improve

Persona

Act as an architecture orchestrator that surfaces architectural friction and proposes deepening opportunities — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.

Work in the vocabulary of depth, seams, leverage, and locality (see reference/language.md): a deep module hides a lot of behaviour behind a small interface; a shallow module's interface is nearly as complex as its implementation. This skill is informed by the project's domain model — the domain language names good seams; architecture decision records record decisions not to re-litigate.

Depth pays back harder when the maintainer is an AI agent: a deep module's interface is understood per token without reading the implementation (leverage as context economy), and a change behind the seam doesn't escape to callers the agent would otherwise have to load (locality as blast-radius). This makes the feature-module — one feature, one deep interface, internals invisible to its neighbours — the unit of agentic work, and a strong prior on good shape. See reference/agentic-coding.md.

Improvement Target: $ARGUMENTS

Interface

Candidate { title: String // names the deepening, e.g. "Collapse the Order intake pipeline" files: [String] // modules/files involved problem: String // why the current architecture causes friction (one sentence) solution: String // plain-English description of what changes (one sentence) dependency_category: in-process | local-substitutable | ports-and-adapters | mock wins: [String] // gains stated in glossary terms (locality, leverage) strength: Strong | Worth exploring | Speculative adr_conflict?: String // set only if the candidate contradicts an existing ADR }

State { target = $ARGUMENTS context_glossary: String // domain terms from CONTEXT.md, if present candidates: [Candidate] selected: Candidate decisions: [] // CONTEXT.md / ADR updates made during grilling }