code-optimizer

name	code-optimizer
description	Deep code optimization audit using parallel specialist agents. Each agent hunts for performance anti-patterns, inefficiencies, and suboptimal code using pattern-based detection (Grep/Glob) WITHOUT reading the full source code first — avoiding anchoring bias on existing implementations. Covers ALL optimization domains: database queries, memory leaks, algorithmic complexity, concurrency, bundle size, dead code, I/O & network, rendering/UI, data structures, error handling, caching, build config, security-performance, logging, and infrastructure. Use when asked to: "optimize my code", "find performance issues", "audit code quality", "speed up my app", "find bottlenecks", "code review for performance", "find anti-patterns", "improve code efficiency", "reduce latency", "optimize performance", "code smell detection", "find slow code", "optimize this project", "performance audit", "code optimization". Also triggers on: "optimizar codigo", "encontrar cuellos de botella", "mejorar rendimiento".
tags	["gsd-2","skills","code-optimizer","performance","devops","security","database","code-review","compliance"]

--------|----------------|-------| | 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 | references/security-performance.md | Crypto misuse, missing rate limiting, ReDoS, SQL injection vectors |

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 ...]