dimensions allows the system to output a clear difficulty category for educational quizzes and practice banks.

Can I use Ragas metrics to tag questions for adaptive testing platforms?▼

Yes, educational platforms can use this automated difficulty labeling to auto-tag questions for adaptive testing, review, and curriculum alignment. It evaluates multi-hop questions and provides adjustment suggestions to ensure accurate difficulty scaling.

How do I analyze multi-hop question difficulty in a practice bank?▼

Analyzing multi-hop question difficulty involves evaluating the question's hop count, specificity, cognitive level, and distractor quality. The system outputs a difficulty category and provides a workflow for automated adjustment suggestions.

Does the difficulty classifier support automated adjustment suggestions for exam questions?▼

Yes, the difficulty classifier provides a workflow for automated adjustment suggestions after labeling. It evaluates cognitive level and distractor quality to output a difficulty category and recommend how to calibrate the question for educational content.

What is the best way to label quiz difficulty levels for curriculum alignment?▼

The best way to label quiz difficulty is applying Ragas-based metrics to assess hop count and cognitive level. This assigns easy, medium, or hard categories to practice bank questions, directly supporting curriculum alignment and review processes.