# Erroneous Example Designer

Design deliberately flawed examples that develop error-detection skills and deepen...

- Repository: https://github.com/GarethManning/education-agent-skills (939 stars)
- Category: Design
- License: see repository
- Last updated: 2026-10-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add GarethManning/education-agent-skills --skill erroneous-example-designer -g -a claude-code -y`
Codex: `npx skills add GarethManning/education-agent-skills --skill erroneous-example-designer -g -a codex -y`
Cursor: `npx skills add GarethManning/education-agent-skills --skill erroneous-example-designer -g -a cursor -y`
Gemini CLI: `npx skills add GarethManning/education-agent-skills --skill erroneous-example-designer -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add GarethManning/education-agent-skills`

## Description

Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Erroneous Example Designer

## What This Skill Does

Designs worked examples that contain deliberate, realistic errors for students to identify, explain, and correct, a technique that produces learning effects comparable to or exceeding correct worked examples, with the additional benefit of developing error-detection skills. The critical insight from McLaren et al. (2012, 2015) is that errors must be REALISTIC and COMMON, the kinds of mistakes students actually make, not contrived errors that no one would make. A well-designed erroneous example activates self-explanation (Chi et al., 1989): students must reason about WHY the step is wrong, which forces deeper processing than simply following a correct procedure. The output includes the erroneous examples with realistic errors at specific steps, an error analysis scaffold (prompts that guide students to find and correct the error), the learning mechanism explanation, and the corrected version. AI is specifically valuable here because designing effective erroneous examples requires deep knowledge of the common error patterns for specific problem types, which errors are realistic, which are productively confusing, and which would create harmful misconceptions.

## Evidence Foundation

The rest: https://github.com/GarethManning/education-agent-skills/blob/HEAD/skills/ai-learning-science/erroneous-example-designer/SKILL.md