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refactoringrefactoring 工具

Agent Skill

refactoring 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

927

周安装

39

GitHub Stars

4

下载量

324
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:refactoring(refactoring 工具)
来源仓库:https://github.com/eyadsibai/ltk
仓库路径:skills/refactoring
安装命令:
npx skills add https://github.com/eyadsibai/ltk --skill Refactoring
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/eyadsibai/ltk --skill Refactoring

简介

用于安全重构代码结构,包括重命名、迁移与现代化工序。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中优化遗留系统。
  • 提供搜索模式、依赖检查与回滚策略。
  • 安装命令:npx skills add https://github.com/eyadsibai/ltk --skill Refactoring。
  • 每次仅实施一项变更并充分测试。

SKILL.md

Refactoring

Comprehensive refactoring skill for safe code restructuring, migrations, and modernization.

Core Capabilities

Safe Renaming

Rename identifiers across the codebase:

Rename workflow:

  1. Find all references: Search for all usages
  2. Identify scope: Module-local, package-wide, or public API
  3. Check dependencies: External code that might break
  4. Perform rename: Update all occurrences
  5. Verify: Run tests, check imports

Search patterns:

# Find all references to a function
grep -rn "function_name" --include="*.py"

# Find class usages
grep -rn "ClassName" --include="*.py"

# Find imports
grep -rn "from .* import.*function_name" --include="*.py"

Renaming considerations:

  • Update docstrings mentioning the old name
  • Update comments referencing the name
  • Update configuration files
  • Update tests
  • Consider deprecation period for public APIs

Method Extraction

Extract code into separate functions:

When to extract:

  • Code block is too long (> 20 lines)
  • Code is duplicated elsewhere
  • Code has a clear single purpose
  • Code can be tested independently

Extraction process:

  1. Identify the code block to extract
  2. Determine inputs (parameters)
  3. Determine outputs (return values)
  4. Create new function with clear name
  5. Replace original code with function call
  6. Add tests for new function

Before:

def process_order(order):
    # Validate order (candidate for extraction)
    if not order.items:
        raise ValueError("Empty order")
    if order.total < 0:
        raise ValueError("Invalid total")
    for item in order.items:
        if item.quantity <= 0:
            raise ValueError("Invalid quantity")

    # Process payment
    payment_result = gateway.charge(order.total)
    return payment_result

After:

def validate_order(order: Order) -> None:
    """Validate order has valid items and total."""
    if not order.items:
        raise ValueError("Empty order")
    if order.total < 0:
        raise ValueError("Invalid total")
    for item in order.items:
        if item.quantity <= 0:
            raise ValueError("Invalid quantity")

def process_order(order: Order) -> PaymentResult:
    validate_order(order)
    return gateway.charge(order.total)

Class Splitting

Split large classes into focused components:

When to split:

  • Class has multiple responsibilities
  • Class has > 500 lines
  • Groups of methods work on different data
  • Testing requires excessive mocking

Splitting strategies:

Extract class:

# Before: God class
class OrderManager:
    def create_order(self): ...
    def validate_order(self): ...
    def calculate_tax(self): ...
    def calculate_shipping(self): ...
    def send_confirmation_email(self): ...
    def send_shipping_notification(self): ...

# After: Focused classes
class OrderService:
    def create_order(self): ...
    def validate_order(self): ...

class PricingService:
    def calculate_tax(self): ...
    def calculate_shipping(self): ...

class NotificationService:
    def send_confirmation_email(self): ...
    def send_shipping_notification(self): ...

Complexity Reduction

Reduce cyclomatic complexity:

Replace conditionals with polymorphism:

# Before: Complex switch
def calculate_price(product_type, base_price):
    if product_type == "physical":
        return base_price + shipping_cost
    elif product_type == "digital":
        return base_price
    elif product_type == "subscription":
        return base_price * 12 * 0.9
    else:
        raise ValueError("Unknown type")

# After: Polymorphism
class Product(ABC):
    @abstractmethod
    def calculate_price(self, base_price): ...

class PhysicalProduct(Product):
    def calculate_price(self, base_price):
        return base_price + self.shipping_cost

class DigitalProduct(Product):
    def calculate_price(self, base_price):
        return base_price

class Subscription(Product):
    def calculate_price(self, base_price):
        return base_price * 12 * 0.9

Extract guard clauses:

# Before: Nested conditionals
def process(data):
    if data:
        if data.is_valid:
            if data.is_ready:
                return do_processing(data)
    return None

# After: Guard clauses
def process(data):
    if not data:
        return None
    if not data.is_valid:
        return None
    if not data.is_ready:
        return None
    return do_processing(data)

Code Migration

Migrate code between patterns or versions:

Migration types:

  • Python 2 to 3
  • Sync to async
  • ORM migrations
  • API version upgrades
  • Framework migrations

Migration workflow:

  1. Assess scope: What needs to change
  2. Create compatibility layer: If gradual migration
  3. Migrate in phases: Start with low-risk areas
  4. Maintain tests: Ensure behavior preserved
  5. Remove old code: Clean up after migration

Example: Sync to Async

# Before: Synchronous
def fetch_user(user_id: int) -> User:
    response = requests.get(f"/users/{user_id}")
    return User.from_dict(response.json())

# After: Asynchronous
async def fetch_user(user_id: int) -> User:
    async with aiohttp.ClientSession() as session:
        async with session.get(f"/users/{user_id}") as response:
            data = await response.json()
            return User.from_dict(data)

Refactoring Workflow

Safe Refactoring Process

  1. Ensure test coverage: Add tests if missing
  2. Run tests: Verify green baseline
  3. Make small change: One refactoring at a time
  4. Run tests again: Verify still green
  5. Commit: Save progress
  6. Repeat: Continue with next change

Pre-Refactoring Checklist

[ ] Tests exist for affected code
[ ] All tests pass
[ ] Change is well understood
[ ] Impact scope identified
[ ] Rollback plan exists

Post-Refactoring Verification

[ ] All tests still pass
[ ] No new linting errors
[ ] Type checking passes
[ ] Functionality unchanged
[ ] Performance acceptable

Common Refactorings

Remove Dead Code

# Find and remove:
# - Unused imports
# - Unused variables
# - Unreachable code
# - Commented-out code
# - Deprecated functions

# Tools:
# - vulture (Python)
# - autoflake (Python)
# - eslint (JavaScript)

Simplify Expressions

# Before
if condition == True:
    return True
else:
    return False

# After
return condition

Introduce Explaining Variables

# Before
if user.age >= 18 and user.country in ['US', 'CA'] and user.verified:
    allow_purchase()

# After
is_adult = user.age >= 18
is_supported_country = user.country in ['US', 'CA']
is_verified = user.verified
can_purchase = is_adult and is_supported_country and is_verified

if can_purchase:
    allow_purchase()

Safety Guidelines

Breaking Changes

When refactoring public APIs:

  1. Add deprecation warnings first
  2. Maintain backwards compatibility
  3. Document migration path
  4. Remove after deprecation period

Database Migrations

When refactoring data models:

  1. Create migration scripts
  2. Test migrations on copy of data
  3. Plan for rollback
  4. Consider zero-downtime migrations

Performance Impact

After refactoring:

  1. Run performance benchmarks
  2. Compare with baseline
  3. Profile if regression found
  4. Optimize if necessary

Integration

Coordinate with other skills:

  • code-quality skill: Measure improvement
  • test-coverage skill: Ensure test coverage
  • architecture-review skill: Validate structural changes
  • git-workflows skill: Proper commit messages for refactors

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

40.06%
按下载量换算130

Claude

28.51%
按下载量换算92

Cursor

17.75%
按下载量换算58

Gemini CLI

9.29%
按下载量换算30

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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