code-optimizer

# 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 | `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 ```markdown # 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 ...] ```