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Found 19 Skills
Help users manage technical debt strategically. Use when someone is dealing with legacy code, planning refactoring work, deciding between rewrites vs. incremental fixes, trying to get buy-in for tech debt reduction, or balancing new features with maintenance.
Transform legacy codebases into AI-ready projects with Claude Code configurations. Use when (1) analyzing old projects to generate AI coding configurations, (2) creating CLAUDE.md, skills, subagents, slash commands, hooks, or rules for existing projects, (3) user wants to enable vibe coding for a codebase, (4) onboarding new team members with AI-assisted development, (5) user mentions "make project AI-ready", "generate Claude config", or "create coding standards for AI".
Audit code comments and docstrings quality across 6 categories (WHY-not-WHAT, Density, Forbidden Content, Docstrings, Actuality, Legacy). Use when code needs comment review, after major refactoring, or as part of ln-100-documents-pipeline. Outputs Compliance Score X/10 per category + Findings + Recommended Actions.
Dead code & legacy audit worker (L3). Checks unreachable code, unused imports/variables/functions, commented-out code, backward compatibility shims, deprecated patterns. Returns findings.
Use when working with SceneKit 3D scenes, migrating SceneKit to RealityKit, or maintaining legacy SceneKit code. Covers scene graph, materials, physics, animation, SwiftUI bridge, migration decision tree.
Use when modifying existing files, refactoring, improving code quality, or touching legacy code by applying the Boy Scout Rule to leave code better than you found it.
Test-driven development methodology — red-green-refactor cycle, writing failing tests first, minimal implementation, and iterative refinement. Use when implementing features test-first, when the user asks for TDD, or when writing tests before code.
Systematic code refactoring following Martin Fowler's catalog. Methodologies: characterization tests, Red-Green-Refactor, incremental transformation. Capabilities: SOLID compliance, DRY cleanup, code smell detection, complexity reduction, legacy modernization, design patterns, functional programming patterns. Actions: refactor, extract, inline, rename, move, simplify code. Keywords: refactor, SOLID, DRY, code smell, complexity, extract method, inline, rename, move, clean code, technical debt, legacy code, design pattern, characterization test, Red-Green-Refactor, functional programming, higher-order function, immutability, pure function, composition, currying, side effects. Use when: improving code quality, reducing technical debt, applying SOLID principles, fixing DRY violations, removing code smells, modernizing legacy code, applying design patterns.
Used when you need to perform Discover (reverse engineering) on legacy projects with existing code, consolidate repository facts into `.aisdlc/project/`, and you find that AI or teams frequently guess entry points and boundaries, have duplicate writing of indexes and details, or lack evidence chains leading to repeated rework.
Precise, instant code structure queries for active development — answer 'who depends on this interface before I refactor it', 'how many modules break if I change this', 'what is the real impact radius of this feature change', 'which module is the true high-coupling hotspot in this legacy codebase'. Essential before any interface change, continuous refactoring task, sprint work estimation, or when navigating unfamiliar or large legacy codebases. Requires Python 3.10+ and shell. Use nexus-mapper instead when building a full .nexus-map/ knowledge base.
Generate feature specifications by analyzing existing source code.
Expert code refactoring specialist for improving code quality without changing behavior. Activate on: refactor, code smell, technical debt, legacy code, cleanup, simplify, extract method, extract class, DRY, SOLID principles. NOT for: new feature development (use feature skills), bug fixing (use debugging skills), performance optimization (use performance skills).