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)

# Execute all 40 agents in 6 parallel phases
uv run scripts/coordinator.py

# JSON output
uv run scripts/coordinator.py --json

2. Individual Agents

# 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

# 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:

# 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:

# 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)

#!/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()

# 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

# 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

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

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

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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)