Design

Designs deliberate practice exercises applying evidence-based learning strategies like retrieval practice, spaced repetition, and interleaving. Activate when educators need varied exercise types (fill-in-blank, debug-this, build-from-scratch, extend-code, AI-collaborative) targeting learning objectives with appropriate difficulty progression. Creates exercise sets that apply cognitive science principles to maximize retention and skill development. Use when designing practice activities for Python concepts, creating homework assignments, generating problem sets, or evaluating exercise quality.

Design reusable prompt templates that encode domain-specific patterns for recurring AI tasks. Use when you've executed similar prompts 2+ times and need to capture the pattern as reusable intelligence. NOT for one-off prompts or generic "ask AI a question" patterns.

Orchestrates JUCE plugin implementation through stages 1-3 (Foundation, DSP, GUI) using subagent delegation with automatic validation after each stage. Use when implementing plugins after planning completes, or when resuming with /continue command. Invoked by /implement command.

Use when implementing polymorphism and interfaces in object-oriented design. Use when creating flexible, extensible systems with interchangeable components.

Use when designing solutions, adding features, or refactoring by applying KISS, YAGNI, and Principle of Least Astonishment to write simple, predictable code.

Use when demonstrating plugin workflow features - shows how skills can guide multi-step processes

Use when implementing Apollo caching strategies including cache policies, optimistic UI, cache updates, and normalization.

Use when building iOS apps with UIKit, implementing MVVM/MVC/Coordinator patterns, or integrating UIKit with SwiftUI.

Use when building comprehensive monitoring and observability systems.

Use when oTP behaviors including gen_server for stateful processes, gen_statem for state machines, supervisors for fault tolerance, gen_event for event handling, and building robust, production-ready Erlang applications with proven patterns.

Use when domain-specific language design in Kotlin using type-safe builders, infix functions, operator overloading, lambdas with receivers, and patterns for creating expressive, readable DSLs for configuration and domain modeling.

Build and train neural networks with TensorFlow

Use when designing modules and components requiring Composition Over Inheritance, Law of Demeter, Tell Don't Ask, and Encapsulation principles that transcend programming paradigms.

This skill should be used when planning and tracking complex feature implementations that require systematic task decomposition. Use this skill to break down large features into manageable, well-documented tasks with clear dependencies, action items, and success criteria. The skill provides a structured template and methodology for iterative planning and tracking throughout implementation.

Use when configuring Credo static analysis for Elixir projects requiring customized code quality rules and settings.

Use when Elixir Ecto patterns including schemas, changesets, queries, and transactions. Use when building database-driven Elixir applications.

Use when working with React Native Web projects. Provides core concepts, components, and cross-platform patterns for building web applications with React Native.

Use when configuring Expo apps with app.json, app.config.js, and EAS configuration. Covers app metadata, plugins, build configuration, and environment variables.

Use when applying Checkstyle built-in checks for Java code including naming conventions, code metrics, and suppressions.

Use when designing or reviewing a backend MVP with tight budget, evolving schema, and reliance on third-party backends where idempotency, replay, and responsibility attribution are high-risk.