code-optimizer

Code Optimization Analyze code for performance issues following this priority order: Analysis Priorities Performance bottlenecks - O(n²) operations, inefficient loops, unnecessary iterations Memory leaks - unreleased resources, circular references, growing collections Algorithm improvements - better algorithms or data structures for the use case Caching opportunities - repeated computations, redundant I/O, memoization candidates Concurrency issues - race conditions, deadlocks, thread safety problems Repo Sync Before Edits (mandatory) Before creating/updating/deleting files in an existing repository, sync the current branch with remote: branch = " $( git rev-parse --abbrev-ref HEAD ) " git fetch origin git pull --rebase origin " $branch " If the working tree is not clean, stash first, sync, then restore: git stash push -u -m "pre-sync" branch = " $( git rev-parse --abbrev-ref HEAD ) " git fetch origin && git pull --rebase origin " $branch " git stash pop If origin is missing, pull is unavailable, or rebase/stash conflicts occur, stop and ask the user before continuing. Workflow 0. Create Feature Branch Before making any changes: Check the current branch - if already on a feature branch for this task, skip Check the repo for branch naming conventions (e.g., feat/ , feature/ , etc.) Create and switch to a new branch following the repo's convention, or fallback to: feat/optimize- Example: feat/optimize-api-handlers 1. Analysis Read the target code file(s) or directory Identify language, framework, and runtime context (Node.js, CPython, browser, etc.) Analyze for each priority category in order For each issue found, estimate the performance impact (e.g., "reduces API response from ~500ms to ~50ms") Report findings sorted by severity (Critical first) 2. Apply Fixes Present the optimization report to the user On approval, apply fixes starting with Critical/High severity Run existing tests after each change to verify no regressions If no tests exist, warn the user before applying changes Response Format For each issue found:

[Severity] Issue Title

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]

Severity Levels

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) Language-Specific Checks JavaScript/TypeScript Array methods inside loops (map/filter/find in forEach) Missing async/await causing blocking Event listener leaks Unbounded arrays/objects Python List comprehensions vs generator expressions for large data Global interpreter lock considerations Context manager usage for resources N+1 query patterns General Premature optimization warnings (only flag if genuinely impactful) Database query patterns (N+1, missing indexes) I/O in hot paths Error Handling No obvious performance issues found 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. Target file is too large (>2000 lines) Solution: Ask the user to specify which functions or sections to focus on. Analyze the most performance-critical paths first. Optimization breaks existing tests Solution: Revert the change immediately. Re-examine the optimization and adjust the approach to preserve existing behavior.