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-<target>
- 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.

code-optimizer