| 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
- Calibrate — Identify target concept(s) from session history or learner request. Select challenge type based on learning objective. Map learner level to difficulty tier.
- 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.
- Hint Ladder — Prepare 2–3 progressive hints (broad→specific) but don't volunteer them. Release only on request or after two failed attempts.
- 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.
- Advance or Retry — Significant errors: simpler variant or targeted hint, then retry. Clean pass: increase tier or shift to next weak area.
- 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
