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):

Disk Cleanup (15 agents): Python/Node zombies, Browser helpers, Network leaks, Docker containers
RAM Optimization (9 agents): Memory pressure, App profiler, Browser tabs, Electron apps
Developer Cache (5 agents): Time Machine, Xcode, Build caches, Docker cleanup
Advanced Memory (4 agents): Swap optimizer, WindowServer, Spotlight, Memory leaks
Browser Deep Cleanup (3 agents): Chrome, Safari, Firefox optimizers
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