What problem does it solve? Turning a feature idea or architecture question into a concrete, evidence-backed design and implementation plan is slow and error-prone, especially when technology choices, AI subsystem behavior, and existing code constraints must all be reconciled before coding starts. ## Core Features & Use Cases - Seven-phase design workflow: Understand requirements, inspect the existing system (brownfield) or confirm greenfield context, research unfamiliar technologies, delegate stack and AI-subsystem decisions, design the solution, review it against system-design rules, and produce a /plan handoff. - Scoped specialist delegation: Routes technology due diligence to research, application-stack choices to app-stack, AI subsystem behavior and policy to ai-agent-design, AI component selection to ai-stack, and checklist review to system-design-rules without reopening fixed decisions. - AI capability classification: Classifies every AI-enabled capability as deterministic code, a direct model call, a deterministic workflow, or an agent, following the principle of deterministic where possible and agentic where necessary. - Use Case: You need to add a new API-backed feature to an existing service. The skill inspects the repository, confirms the fixed stack, designs a vertical end-to-end slice, compares alternatives, and emits a /plan handoff another session can implement directly. ## Quick Start Ask the agent to use the design skill to turn your feature request into an evidence-backed design and implementation plan before any code is written.