code

Interactive codebase pruning analysis — dead code, unused dependencies, speculative abstractions, scope boundaries, and prioritized removal plan

Generate a context-aware self-review checklist before code review

Structured code smell detection on recent changes

Systematic tradeoff analysis for design decisions

Reviews code quality using pillar-based criteria — readability, naming, error handling, antipatterns, and testability. Use during serve phase for enhanced quality feedback beyond structural review.

Antipattern catalog with categorized patterns, symptoms, examples, and fixes. Use when reviewing code for quality issues, detecting bad patterns during plan-audit or scope, evaluating whether a pattern is an antipattern or acceptable tradeoff, or fixing existing code smells. Covers surprise, misuse, complexity, and premature antipattern categories with severity classification.

Naming decisions for variables, functions, classes, constants, and APIs. Use when choosing names, reviewing naming quality, refactoring unclear names, or establishing naming conventions. Covers descriptive naming, naming as documentation, language-specific conventions, and common naming antipatterns.

Plan quality auditing with completeness scorecard, antipattern risk detection, and readiness checks. Use when auditing implementation plans, evaluating plan completeness, checking error handling strategy, assessing naming and readability pre-checks, reviewing testing strategy, evaluating tradeoff documentation, or running plan-audit on a software project. Covers the 10-point completeness scorecard, code quality pre-checks, antipattern risk assessment, and build-readiness decision.

Dead code detection strategies, safe removal processes, dependency pruning, and bloat metrics for systematically reducing codebase dead weight. Use when evaluating codebase health during architecture-audit, performing cleanup during cook, reviewing unused dependencies, removing commented-out code, or measuring code bloat.

Code quality pillars, goals, abstraction layers, and tradeoff thinking. Use when evaluating code quality, setting quality goals, choosing abstraction levels, making design tradeoffs, or auditing code against quality pillars. Covers readability, modularity, testability, reusability, and the principle of least astonishment.

Code readability patterns covering comments, function decomposition, nesting, naming conventions, and code structure. Use when evaluating whether code is easy to understand, deciding when and how to write comments, decomposing functions, reducing nesting depth, improving code structure, or reviewing code for readability. Covers comment strategy, function length, abstraction level mixing, nesting reduction, anonymous function readability, and making bad code visually obvious.

Code review process, effective feedback, PR best practices, team working agreements, review automation, AI-augmented review, and review antipatterns. Use when reviewing code, preparing PRs, giving review feedback, setting up review processes, writing review comments, or conducting code reviews during serve phase.

Project-level scope analysis for evaluating whether a project has exceeded its useful boundaries, needs splitting, or is experiencing scope creep. Use when evaluating project structure, considering whether to split a project, assessing feature belonging, or when projects feel too large or unfocused during brainstorm, scope, or architecture review. Covers cohesion test at project level, feature belonging assessment, scope creep detection, split-vs-keep decision framework, and safe splitting patterns.

Unit testing principles, test structure patterns, test doubles strategy, naming conventions, testing antipatterns, and coverage guidance. Use when writing tests, reviewing test quality, evaluating test coverage, choosing test doubles, fixing flaky tests, or assessing test structure during cook and serve phases. Enhances taster agent during TDD cycle.

YAGNI decision frameworks for evaluating whether to build a feature, detecting speculative generality, and preventing unnecessary feature bloat. Use when planning involves new features, design decisions, or "should we build this" questions during brainstorm, scope, or architecture review. Covers four costs of presumptive features, build-vs-not-build decision framework, speculative generality detection, and bloat antipatterns.

Error handling strategy selection, recoverability analysis, and signaling techniques. Use when choosing between exceptions, result types, error codes, or other error handling approaches, designing error-handling APIs, wrapping third-party library errors, or evaluating whether errors should be explicit or implicit.

Refactoring patterns, techniques, and decision frameworks. Use when improving existing code structure, reducing complexity, eliminating code smells, choosing between refactoring approaches, or deciding when refactoring is worthwhile. Covers extraction, encapsulation, type-based refactoring, strategy patterns, modularity improvements, and compiler-guided transformation.

Software tradeoff analysis, decision frameworks, and common tradeoff patterns. Use when evaluating design alternatives, choosing between competing approaches, analyzing costs vs. benefits of technical decisions, or when a decision involves tension between two desirable properties. Covers duplication vs. coupling, flexibility vs. complexity, performance optimization, API design, dependency management, error handling strategies, and distributed system tradeoffs.