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challenge-generator

Use when generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.

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Datos de origen

Repositorio
yugash007/edu-agent-skills
Última actividad en el origen
18 de mayo de 2026 a las 16:48
Idioma detectado de SKILL.md
inglés
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7
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2

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SKILL.md
Instrucciones de origen · Vista previa de solo lectura
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.
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