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code-optimizer

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Code Optimization

代码优化

Analyze code for performance issues following this priority order:
按照以下优先级顺序分析代码中的性能问题：

Analysis Priorities

分析优先级

  1. Performance bottlenecks - O(n²) operations, inefficient loops, unnecessary iterations
  2. Memory leaks - unreleased resources, circular references, growing collections
  3. Algorithm improvements - better algorithms or data structures for the use case
  4. Caching opportunities - repeated computations, redundant I/O, memoization candidates
  5. Concurrency issues - race conditions, deadlocks, thread safety problems
  1. 性能瓶颈 - O(n²) 操作、低效循环、不必要的迭代
  2. 内存泄漏 - 未释放的资源、循环引用、持续增长的集合
  3. 算法改进 - 更适合当前场景的算法或数据结构
  4. 缓存机会 - 重复计算、冗余I/O、可记忆化的候选点
  5. 并发问题 - 竞态条件、死锁、线程安全问题

Workflow

工作流程

0. Create Feature Branch

0. 创建功能分支

Before making any changes:
  1. Check the current branch - if already on a feature branch for this task, skip
  2. Check the repo for branch naming conventions (e.g., 
    feat/
    , 
    feature/
    , etc.)
  3. Create and switch to a new branch following the repo's convention, or fallback to: 
    feat/optimize-<target>
    • Example: 
      feat/optimize-api-handlers
在进行任何修改之前：
  1. 检查当前分支 - 如果已处于该任务对应的功能分支，可跳过此步骤
  2. 查看仓库的分支命名规范（例如 
    feat/
    、
    feature/
    等）
  3. 按照仓库规范创建并切换到新分支，默认使用：
    feat/optimize-<target>
    • 示例：
      feat/optimize-api-handlers

1. Analysis

1. 分析阶段

  1. Read the target code file(s) or directory
  2. Identify language, framework, and runtime context (Node.js, CPython, browser, etc.)
  3. Analyze for each priority category in order
  4. For each issue found, estimate the performance impact (e.g., "reduces API response from ~500ms to ~50ms")
  5. Report findings sorted by severity (Critical first)
  1. 阅读目标代码文件或目录
  2. 识别语言、框架和运行时环境（Node.js、CPython、浏览器等）
  3. 按优先级顺序逐一分析每个类别
  4. 针对每个发现的问题，评估其性能影响（例如：“将API响应时间从约500ms缩短至约50ms”）
  5. 按严重程度排序报告发现的问题（严重问题优先）

2. Apply Fixes

2. 应用修复

  1. Present the optimization report to the user
  2. On approval, apply fixes starting with Critical/High severity
  3. Run existing tests after each change to verify no regressions
  4. If no tests exist, warn the user before applying changes
  1. 向用户提交优化报告
  2. 获得批准后，从严重/高优先级问题开始应用修复
  3. 每次修改后运行现有测试，验证无回归问题
  4. 如果没有测试用例，在应用修改前向用户发出警告

Response Format

响应格式

For each issue found:
undefined
针对每个发现的问题：
undefined

[Severity] Issue Title

[严重等级] 问题标题

Location: file:line_number Category: Performance | Memory | Algorithm | Caching | Concurrency
Problem: Brief explanation of the issue
Impact: Why this matters (performance cost, resource usage, etc.)
Fix: [Code example showing the optimized version]
undefined
位置: 文件:行号 类别: 性能 | 内存 | 算法 | 缓存 | 并发
问题: 问题简要说明
影响: 该问题的影响（性能损耗、资源占用等）
修复: [展示优化后版本的代码示例]
undefined

Severity Levels

严重等级

  • Critical: Causes crashes, severe memory leaks, or O(n³)+ complexity
  • High: Significant performance impact (O(n²), blocking operations, resource exhaustion)
  • Medium: Noticeable impact under load (redundant operations, suboptimal algorithms)
  • Low: Minor improvements (micro-optimizations, style improvements with perf benefit)
  • 严重：导致崩溃、严重内存泄漏或O(n³)+复杂度
  • 高：显著性能影响（O(n²)、阻塞操作、资源耗尽）
  • 中：高负载下有明显影响（冗余操作、次优算法）
  • 低：小幅改进（微优化、带性能收益的风格改进）

Language-Specific Checks

语言专属检查

JavaScript/TypeScript

JavaScript/TypeScript

  • Array methods inside loops (map/filter/find in forEach)
  • Missing async/await causing blocking
  • Event listener leaks
  • Unbounded arrays/objects
  • 循环内的数组方法（forEach中嵌套map/filter/find）
  • 缺少async/await导致阻塞
  • 事件监听器泄漏
  • 无边界的数组/对象

Python

Python

  • List comprehensions vs generator expressions for large data
  • Global interpreter lock considerations
  • Context manager usage for resources
  • N+1 query patterns
  • 大数据量下的列表推导式与生成器表达式对比
  • 全局解释器锁（GIL）考量
  • 资源的上下文管理器使用
  • N+1查询模式

General

通用检查

  • Premature optimization warnings (only flag if genuinely impactful)
  • Database query patterns (N+1, missing indexes)
  • I/O in hot paths
  • 过早优化警告（仅标记真正有影响的问题）
  • 数据库查询模式（N+1、缺失索引）
  • 热点路径中的I/O操作

Error Handling

错误处理

No obvious performance issues found

未发现明显性能问题

Solution: Report that the code is already well-optimized. Suggest profiling with runtime tools (e.g., 
perf
, Chrome DevTools, 
py-spy
) to find runtime-specific bottlenecks.
解决方案: 报告代码已优化良好。建议使用运行时工具（如 
perf
、Chrome DevTools、
py-spy
）进行性能分析，以发现运行时特定的瓶颈。

Target file is too large (>2000 lines)

目标文件过大（超过2000行）

Solution: Ask the user to specify which functions or sections to focus on. Analyze the most performance-critical paths first.
解决方案: 请求用户指定需要重点关注的函数或代码段。优先分析对性能最关键的路径。

Optimization breaks existing tests

优化导致现有测试失败

Solution: Revert the change immediately. Re-examine the optimization and adjust the approach to preserve existing behavior.
解决方案: 立即回滚修改。重新审视优化方案，调整方法以保留原有功能。