assessment-builder

Generate Python assessments with varied item types, answer keys, and rubrics.

7|1|Updated Feb 14, 2026

Search Tags:#education #python #assessment #rubrics #blooms-taxonomy #distractor-design

Ready to run

npx skills add https://github.com/SARAMALI15792/AINativeBook --skill assessment-builder-saramali15792

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: assessment-builder
Source: https://github.com/SARAMALI15792/AINativeBook/tree/main/.qwen/skills/assessment-builder
Command: npx skills add https://github.com/SARAMALI15792/AINativeBook --skill assessment-builder-saramali15792

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyYAML, and includes scripts (resource) components.

What problem does it solve?

Educators need a robust method to design diverse, Bloom-aligned assessments for programming concepts that reliably measure understanding and ability.

Core Features & Use Cases

Generate question sets with multiple formats (MCQ, code-completion, debugging, projects) mapped to learning objectives.
Provide distractor design based on common misconceptions, rubrics for open-ended items, and an answer key with explanations.
Validate cognitive distribution and instructional alignment to ensure 60%+ non-recall questions and rubric presence.

Quick Start

Create a complete 6-question Python assessment with varied items, an answer key, and rubrics.

Frequently Asked Questions about assessment-builder

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

FAQPage Schema

How do I generate Python assessments with varied item types and rubrics?▼

To generate Python assessments with varied item types and rubrics, you can design a structured assessment pipeline that outputs MCQs, code-completion, and debugging tasks mapped to learning objectives, complete with an answer key and analytic rubrics.

What is Bloom's taxonomy alignment for programming assessments?▼

Bloom's taxonomy alignment for programming assessments is a validation process ensuring cognitive distribution measures understanding and ability. It validates that 60%+ of questions are non-recall, mapping items to specific learning objectives.

How to design MCQ distractors based on common misconceptions in Python?▼

To design MCQ distractors based on common Python misconceptions, the assessment pipeline applies structured distractor design. This generates plausible incorrect options targeting specific student misunderstandings alongside the correct answer key.

Can I use Python to validate cognitive distribution and instructional alignment for exams?▼

Yes, you can use Python to validate cognitive distribution and instructional alignment for exams. The pipeline automatically checks that 60%+ of questions are non-recall and verifies that analytic rubrics are present for open-ended items.

What's the best way to build a complete assessment package with an objective-to-question mapping?▼

The best way to build a complete assessment package with objective-to-question mapping is to use an automated validation pipeline. It outputs the question set, answer key with explanations, and the objective mapping in one structured package.

Do I need PyYAML to create Bloom-aligned Python assessments?▼

Yes, you need PyYAML installed to create Bloom-aligned Python assessments. It serves as the required dependency for the scripts that design the assessment pipeline, validate instructional alignment, and output the assessment package.

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