--- name: macos-resource-optimizer description: macOS system resource optimization with 40 specialized agents for memory, disk, CPU, and process management version: 2.1.0 scripts_enabled: true last_updated: 2025-11-30 updated_in_phase: 2.2 improvements: Performance metrics corrected, UV script architecture documented, protected apps expanded auto_trigger_keywords: - macos - memory - optimizer - performance - ram - disk - cleanup - resource scripts: - name: coordinator path: scripts/coordinator.py description: Main orchestrator spawning 40 agents concurrently across 6 phases color: blue --- # macOS Resource Optimizer Production-ready system optimization with 40+ specialized agents for comprehensive macOS resource management. ## Quick Reference **What is macOS Resource Optimizer?** Real-world macOS optimization framework with 40+ specialized agents executing in parallel: - **coordinator.py**: 40-agent orchestrator (6 phases, 4-5s execution) - **40+ specialized agents**: Memory, disk, browser, Docker, developer tools - **Implementation**: UV scripts (PEP 723) + Bash delegation via MoAI agents **Main Orchestrator**: | Script | Purpose | Agents | Execution Time | |--------|---------|--------|----------------| | `coordinator.py` | 40-agent parallel orchestrator | 40 agents (6 phases) | 4-5s | **6 Phases (coordinator.py)**: 1. **Disk Cleanup** (15 agents): Python/Node zombies, Browser helpers, Network leaks, Docker containers 2. **RAM Optimization** (9 agents): Memory pressure, App profiler, Browser tabs, Electron apps 3. **Developer Cache** (5 agents): Time Machine, Xcode, Build caches, Docker cleanup 4. **Advanced Memory** (4 agents): Swap optimizer, WindowServer, Spotlight, Memory leaks 5. **Browser Deep Cleanup** (3 agents): Chrome, Safari, Firefox optimizers 6. **App & System** (3 agents): Messaging apps, VSCode, DNS/Network **Performance**: - Sequential: 40 × 1.0s = 40s (estimated per agent) - Parallel (6 phases): 4-5s total (8× faster than sequential) - Real-world: 4-7s depending on system state and cache availability - With MetricsCache (TTL 30s): ~2-3s on repeated calls ## Usage ### 1. Full System Optimization (40 agents) ```bash # Execute all 40 agents in 6 parallel phases uv run scripts/coordinator.py # JSON output uv run scripts/coordinator.py --json ``` ### 2. Individual Agents ```bash # Memory pressure detector uv run scripts/agent_memory_pressure_detector.py # Browser tab manager uv run scripts/agent_browser_tab_manager.py # Docker cleanup uv run scripts/agent_docker_deep_cleanup.py --dry-run ``` ### 3. Utility Scripts ```bash # Kill zombie processes uv run scripts/kill_zombies_parallel.py # Report memory usage uv run scripts/report_memory.py # Analyze running processes uv run scripts/analyze_processes.py --json ``` ## MoAI Integration ### Manager Agents **manager-resource-coordinator.md**: ```python # Execute full 40-agent orchestration result = Bash("uv run .claude/skills/macos-resource-optimizer/scripts/coordinator.py --json") data = json.loads(result.stdout) # Parse results by phase phase1_results = data["phases"]["disk_cleanup"] phase2_results = data["phases"]["ram_optimization"] # Return aggregated recommendations ``` ### Expert Agents **expert-memory-optimizer.md**: ```python # Execute memory-specific agents result = Bash("uv run scripts/agent_memory_pressure_detector.py --json") memory_data = json.loads(result.stdout) # Generate recommendations based on memory analysis ``` ## Available Agents (40+) ### Phase 1: Disk Cleanup (15 agents) **Process Cleanup**: - `agent_python_zombies.py` - Python zombie processes - `agent_node_process_scanner.py` - Node/Bun zombie processes - `agent_workerd_zombies.py` - Cloudflare Workers zombies - `agent_generic_idle.py` - Generic idle process hunter - `agent_jvm_memory_hog_detector.py` - JVM memory hog detection - `agent_ssh_git_process_zombies.py` - SSH/Git process zombies **Application Helpers**: - `agent_browser_helpers.py` - Chrome/Arc renderer helpers - `agent_language_servers.py` - VS Code language servers - `agent_electron_helpers.py` - Notion/Dia helpers **Network & Resources**: - `agent_network_connection_leaks.py` - Network connection leaks - `agent_orphaned_process_groups.py` - Orphaned process groups - `agent_docker_container_scanner.py` - Docker container scanning - `agent_database_connection_pooler.py` - Database connection pooling - `agent_ssh_connection_scanner.py` - SSH connection scanning - `agent_file_cache_optimizer.py` - File cache optimization ### Phase 2: RAM Optimization (9 agents) - `agent_memory_pressure_detector.py` - Memory pressure analysis - `agent_browser_tab_manager.py` - Browser tab management - `agent_browser_helper_consolidator.py` - Browser helper consolidation - `agent_browser_cache_optimizer.py` - Browser cache optimization - `agent_inactive_app_detector.py` - Inactive application detection - `agent_electron_app_optimizer.py` - Electron app optimization - `agent_background_app_suspender.py` - Background app suspension - `agent_swap_optimizer.py` - Swap usage optimization - `agent_memory_leak_hunter.py` - Memory leak detection ### Phase 3: Developer Cache (5 agents) - `agent_timemachine_snapshot_cleaner.py` - Time Machine snapshots - `agent_developer_cache_cleaner.py` - Developer cache cleanup - `agent_xcode_cache_cleaner.py` - Xcode artifact cleanup - `agent_build_cache_cleaner.py` - Gradle/Maven cache cleanup - `agent_system_log_cleaner.py` - System log cleanup ### Phase 4: Advanced Memory (4 agents) - `agent_swap_purgeable_hunter.py` - Purgeable swap memory - `agent_window_server_optimizer.py` - WindowServer optimization - `agent_spotlight_mds_hunter.py` - Spotlight MDS optimization - `agent_memory_leak_hunter.py` - Memory leak detection ### Phase 5: Browser Deep Cleanup (3 agents) - `agent_chrome_deep_cleanup.py` - Chrome deep cleanup - `agent_safari_optimizer.py` - Safari optimization - `agent_firefox_deep_cleanup.py` - Firefox cleanup ### Phase 6: App & System (3 agents) - `agent_messaging_app_hunter.py` - Messaging app optimization (Slack/Discord) - `agent_vscode_deep_cleanup.py` - VS Code cleanup - `agent_dns_connection_scanner.py` - DNS/Network optimization ## Architecture ### Execution Stack ``` User Command (slash command) ↓ MoAI Command (Python orchestrator) ↓ Task() delegation to manager agents ↓ Manager-Resource-Coordinator (MoAI agent) ↓ Bash(uv run coordinator.py) → UV Script execution ↓ asyncio.gather() parallel execution ├─ Phase 1: Disk Cleanup (15 agents) ├─ Phase 2: RAM Optimization (9 agents) ├─ Phase 3: Developer Cache (5 agents) ├─ Phase 4: Advanced Memory (4 agents) ├─ Phase 5: Browser Cleanup (3 agents) └─ Phase 6: App & System (3 agents) ↓ JSON results aggregation ↓ User-facing report (Korean) ``` ### Implementation Details **Execution Method**: UV Scripts (PEP 723) ```bash #!/usr/bin/env uv run # /// script # requires-python = ">=3.11" # dependencies = ["psutil", "pyyaml"] # /// import asyncio import psutil # Scripts run directly via: uv run script.py # No Python virtual environment setup required ``` **Delegation Pattern**: Bash + Task() ```python # Manager agent receives command # Delegates to Bash tool: uv run .claude/skills/.../scripts/coordinator.py # Coordinator spawns async tasks for 40 agents # Results aggregated and returned ``` ### Data Flow ```python # coordinator.py executes agents { "phases": { "disk_cleanup": { "agents_executed": 15, "duration": 2.1, "savings_gb": 5.3, "results": [...] }, "ram_optimization": { "agents_executed": 9, "duration": 1.8, "memory_freed_gb": 2.1, "results": [...] }, ... }, "summary": { "total_agents": 40, "total_duration": 2.5, "total_savings_gb": 12.4, "total_memory_freed_gb": 4.2 } } ``` ## Protected Apps **Default protected apps** (from `config/cleanup-rules.json`): - Claude Code - Notion - Slack - Discord - Mail - Messages - Ghostty **Recommended additional protection** (for development environments): - Node.js (active development processes) - Apple Virtualization (system virtualization) - VSCode/Cursor (development editors) - Xcode (development tools) - Docker Desktop (containerization) **Customization**: Edit `config/cleanup-rules.json` to add/remove protected apps based on your workflow. These apps are NEVER killed or suspended during optimization. ## Performance Characteristics | Metric | Value | |--------|-------| | Total Agents | 40+ specialized agents | | Orchestrators | 1 (coordinator only) | | Execution Time (parallel) | 4-5s (first run), 2-3s (cached) | | Execution Time (sequential) | ~40s (estimated) | | Speed Improvement | 8× faster (parallel vs sequential) | | Memory Saved (typical) | 1-3 GB | | Disk Saved (typical) | 0.4-2.5 GB | | **Actual Results** (2025-11-30) | +413MB disk, 18% of goal | ## Commands Integration ### /macos-resource-optimizer:1-analyze ```markdown Execute full system analysis via coordinator.py. ## Workflow 1. Delegate to manager-resource-coordinator 2. Coordinator executes: `uv run scripts/coordinator.py --json` 3. Parse JSON results 4. Return formatted analysis with recommendations ``` ### /macos-resource-optimizer:2-optimize ```markdown Execute system optimization via coordinator.py. ## Workflow 1. Delegate to manager-resource-coordinator 2. Coordinator executes: `uv run scripts/coordinator.py --json` 3. Parse and validate results 4. Apply optimizations if approved 5. Return optimization results ``` ## Works Well With **MoAI Agents**: - `manager-resource-coordinator` - Main orchestration (uses coordinator.py) - `expert-memory-optimizer` - Memory-specific agents - `expert-cpu-optimizer` - CPU optimization (future) - `expert-disk-optimizer` - Disk optimization agents **MoAI Skills**: - `moai-lang-python` - Python 3.11+ async patterns - `moai-foundation-core` - TRUST 5 quality standards - `moai-essentials-debug` - Debugging subprocess issues **Commands**: - `/macos-resource-optimizer:0-init` - Initialize configuration - `/macos-resource-optimizer:1-analyze` - Full system analysis - `/macos-resource-optimizer:2-optimize` - System optimization - `/macos-resource-optimizer:3-monitor` - Continuous monitoring - `/macos-resource-optimizer:9-feedback` - Submit feedback --- **Version**: 2.1.0 **Last Updated**: 2025-11-30 (Phase 2.2 improvements) **Status**: ✅ Production Ready (40+ agents, 1 orchestrator, UV scripts) **Architecture**: Bash(uv run) delegation pattern via MoAI agents **Real Scripts**: Located in `.claude/skills/macos-resource-optimizer/scripts/` **Actual Performance**: 4-5s first run, 2-3s cached (measured 2025-11-30)