code-optimizer

name

code-optimizer

description

Deep code optimization audit using parallel specialist agents that hunt performance anti-patterns via pattern-based detection, avoiding anchoring bias. Covers DB queries, memory leaks, algorithmic complexity, concurrency, bundle size, dead code, I/O/network, rendering, caching, and build config. Use when asked to optimize code, find performance issues or bottlenecks, speed up an app, reduce latency, detect code smells, or run a performance/quality audit.

Code Optimizer

Parallel multi-agent code optimization audit. Spawn 13 specialist agents simultaneously, each hunting for a different class of performance problem using pattern-based detection.

Critical Principle: No Code Reading Before Analysis

Agents MUST NOT read source files before searching for patterns. Reading the code first causes anchoring bias — the agent accepts the existing implementation as "reasonable" and misses better alternatives. Instead, each agent:

1. Read its assigned reference file from references/ to load detection patterns
2. Use Grep/Glob to scan the codebase for anti-patterns
3. For each finding, ONLY THEN read the surrounding context (5-10 lines) to confirm the issue
4. Propose the optimal solution based on best practices, NOT based on the existing code

Workflow

Step 1: Detect Stack

Use Glob to identify the project's tech stack:

• **/package.json → Node.js/JS/TS (check for React, Next.js, Express, etc.)
• **/requirements.txt, **/pyproject.toml, **/setup.py → Python
• **/go.mod → Go
• **/Cargo.toml → Rust
• **/pom.xml, **/build.gradle → Java
• **/Gemfile → Ruby
• **/Dockerfile → Docker
• **/*.sql → SQL
• **/webpack.config.*, **/vite.config.*, **/tsconfig.json → Build tools

Step 2: Spawn 13 Parallel Agents

Launch ALL agents simultaneously using the Agent tool. Each agent receives:

• Its domain name and reference file path
• The detected tech stack (so it can focus on relevant patterns)
• The project root path
• Instructions to NOT read code files, only Grep/Glob for patterns

Agent definitions (spawn all 13 in a single message):

#	Agent Name	Reference File	Focus
1	Database & Queries	references/database-queries.md	N+1 queries, SELECT *, missing indexes, ORM misuse, connection pooling
2	Memory & Resources	references/memory-resources.md	Memory leaks, unclosed resources, large allocations, string concat in loops
3	Algorithmic Complexity	references/algorithmic-complexity.md	O(n^2) patterns, unnecessary iterations, wrong data structures for lookups
4	Concurrency & Async	references/concurrency-async.md	Sequential awaits, blocking in async, race conditions, unbounded concurrency
5	Bundle & Dependencies	references/bundle-dependencies.md	Heavy imports, unused deps, duplicate libs, missing lazy loading
6	Dead Code & Redundancy	references/dead-code-redundancy.md	Unused exports, commented code, dead branches, duplicate logic
7	I/O & Network	references/io-network.md	Sequential requests, missing batching, no dedup, missing compression
8	Rendering & UI	references/rendering-ui.md	Re-renders, missing virtualization, layout thrashing, animation perf
9	Data Structures	references/data-structures.md	Wrong structures, unnecessary copies, inefficient serialization
10	Error & Resilience	references/error-resilience.md	Missing timeouts, swallowed errors, no retries, no circuit breakers
11	Caching & Memoization	references/caching-memoization.md	Missing memoization, cache without invalidation, redundant API calls
12	Build & Compilation	references/build-compilation.md	Dev code in prod, missing optimization flags, slow tests, Docker issues
13	Security-Performance

Optional agents (spawn if relevant to detected stack):

• Logging & Observability (references/logging-observability.md) — if logging framework detected
• Config & Infrastructure (references/config-infra.md) — if Docker/deployment config detected

Agent Prompt Template

Each agent MUST receive this prompt structure:

You are a {DOMAIN_NAME} optimization specialist. Your job is to find performance
anti-patterns in the codebase at {PROJECT_ROOT}.

CRITICAL RULES:
1. DO NOT read source code files before searching. This avoids anchoring bias.
2. First, read your reference file: {SKILL_DIR}/references/{REFERENCE_FILE}
3. Use Grep and Glob to search for the patterns described in the reference file.
4. Only read 5-10 lines of context around each finding to confirm it's a real issue.
5. Skip patterns that don't match the project's stack: {DETECTED_STACK}

Tech stack detected: {DETECTED_STACK}
Project root: {PROJECT_ROOT}

For each finding, report:
- **File**: path:line_number
- **Pattern**: what anti-pattern was detected
- **Severity**: CRITICAL / HIGH / MEDIUM / LOW
- **Current code**: the problematic snippet (keep short)
- **Why it's slow**: brief explanation of the performance impact
- **Optimal fix**: the recommended solution (code snippet or approach)
- **Estimated impact**: qualitative improvement expected (e.g., "10x faster for large lists")

If you find 0 issues in your domain, report "No issues found" — this is a valid outcome.
Sort findings by severity (CRITICAL first).

Step 3: Consolidate Report

After all agents complete, consolidate their findings into a single prioritized report:

1. Collect all findings from all agents
2. Deduplicate (different agents may flag the same code for different reasons)
3. Sort by severity: CRITICAL > HIGH > MEDIUM > LOW
4. Group by file (so the user can fix file-by-file)
5. Present the final report with:
   • Executive summary: total findings by severity, top 3 most impactful
   • Detailed findings table grouped by file
   • Improvement plan: ordered list of fixes from highest to lowest impact

Report Format

# Code Optimization Audit Report

## Executive Summary
- **X** critical issues, **Y** high, **Z** medium, **W** low
- Top 3 highest-impact fixes:
  1. [brief description] — [estimated impact]
  2. [brief description] — [estimated impact]
  3. [brief description] — [estimated impact]

## Findings by File

### `path/to/file.ts`

| # | Severity | Domain | Pattern | Fix | Impact |
|---|----------|--------|---------|-----|--------|
| 1 | CRITICAL | Database | N+1 query in loop | Use prefetch_related | 50x fewer queries |
| 2 | HIGH | Async | Sequential awaits | Use Promise.all | 3x faster |

[... for each file with findings ...]

## Improvement Plan

Priority-ordered steps to implement the fixes:

1. **[CRITICAL] Fix N+1 queries in `api/users.py`**
   - Current: loop queries user.posts for each user
   - Fix: add prefetch_related('posts') to queryset
   - Impact: reduces N+1 to 2 queries

2. **[HIGH] Parallelize API calls in `services/sync.ts`**
   - Current: 5 sequential await fetch() calls
   - Fix: Promise.all([fetch1, fetch2, ...])
   - Impact: ~5x faster sync operation

[... continue for all findings ...]