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Found 5 Skills
Identify refactoring opportunities by surfacing architectural friction. Apply the deletion test, deep-modules vocabulary, and seams analysis. Each opportunity becomes its own evanflow-writing-plans cycle. Use when reviewing code for refactoring, when a file has grown too large, or when architecture concerns surface during feature work.
Diagnoses what makes code complex and why, using the three-symptom two-root-cause framework. Use when code feels harder to work with than it should but the specific problem is unclear. This skill identifies WHETHER complexity exists and WHERE it comes from. Not for scanning a checklist of known design smells (use red-flags) or evaluating a specific module's depth (use deep-modules).
Manage software complexity through deep modules, information hiding, and strategic programming. Use when the user mentions "module design", "API too complex", "shallow class", "complexity budget", or "strategic vs tactical". Covers deep vs shallow modules, red flags for complexity, and comments as design documentation. For code quality, see clean-code. For boundaries, see clean-architecture.
Designs and refactors software codebases to be AI-friendly by aligning the filesystem with domain/feature boundaries, creating deep (greybox) modules with small public interfaces, enforcing import boundaries, and tightening tests/feedback loops. Use when the user asks to "make the codebase AI-ready", "reduce coupling", "introduce deep modules", "create module boundaries", "restructure folders by feature", "define service interfaces", or "plan a refactor + tests so AI agents can work safely".
Python design patterns for CLI scripts and utilities — type-first development, deep modules, complexity management, and red flags. Use when reading, writing, reviewing, or refactoring Python files, especially in .trellis/scripts/ or any CLI/scripting context. Also activate when planning module structure, deciding where to put new code, or doing code review.