macos-resource-optimizer

macOS system resource optimization with 40 specialized agents for memory, disk, CPU, and process management

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Repositório: rdmptv/AdbAutoPlayer
Última atividade na origem: 3 de dezembro de 2025 às 16:22
Idioma detectado do SKILL.md: inglês
Estrelas: 0
Forks: 1

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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	"2025-11-30T00:00:00.000Z"
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):

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)

# 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


uv run scripts/agent_docker_deep_cleanup.py --dry-run

# 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

# 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

# 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

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)

#!/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

# 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

# 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
    }
}

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

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

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