refactor

Code refactoring best practices based on Martin Fowler's catalog and Clean Code principles (formerly refactoring). This skill should be used when refactoring existing code, improving code structure, reducing complexity, eliminating code smells, or reviewing code for maintainability. Triggers on tasks involving extract method, rename, decompose conditional, reduce coupling, or improve readability.

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저장소: pproenca/dot-skills
최근 소스 활동: 2026년 5월 14일 18:16
감지된 SKILL.md 언어: 영어
스타: 215
포크: 19

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name: refactor
description: Code refactoring best practices based on Martin Fowler's catalog and Clean Code principles (formerly refactoring). This skill should be used when refactoring existing code, improving code structure, reducing complexity, eliminating code smells, or reviewing code for maintainability. Triggers on tasks involving extract method, rename, decompose conditional, reduce coupling, or improve readability.

# Fowler/Martin Code Refactoring Best Practices Comprehensive code refactoring guide based on Martin Fowler's catalog and Clean Code principles, designed for AI agents and LLMs. Contains 43 rules across 8 categories, prioritized by impact to guide automated refactoring and code generation. ## When to Apply Reference these guidelines when: - Refactoring existing code to improve maintainability - Decomposing long methods or large classes - Reducing coupling between components - Simplifying complex conditional logic - Reviewing code for code smells and anti-patterns ## Rule Categories by Priority | Priority | Category | Impact | Prefix | |----------|----------|--------|--------| | 1 | Structure & Decomposition | CRITICAL | `struct-` | | 2 | Coupling & Dependencies | CRITICAL | `couple-` | | 3 | Naming & Clarity | HIGH | `name-` | | 4 | Conditional Logic | HIGH | `cond-` | | 5 | Abstraction & Patterns | MEDIUM-HIGH | `pattern-` | | 6 | Data Organization | MEDIUM | `data-` | | 7 | Error Handling | MEDIUM | `error-` | | 8 | Micro-Refactoring | LOW | `micro-` | ## Quick Reference ### 1. Structure & Decomposition (CRITICAL) - `struct-extract-method` - Extract Method for Long Functions - `struct-single-responsibility` - Apply Single Responsibility Principle - `struct-extract-class` - Extract Class from Large Class - `struct-compose-method` - Compose Method for Readable Flow - `struct-function-length` - Keep Functions Under 20 Lines - `struct-replace-method-with-object` - Replace Method with Method Object - `struct-parameter-object` - Introduce Parameter Object ### 2. Coupling & Dependencies (CRITICAL) - `couple-dependency-injection` - Use Dependency Injection - `couple-hide-delegate` - Hide Delegate to Reduce Coupling - `couple-remove-middle-man` - Remove Middle Man When Excessive - `couple-feature-envy` - Fix Feature Envy by Moving Methods - `couple-interface-segregation` - Apply Interface Segregation Principle - `couple-preserve-whole-object` - Preserve Whole Object Instead of Fields ### 3. Naming & Clarity (HIGH) - `name-intention-revealing` - Use Intention-Revealing Names - `name-avoid-abbreviations` - Avoid Abbreviations and Acronyms - `name-consistent-vocabulary` - Use Consistent Vocabulary - `name-searchable-names` - Use Searchable Names - `name-avoid-encodings` - Avoid Type Encodings in Names ### 4. Conditional Logic (HIGH) - `cond-guard-clauses` - Replace Nested Conditionals with Guard Clauses - `cond-polymorphism` - Replace Conditional with Polymorphism - `cond-decompose` - Decompose Complex Conditionals - `cond-consolidate` - Consolidate Duplicate Conditional Fragments - `cond-special-case` - Introduce Special Case Object - `cond-lookup-table` - Replace Conditional with Lookup Table ### 5. Abstraction & Patterns (MEDIUM-HIGH) - `pattern-strategy` - Extract Strategy for Algorithm Variants - `pattern-template-method` - Use Template Method for Shared Skeleton - `pattern-factory` - Use Factory for Complex Object Creation - `pattern-open-closed` - Apply Open-Closed Principle - `pattern-composition-over-inheritance` - Prefer Composition Over Inheritance - `pattern-extract-superclass` - Extract Superclass for Common Behavior ### 6. Data Organization (MEDIUM) - `data-encapsulate-collection` - Encapsulate Collection - `data-replace-primitive` - Replace Primitive with Object - `data-encapsulate-record` - Encapsulate Record into Class - `data-split-variable` - Split Variable with Multiple Assignments - `data-replace-temp-with-query` - Replace Temp with Query ### 7. Error Handling (MEDIUM) - `error-exceptions-over-codes` - Use Exceptions Instead of Error Codes - `error-custom-exceptions` - Create Domain-Specific Exception Types - `error-fail-fast` - Fail Fast with Preconditions - `error-separate-concerns` - Separate Error Handling from Business Logic ### 8. Micro-Refactoring (LOW) - `micro-remove-dead-code` - Remove Dead Code - `micro-inline-variable` - Inline Trivial Variables - `micro-simplify-expressions` - Simplify Boolean Expressions - `micro-rename-for-clarity` - Rename for Clarity ## How to Use Read individual reference files for detailed explanations and code examples: - [Section definitions](references/_sections.md) - Category structure and impact levels - [Rule template](assets/templates/_template.md) - Template for adding new rules - Individual rules: `references/{prefix}-{slug}.md` ## Full Compiled Document For the complete guide with all rules expanded: `AGENTS.md`

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ray-llm

LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch inference with ray.data.llm (build_processor). Corrects the stale defaults a model produces — the archived ray-llm repo and its YAML configs, hand-rolled vLLM engines inside plain Serve deployments, the removed build_llm_processor name, deprecated boolean stage flags, top-level LLMServer/LLMRouter imports, free-form accelerator strings, one-deployment-per-LoRA-adapter designs — with the 2.57 idioms (stage configs, placement_group_config over hand-rolled PGs, deployment_config autoscaling, dynamic LoRA multiplexing, prefix-cache-affinity routing, the full OpenAI endpoint surface). Use when writing, reviewing, or productionizing LLM serving or batch inference on Ray. Classic-ML Ray (Train/Tune/Data/Serve/clusters) lives in the sibling ray skill.

2026-08-15

ray

Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, map_batches concurrency=, DatasetPipeline/to_torch, max_concurrent_queries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning, compute strategies, streaming datasets, DeploymentHandle/DeploymentResponse, serve build/deploy, ray.util.state, KubeRay CRDs and Jobs API). Use when writing, reviewing, or productionizing Python code that touches Ray distributed training, data pipelines, hyperparameter tuning, model serving, or Ray cluster operations. LLM serving/batch-inference on Ray lives in the sibling ray-llm skill.

2026-08-15

mlflow-3

MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a tracking server, evaluating with gates, and serving. Corrects the MLflow 2-era defaults a model reaches for (artifact_path, registry stages and get_latest_versions, top-level mlflow.evaluate with baseline_model, runs-URI registration, pickle serialization, mlruns file stores, MLServer serving) with the MLflow 3 idioms that replaced them (named LoggedModels, aliases and copy_model_version, models.evaluate plus validate_evaluation_results, skops/torch.export defaults, database backends, the FastAPI scoring server). Use when writing, reviewing, or migrating Python code that touches MLflow tracking, the model registry, evaluation, or serving.

2026-08-15

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