exercise-designer

Design exercises by applying evidence-based learning frameworks like retrieval practice, spaced repetition, and interleaving. This Skill automates that process, generating varied exercise types (fill-in-blank, debug-this, build-from-scratch) sequenced by difficulty with concrete objective mappings and evaluation criteria aligned to cognitive science research.

Generate fill-in-blank, debug-this, build-from-scratch, extend-code, and AI-collaborative exercises. Each type targets specific learning objectives and Bloom's Taxonomy levels, with difficulty progression from easy to hard. The Skill produces complete sets including test cases and rubrics for assessment.

Yes. Specify your target topic (e.g., dictionaries) and prior concepts to review (e.g., lists, conditionals). The Skill designs exercise sets incorporating spaced repetition of earlier material while introducing new content, ensuring comprehensive preparation for evaluation.

Define your evaluation criteria upfront. The Skill generates exercises with explicit objective mappings and validation checks that align practice problems directly to your assessment rubrics, reducing disconnect between homework and grading.

Sequence problems by cognitive load and exercise type. This Skill handles that structure automatically—mixing problem formats, applying interleaving principles, and scaffolding difficulty so intermediate learners encounter diverse practice modes before moving to harder concepts.

No. The Skill generates complete exercise sets that include test cases and rubrics as part of the design output, eliminating manual effort in defining evaluation criteria and validation checks for each problem.