challenge-generator

name	challenge-generator
description	Use when generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.
version	1.1.0
authors	["edu-agent-skills contributors"]
tags	["assessment","practice","active-learning","weak-area-targeting"]
status	stable

Purpose

Generate targeted, level-appropriate practice challenges that force application over recall. Challenges must be grounded in learner's weak areas, current project, and chosen difficulty tier.

Activation

• Concept just taught and practice needed. Learner asks for exercises/challenges. check-understanding or misconception-detector flagged a weak area. Interview/exam/milestone prep.
• Skip if: no concept context established → run teach-concept first. Learner is blocked on production issue → debug-teacher.
• Routing: pair with check-understanding to evaluate responses. Escalate to interview-mode for timed pressure practice.

Inputs

• Target concept(s), learner level, known weak areas, project/repo context, preferred type (implement/debug/explain/design).

Challenge Types

• Implement: write code from scratch to satisfy criteria.
• Debug: identify and fix a deliberately broken snippet.
• Explain: articulate behavior, tradeoffs, or mechanism in prose.
• Design: propose architecture or algorithm for given constraints.

Difficulty Tiers

• Beginner: one concept, well-defined, limited scope.
• Intermediate: composite concepts, partially specified, tradeoff thinking required.
• Advanced: ambiguous spec, production constraints, edge-case awareness required.

Workflow

1. Calibrate — Identify target concept(s) from session history or learner request. Select challenge type based on learning objective. Map learner level to difficulty tier.
2. Construct — State challenge clearly: context, constraints, success criteria, time/scope hint. Include starter scaffold where appropriate. Embed at least one non-obvious constraint testing deeper understanding.
3. Hint Ladder — Prepare 2–3 progressive hints (broad→specific) but don't volunteer them. Release only on request or after two failed attempts.
4. Evaluate — Grade reasoning quality, not just correctness. Identify what's right, where reasoning broke down, root cause. Classify: conceptual gap, implementation slip, or edge-case blindness.
5. Advance or Retry — Significant errors: simpler variant or targeted hint, then retry. Clean pass: increase tier or shift to next weak area.
6. Reinforce — Summarize the key insight the challenge surfaced. Record outcome for weak-area-tracker.

Rules

• DO: require active reasoning — not definition recall.
• DO: make success criteria explicit and testable before learner starts.
• DO: state difficulty tier and confirm before starting.
• DO: ground at least one challenge variant in the learner's current project/repo.
• DON'T: reveal the solution before the learner attempts.
• DON'T: release hints before at least one learner attempt.
• DON'T: end without a one-sentence insight summary.
• DON'T: generate generic challenges disconnected from session context.

Output

Responses should contain: context (concept + type + tier + grounding), challenge statement with acceptance criteria, starter scaffold if applicable, checkpoint prompt, and next step after evaluation. Format naturally.

Checklist

• Difficulty tier stated and calibrated.
• Acceptance criteria are measurable.
• Challenge references session context or learner's project.
• Post-challenge insight summary included.