# Adaptive Hint Sequence Designer

Generate a cascading hint sequence for a problem type, revealing progressively without...

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

## Description

Generate a cascading hint sequence for a problem type, revealing progressively without giving answers. Use when designing tutoring dialogues or scaffolded worksheets.

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

# Adaptive Hint Sequence Designer

## What This Skill Does

Designs a cascading hint sequence for a specific problem type, a series of progressively more revealing hints that help students move past sticking points without simply giving them the answer. This is one of the most technically demanding aspects of intelligent tutoring system (ITS) design. The critical insight from VanLehn's (2011) meta-analysis is that the effectiveness of tutoring (human or AI) depends heavily on the quality of the scaffolding, and hint sequences are the primary scaffolding mechanism. Get the sequence wrong and you either frustrate students (hints too vague, too few) or steal their learning (hints too specific, too early). The output includes the complete hint cascade (typically 3, 5 levels from general strategic guidance to specific procedural nudge), a design rationale explaining the cognitive function of each level, trigger conditions (when each hint fires), and a bottom-out strategy (what happens when hints are exhausted). AI is specifically valuable here because designing effective hint sequences requires simultaneously anticipating student errors, calibrating hint specificity, and ensuring that each hint level provides just enough information to unstick the student without bypassing the cognitive work that produces learning.

## Evidence Foundation

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