macos-cleaner

name	macos-cleaner
description	Analyze and reclaim macOS disk space through intelligent cleanup recommendations. This skill should be used when users report disk space issues, need to clean up their Mac, or want to understand what's consuming storage. Focus on safe, interactive analysis with user confirmation before any deletions.

macOS Cleaner

Overview

Intelligently analyze macOS disk usage and provide actionable cleanup recommendations to reclaim storage space. This skill follows a safety-first philosophy: analyze thoroughly, present clear findings, and require explicit user confirmation before executing any deletions.

Target users: Users with basic technical knowledge who understand file systems but need guidance on what's safe to delete on macOS.

Core Principles

1. Safety First, Never Bypass: NEVER execute dangerous commands (rm -rf, mo clean, etc.) without explicit user confirmation. No shortcuts, no workarounds.
2. Precision Deletion Only: Delete by specifying exact object IDs/names. Never use batch prune commands.
3. Every Object Listed: Reports must show every specific image, volume, container — not just "12 GB of unused images".
4. Value Over Vanity: Your goal is NOT to maximize cleaned space. Your goal is to identify what is truly useless vs valuable cache. Clearing 50GB of useful cache just to show a big number is harmful.
5. Network Environment Awareness: Many users (especially in China) have slow/unreliable internet. Re-downloading caches can take hours. A cache that saves 30 minutes of download time is worth keeping.
6. Impact Analysis Required: Every cleanup recommendation MUST include "what happens if deleted" column. Never just list items without explaining consequences.
7. Double-Check Before Delete: Verify each Docker object with independent cross-checks before deletion (see Step 2A).
8. Patience Over Speed: Disk scans can take 5-10 minutes. NEVER interrupt or skip slow operations. Report progress to user regularly.
9. User Executes Cleanup: After analysis, provide the cleanup command for the user to run themselves. Do NOT auto-execute cleanup.
10. Conservative Defaults: When in doubt, don't delete. Err on the side of caution.

ABSOLUTE PROHIBITIONS:

• ❌ NEVER use docker image prune, docker volume prune, docker system prune, or ANY prune-family command (exception: docker builder prune is safe — build cache contains only intermediate layers, never user data)
• ❌ NEVER use docker container prune — stopped containers may be restarted at any time
• ❌ NEVER run rm -rf on user directories without explicit confirmation
• ❌ NEVER run mo clean without --dry-run preview first
• ❌ NEVER skip analysis steps to save time
• ❌ NEVER append --help to Mole commands (only mo --help is safe)
• ❌ NEVER present cleanup reports with only categories — every object must be individually listed
• ❌ NEVER recommend deleting useful caches just to inflate cleanup numbers

Workflow Decision Tree

User reports disk space issues
           ↓
    Quick Diagnosis
           ↓
    ┌──────┴──────┐
    │             │
Immediate    Deep Analysis
 Cleanup      (continue below)
    │             │
    └──────┬──────┘
           ↓
  Present Findings
           ↓
   User Confirms
           ↓
   Execute Cleanup
           ↓
  Verify Results

Step 1: Quick Diagnosis with Mole

Primary tool: Use Mole for disk analysis. It provides comprehensive, categorized results.

1.1 Pre-flight Checks

# Check Mole installation and version
which mo && mo --version

# If not installed
brew install tw93/tap/mole

# Check for updates (Mole updates frequently)
brew info tw93/tap/mole | head -5

# Upgrade if outdated
brew upgrade tw93/tap/mole

1.2 Choose Analysis Method

IMPORTANT: Use mo analyze as the primary analysis tool, NOT mo clean --dry-run.

Command	Purpose	Use When
mo analyze	Interactive disk usage explorer (TUI tree view)	PRIMARY: Understanding what's consuming space
mo clean --dry-run	Preview cleanup categories	SECONDARY: Only after mo analyze to see cleanup preview

Why prefer mo analyze:

• Dedicated disk analysis tool with interactive tree navigation
• Allows drilling down into specific directories
• Shows actual disk usage breakdown, not just cleanup categories
• More informative for understanding storage consumption

1.3 Run Analysis via tmux

IMPORTANT: Mole requires TTY. Always use tmux from Claude Code.

CRITICAL TIMING NOTE: Home directory scans are SLOW (5-10 minutes or longer for large directories). Inform user upfront and wait patiently.

# Create tmux session
tmux new-session -d -s mole -x 120 -y 40

# Run disk analysis (PRIMARY tool - interactive TUI)
tmux send-keys -t mole 'mo analyze' Enter

# Wait for scan - BE PATIENT!
# Home directory scanning typically takes 5-10 minutes
# Report progress to user regularly
sleep 60 && tmux capture-pane -t mole -p

# Navigate the TUI with arrow keys
tmux send-keys -t mole Down    # Move to next item
tmux send-keys -t mole Enter   # Expand/select item
tmux send-keys -t mole 'q'     # Quit when done

Alternative: Cleanup preview (use AFTER mo analyze)

# Run dry-run preview (SAFE - no deletion)
tmux send-keys -t mole 'mo clean --dry-run' Enter

# Wait for scan (report progress to user every 30 seconds)
# Be patient! Large directories take 5-10 minutes
sleep 30 && tmux capture-pane -t mole -p

1.4 Progress Reporting

Report scan progress to user regularly:

📊 Disk Analysis in Progress...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⏱️ Elapsed: 2 minutes

Current status:
✅ Applications: 49.5 GB (complete)
✅ System Library: 10.3 GB (complete)
⏳ Home: scanning... (this may take 5-10 minutes)
⏳ App Library: pending

I'm waiting patiently for the scan to complete.
Will report again in 30 seconds...

1.5 Present Final Findings

After scan completes, present structured results:

📊 Disk Space Analysis (via Mole)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Free space: 27 GB

🧹 Recoverable Space (dry-run preview):

➤ User Essentials
  • User app cache:     16.67 GB
  • User app logs:      102.3 MB
  • Trash:              642.9 MB

➤ Browser Caches
  • Chrome cache:       1.90 GB
  • Safari cache:       4 KB

➤ Developer Tools
  • uv cache:           9.96 GB
  • npm cache:          (detected)
  • Docker cache:       (detected)
  • Homebrew cache:     (detected)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Total recoverable: ~30 GB

⚠️ This was a dry-run preview. No files were deleted.

Step 2: Deep Analysis Categories

Scan the following categories systematically. Reference references/cleanup_targets.md for detailed explanations.

Category 1: System & Application Caches

Locations to analyze:

• ~/Library/Caches/* - User application caches
• /Library/Caches/* - System-wide caches (requires sudo)
• ~/Library/Logs/* - Application logs
• /var/log/* - System logs (requires sudo)

Analysis script:

scripts/analyze_caches.py --user-only

Safety level: 🟢 Generally safe to delete (apps regenerate caches)

Exceptions to preserve:

• Browser caches while browser is running
• IDE caches (may slow down next startup)
• Package manager caches (Homebrew, pip, npm)

Category 2: Application Remnants

Locations to analyze:

• ~/Library/Application Support/* - App data
• ~/Library/Preferences/* - Preference files
• ~/Library/Containers/* - Sandboxed app data

Analysis approach:

1. List installed applications in /Applications
2. Cross-reference with ~/Library/Application Support
3. Identify orphaned folders (app uninstalled but data remains)

Analysis script:

scripts/find_app_remnants.py

Safety level: 🟡 Caution required

• ✅ Safe: Folders for clearly uninstalled apps
• ⚠️ Check first: Folders for apps you rarely use
• ❌ Keep: Active application data

Category 3: Large Files & Duplicates

Analysis script:

scripts/analyze_large_files.py --threshold 100MB --path ~

Find duplicates (optional, resource-intensive):

# Use fdupes if installed
if command -v fdupes &> /dev/null; then
  fdupes -r ~/Documents ~/Downloads
fi

Present findings:

📦 Large Files (>100MB):
━━━━━━━━━━━━━━━━━━━━━━━━
1. movie.mp4                    4.2 GB  ~/Downloads
2. dataset.csv                  1.8 GB  ~/Documents/data
3. old_backup.zip               1.5 GB  ~/Desktop
...

🔁 Duplicate Files:
- screenshot.png (3 copies)     15 MB each
- document_v1.docx (2 copies)   8 MB each

Safety level: 🟡 User judgment required

Category 4: Development Environment Cleanup

Targets:

• Docker: images, containers, volumes, build cache
• Homebrew: cache, old versions
• Node.js: node_modules, npm cache
• Python: pip cache, __pycache__, venv
• Git: .git folders in archived projects

Analysis script:

scripts/analyze_dev_env.py

Example findings:

🐳 Docker Resources:
- Unused images:      12 GB
- Stopped containers:  2 GB
- Build cache:         8 GB
- Orphaned volumes:    3 GB
Total potential:      25 GB

📦 Package Managers:
- Homebrew cache:      5 GB
- npm cache:           3 GB
- pip cache:           1 GB
Total potential:       9 GB

🗂️  Old Projects:
- archived-project-2022/.git  500 MB
- old-prototype/.git          300 MB

Cleanup commands (require confirmation):

# Homebrew cleanup (safe)
brew cleanup -s

# npm _npx only (safe - temporary packages)
rm -rf ~/.npm/_npx

# pip cache (use with caution)
pip cache purge

Docker cleanup - SPECIAL HANDLING REQUIRED:

⚠️ NEVER use these commands:

# ❌ DANGEROUS - deletes ALL volumes without confirmation
docker volume prune -f
docker system prune -a --volumes

✅ Correct approach - per-volume confirmation:

# 1. List all volumes
docker volume ls

# 2. Identify which projects each volume belongs to
docker volume inspect <volume_name>

# 3. Ask user to confirm EACH project they want to delete
# Example: "Do you want to delete all volumes for 'ragflow' project?"

# 4. Delete specific volumes only after confirmation
docker volume rm ragflow_mysql_data ragflow_redis_data

Safety level: 🟢 Homebrew/npm cleanup, 🔴 Docker volumes require per-project confirmation

Step 2A: Docker Deep Analysis

Use agent team to analyze Docker resources in parallel for comprehensive coverage:

Agent 1 — Images:

# List all images sorted by size
docker images --format "table {{.ID}}\t{{.Repository}}:{{.Tag}}\t{{.Size}}\t{{.CreatedSince}}" | sort -k3 -h -r

# Identify dangling images (no tag)
docker images -f "dangling=true" --format "{{.ID}}\t{{.Size}}\t{{.CreatedSince}}"

# For each image, check if any container references it
docker ps -a --filter "ancestor=<IMAGE_ID>" --format "{{.Names}}\t{{.Status}}"

Agent 2 — Containers and Volumes:

# All containers with status
docker ps -a --format "table {{.Names}}\t{{.Image}}\t{{.Status}}\t{{.Size}}"

# All volumes with size
docker system df -v | grep -A 1000 "VOLUME NAME"

# Identify dangling volumes
docker volume ls -f dangling=true

# For each volume, check which container uses it
docker ps -a --filter "volume=<VOLUME_NAME>" --format "{{.Names}}"

Agent 3 — System Level:

# Docker disk usage summary
docker system df

# Build cache
docker builder du

# Container logs size
for c in $(docker ps -a --format "{{.Names}}"); do
  echo "$c: $(docker inspect --format='{{.LogPath}}' $c | xargs ls -lh 2>/dev/null | awk '{print $5}')"
done

Version Management Awareness: Identify version-managed images (e.g., Supabase managed by CLI). When newer versions are confirmed running, older versions are safe to remove. Pay attention to Docker Compose naming conventions (dash vs underscore).

Step 2B: OrbStack-Specific Analysis

OrbStack users have additional considerations.

data.img.raw is a Sparse File:

# Logical size (can show 8TB+, meaningless)
ls -lh ~/Library/OrbStack/data/data.img.raw

# Actual disk usage (this is what matters)
du -h ~/Library/OrbStack/data/data.img.raw

The logical vs actual size difference is normal. Only actual usage counts.

Post-Cleanup: Reclaim Disk Space: After cleaning Docker objects inside OrbStack, data.img.raw does NOT shrink automatically. Instruct user: Open OrbStack Settings → "Reclaim disk space" to compact the sparse file.

OrbStack Logs: Typically 1-2 MB total (~/Library/OrbStack/log/). Not worth cleaning.

Step 2C: Double-Check Verification Protocol

Before deleting ANY Docker object, perform independent verification.

For Images:

# Verify no container (running or stopped) references the image
docker ps -a --filter "ancestor=<IMAGE_ID>" --format "{{.Names}}\t{{.Status}}"

# If empty → safe to delete with: docker rmi <IMAGE_ID>

For Volumes:

# Verify no container mounts the volume
docker ps -a --filter "volume=<VOLUME_NAME>" --format "{{.Names}}"

# If empty → check if database volume (see below)
# If not database → safe to delete with: docker volume rm <VOLUME_NAME>

Database Volume Red Flag Rule: If volume name contains mysql, postgres, redis, mongo, or mariadb, MANDATORY content inspection:

# Inspect database volume contents with temporary container
docker run --rm -v <VOLUME_NAME>:/data alpine ls -la /data
docker run --rm -v <VOLUME_NAME>:/data alpine du -sh /data/*

Only delete after user confirms the data is not needed.

Step 3: Integration with Mole

Mole (https://github.com/tw93/Mole) is a command-line interface (CLI) tool for comprehensive macOS cleanup. It provides interactive terminal-based analysis and cleanup for caches, logs, developer tools, and more.

CRITICAL REQUIREMENTS:

1. TTY Environment: Mole requires a TTY for interactive commands. Use tmux when running from Claude Code or scripts.
2. Version Check: Always verify Mole is up-to-date before use.
3. Safe Help Command: Only mo --help is safe. Do NOT append --help to other commands.

Installation check and upgrade:

# Check if installed and get version
which mo && mo --version

# If not installed
brew install tw93/tap/mole

# Check for updates
brew info tw93/tap/mole | head -5

# Upgrade if needed
brew upgrade tw93/tap/mole

Using Mole with tmux (REQUIRED for Claude Code):

# Create tmux session for TTY environment
tmux new-session -d -s mole -x 120 -y 40

# Run analysis (safe, read-only)
tmux send-keys -t mole 'mo analyze' Enter

# Wait for scan (be patient - can take 5-10 minutes for large directories)
sleep 60

# Capture results
tmux capture-pane -t mole -p

# Cleanup when done
tmux kill-session -t mole

Available commands (from mo --help):

Command	Safety	Description
mo --help	✅ Safe	View all commands (ONLY safe help)
mo analyze	✅ Safe	Disk usage explorer (read-only)
mo status	✅ Safe	System health monitor
mo clean --dry-run	✅ Safe	Preview cleanup (no deletion)
mo clean	⚠️ DANGEROUS	Actually deletes files
mo purge	⚠️ DANGEROUS	Remove project artifacts
mo uninstall	⚠️ DANGEROUS	Remove applications

Reference guide: See references/mole_integration.md for detailed tmux workflow and troubleshooting.

Multi-Layer Deep Exploration with Mole

CRITICAL: For comprehensive analysis, you MUST perform multi-layer exploration, not just top-level scans. This section documents the proven workflow for navigating Mole's TUI.

Navigation Commands

# Create session
tmux new-session -d -s mole -x 120 -y 40

# Start analysis
tmux send-keys -t mole 'mo analyze' Enter

# Wait for initial scan
sleep 8 && tmux capture-pane -t mole -p

# Navigation keys (send via tmux)
tmux send-keys -t mole Enter    # Enter/expand selected directory
tmux send-keys -t mole Left     # Go back to parent directory
tmux send-keys -t mole Down     # Move to next item
tmux send-keys -t mole Up       # Move to previous item
tmux send-keys -t mole 'q'      # Quit TUI

# Capture current view
tmux capture-pane -t mole -p

Multi-Layer Exploration Workflow

Step 1: Top-level overview

# Start mo analyze, wait for initial menu
tmux send-keys -t mole 'mo analyze' Enter
sleep 8 && tmux capture-pane -t mole -p

# Example output:
# 1. Home           289.4 GB (58.5%)
# 2. App Library    145.2 GB (29.4%)
# 3. Applications    49.5 GB (10.0%)
# 4. System Library  10.3 GB (2.1%)

Step 2: Enter largest directory (Home)

tmux send-keys -t mole Enter
sleep 10 && tmux capture-pane -t mole -p

# Example output:
# 1. Library       144.4 GB (49.9%)
# 2. Workspace      52.0 GB (18.0%)
# 3. .cache         19.3 GB (6.7%)
# 4. Applications   17.0 GB (5.9%)
# ...

Step 3: Drill into specific directories

# Go to .cache (3rd item: Down Down Enter)
tmux send-keys -t mole Down Down Enter
sleep 5 && tmux capture-pane -t mole -p

# Example output:
# 1. uv           10.3 GB (55.6%)
# 2. modelscope    5.5 GB (29.5%)
# 3. huggingface   887.8 MB (4.7%)

Step 4: Navigate back and explore another branch

# Go back to parent
tmux send-keys -t mole Left
sleep 2

# Navigate to different directory
tmux send-keys -t mole Down Down Down Down Enter  # Go to .npm
sleep 5 && tmux capture-pane -t mole -p

Step 5: Deep dive into Library

# Back to Home, then into Library
tmux send-keys -t mole Left
tmux send-keys -t mole Up Up Up Up Up Up Enter  # Go to Library
sleep 10 && tmux capture-pane -t mole -p

# Example output:
# 1. Application Support  37.1 GB
# 2. Containers          35.4 GB
# 3. Developer           17.8 GB  ← Xcode is here
# 4. Caches               8.2 GB

Recommended Exploration Path

For comprehensive analysis, follow this exploration tree:

mo analyze
├── Home (Enter)
│   ├── Library (Enter)
│   │   ├── Developer (Enter) → Xcode/DerivedData, iOS DeviceSupport
│   │   ├── Caches (Enter) → Playwright, JetBrains, etc.
│   │   └── Application Support (Enter) → App data
│   ├── .cache (Enter) → uv, modelscope, huggingface
│   ├── .npm (Enter) → _cacache, _npx
│   ├── Downloads (Enter) → Large files to review
│   ├── .Trash (Enter) → Confirm trash contents
│   └── miniconda3/other dev tools (Enter) → Check last used time
├── App Library → Usually overlaps with ~/Library
└── Applications → Installed apps

Time Expectations

Directory	Scan Time	Notes
Top-level menu	5-8 seconds	Fast
Home directory	5-10 minutes	Large, be patient
~/Library	3-5 minutes	Many small files
Subdirectories	2-30 seconds	Varies by size

Example Complete Session

# 1. Create session
tmux new-session -d -s mole -x 120 -y 40

# 2. Start analysis and get overview
tmux send-keys -t mole 'mo analyze' Enter
sleep 8 && tmux capture-pane -t mole -p

# 3. Enter Home
tmux send-keys -t mole Enter
sleep 10 && tmux capture-pane -t mole -p

# 4. Enter .cache to see dev caches
tmux send-keys -t mole Down Down Enter
sleep 5 && tmux capture-pane -t mole -p

# 5. Back to Home, then to .npm
tmux send-keys -t mole Left
sleep 2
tmux send-keys -t mole Down Down Down Down Enter
sleep 5 && tmux capture-pane -t mole -p

# 6. Back to Home, enter Library
tmux send-keys -t mole Left
sleep 2
tmux send-keys -t mole Up Up Up Up Up Up Enter
sleep 10 && tmux capture-pane -t mole -p

# 7. Enter Developer to see Xcode
tmux send-keys -t mole Down Down Down Enter
sleep 5 && tmux capture-pane -t mole -p

# 8. Enter Xcode
tmux send-keys -t mole Enter
sleep 5 && tmux capture-pane -t mole -p

# 9. Enter DerivedData to see projects
tmux send-keys -t mole Enter
sleep 5 && tmux capture-pane -t mole -p

# 10. Cleanup
tmux kill-session -t mole

Key Insights from Exploration

After multi-layer exploration, you will discover:

1. What projects are using DerivedData - specific project names
2. Which caches are actually large - uv vs npm vs others
3. Age of files - Mole shows ">3mo", ">7mo", ">1yr" markers
4. Specific volumes and their purposes - Docker project data
5. Downloads that can be cleaned - old dmgs, duplicate files

Anti-Patterns: What NOT to Delete

CRITICAL: The following items are often suggested for cleanup but should NOT be deleted in most cases. They provide significant value that outweighs the space they consume.

Items to KEEP (Anti-Patterns)

Item	Size	Why NOT to Delete	Real Impact of Deletion
Xcode DerivedData	10+ GB	Build cache saves 10-30 min per full rebuild	Next build takes 10-30 minutes longer
npm _cacache	5+ GB	Downloaded packages cached locally	npm install redownloads everything (30min-2hr in China)
~/.cache/uv	10+ GB	Python package cache	Every Python project reinstalls deps from PyPI
Playwright browsers	3-4 GB	Browser binaries for automation testing	Redownload 2GB+ each time (30min-1hr)
iOS DeviceSupport	2-3 GB	Required for device debugging	Redownload from Apple when connecting device
Docker stopped containers	<500 MB	May restart anytime with docker start	Lose container state, need to recreate
~/.cache/huggingface	varies	AI model cache	Redownload large models (hours)
~/.cache/modelscope	varies	AI model cache (China)	Same as above
JetBrains caches	1+ GB	IDE indexing and caches	IDE takes 5-10 min to re-index

Why This Matters

The vanity trap: Showing "Cleaned 50GB!" feels good but:

• User spends next 2 hours redownloading npm packages
• Next Xcode build takes 30 minutes instead of 30 seconds
• AI project fails because models need redownload

The right mindset: "I found 50GB of caches. Here's why most of them are actually valuable and should be kept..."

What IS Actually Safe to Delete

Item	Why Safe	Impact
Trash	User already deleted these files	None - user's decision
Homebrew old versions	Replaced by newer versions	Rare: can't rollback to old version
npm _npx	Temporary npx executions	Minor: npx re-downloads on next use
Orphaned app remnants	App already uninstalled	None - app doesn't exist
Specific unused Docker volumes	Projects confirmed abandoned	None - if truly abandoned

Report Format Requirements

Every cleanup report MUST follow this format with impact analysis:

## Disk Analysis Report

### Classification Legend
| Symbol | Meaning |
|--------|---------|
| 🟢 | **Absolutely Safe** - No negative impact, truly unused |
| 🟡 | **Trade-off Required** - Useful cache, deletion has cost |
| 🔴 | **Do Not Delete** - Contains valuable data or actively used |

### Findings

| Item | Size | Classification | What It Is | Impact If Deleted |
|------|------|----------------|------------|-------------------|
| Trash | 643 MB | 🟢 | Files you deleted | None |
| npm _npx | 2.1 GB | 🟢 | Temp npx packages | Minor redownload |
| npm _cacache | 5 GB | 🟡 | Package cache | 30min-2hr redownload |
| DerivedData | 10 GB | 🟡 | Xcode build cache | 10-30min rebuild |
| Docker volumes | 11 GB | 🔴 | Project databases | **DATA LOSS** |

### Recommendation
Only items marked 🟢 are recommended for cleanup.
Items marked 🟡 require your judgment based on usage patterns.
Items marked 🔴 require explicit confirmation per-item.

Docker Report: Required Object-Level Detail

Docker reports MUST list every individual object, not just categories:

#### Dangling Images (no tag, no container references)
| Image ID | Size | Created | Safe? |
|----------|------|---------|-------|
| a02c40cc28df | 884 MB | 2 months ago | ✅ No container uses it |
| 555434521374 | 231 MB | 3 months ago | ✅ No container uses it |

#### Stopped Containers
| Name | Image | Status | Size |
|------|-------|--------|------|
| ragflow-mysql | mysql:8.0 | Exited 2 weeks ago | 1.2 GB |

#### Volumes
| Volume | Size | Mounted By | Contains |
|--------|------|------------|----------|
| ragflow_mysql_data | 1.8 GB | ragflow-mysql | MySQL databases |
| redis_data | 500 MB | (none - dangling) | Redis dump |

#### 🔴 Database Volumes Requiring Inspection
| Volume | Inspected Contents | User Decision |
|--------|--------------------|---------------|
| ragflow_mysql_data | 8 databases, 45 tables | Still need? |

High-Quality Report Template

After multi-layer exploration, present findings using this proven template:

## 📊 磁盘空间深度分析报告

**分析日期**: YYYY-MM-DD
**使用工具**: Mole CLI + 多层目录探索
**分析原则**: 安全第一，价值优于虚荣

---

### 总览

| 区域 | 总占用 | 关键发现 |
|------|--------|----------|
| **Home** | XXX GB | Library占一半(XXX GB) |
| **App Library** | XXX GB | 与Home/Library重叠统计 |
| **Applications** | XXX GB | 应用本体 |

---

### 🟢 绝对安全可删除 (约 X.X GB)

| 项目 | 大小 | 位置 | 删除后影响 | 清理命令 |
|------|------|------|-----------|---------|
| **废纸篓** | XXX MB | ~/.Trash | 无 - 你已决定删除的文件 | 清空废纸篓 |
| **npm _npx** | X.X GB | ~/.npm/_npx | 下次 npx 命令重新下载 | `rm -rf ~/.npm/_npx` |
| **Homebrew 旧版本** | XX MB | /opt/homebrew | 无 - 已被新版本替代 | `brew cleanup --prune=0` |

**废纸篓内容预览**:
- [列出主要文件]

---

### 🟡 需要你确认的项目

#### 1. [项目名] (X.X GB) - [状态描述]

| 子目录 | 大小 | 最后使用 |
|--------|------|----------|
| [子目录1] | X.X GB | >X个月 |
| [子目录2] | X.X GB | >X个月 |

**问题**: [需要用户回答的问题]

---

#### 2. Downloads 中的旧文件 (X.X GB)

| 文件/目录 | 大小 | 年龄 | 建议 |
|-----------|------|------|------|
| [文件1] | X.X GB | - | [建议] |
| [文件2] | XXX MB | >X个月 | [建议] |

**建议**: 手动检查 Downloads，删除已不需要的文件。

---

#### 3. 停用的 Docker 项目 Volumes

| 项目前缀 | 可能包含的数据 | 需要你确认 |
|---------|--------------|-----------|
| `project1_*` | MySQL, Redis | 还在用吗？ |
| `project2_*` | Postgres | 还在用吗？ |

**注意**: 我不会使用 `docker volume prune -f`，只会在你确认后删除特定项目的 volumes。

---

### 🔴 不建议删除的项目 (有价值的缓存)

| 项目 | 大小 | 为什么要保留 |
|------|------|-------------|
| **Xcode DerivedData** | XX GB | [项目名]的编译缓存，删除后下次构建需要X分钟 |
| **npm _cacache** | X.X GB | 所有下载过的 npm 包，删除后需要重新下载 |
| **~/.cache/uv** | XX GB | Python 包缓存，重新下载在中国网络下很慢 |
| [其他有价值的缓存] | X.X GB | [保留原因] |

---

### 📋 其他发现

| 项目 | 大小 | 说明 |
|------|------|------|
| **OrbStack/Docker** | XX GB | 正常的 VM/容器占用 |
| [其他发现] | X.X GB | [说明] |

---

### ✅ 推荐操作

**立即可执行** (无需确认):
```bash
# 1. 清空废纸篓 (XXX MB)
# 手动: Finder → 清空废纸篓

# 2. npm _npx (X.X GB)
rm -rf ~/.npm/_npx

# 3. Homebrew 旧版本 (XX MB)
brew cleanup --prune=0

预计释放: ~X.X GB

---

需要你确认后执行:

1. [项目1] - [确认问题]
2. [项目2] - [确认问题]
3. Docker 项目 - 告诉我哪些项目确定不用了

### Report Quality Checklist

Before presenting the report, verify:

- [ ] Every item has "Impact If Deleted" explanation
- [ ] 🟢 items are truly safe (Trash, _npx, old versions)
- [ ] 🟡 items require user decision (age info, usage patterns)
- [ ] 🔴 items explain WHY they should be kept
- [ ] Docker volumes listed by project, not blanket prune
- [ ] Network environment considered (China = slow redownload)
- [ ] No recommendations to delete useful caches just to inflate numbers
- [ ] Clear action items with exact commands

## Step 4: Present Recommendations

Format findings into actionable recommendations with risk levels:

```markdown
# macOS Cleanup Recommendations

## Summary
Total space recoverable: ~XX GB
Current usage: XX%

## Recommended Actions

### 🟢 Safe to Execute (Low Risk)
These are safe to delete and will be regenerated as needed:

1. **Empty Trash** (~12 GB)
   - Location: ~/.Trash
   - Command: `rm -rf ~/.Trash/*`

2. **Clear System Caches** (~45 GB)
   - Location: ~/Library/Caches
   - Command: `rm -rf ~/Library/Caches/*`
   - Note: Apps may be slightly slower on next launch

3. **Remove Homebrew Cache** (~5 GB)
   - Command: `brew cleanup -s`

### 🟡 Review Recommended (Medium Risk)
Review these items before deletion:

1. **Large Downloads** (~38 GB)
   - Location: ~/Downloads
   - Action: Manually review and delete unneeded files
   - Files: [list top 10 largest files]

2. **Application Remnants** (~8 GB)
   - Apps: [list detected uninstalled apps]
   - Locations: [list paths]
   - Action: Confirm apps are truly uninstalled before deleting data

### 🔴 Keep Unless Certain (High Risk)
Only delete if you know what you're doing:

1. **Docker Volumes** (~3 GB)
   - May contain important data
   - Review with: `docker volume ls`

2. **Time Machine Local Snapshots** (~XX GB)
   - Automatic backups, will be deleted when space needed
   - Command to check: `tmutil listlocalsnapshots /`

Step 5: Execute with Confirmation

CRITICAL: Never execute deletions without explicit user confirmation.

Interactive confirmation flow:

# Example from scripts/safe_delete.py
def confirm_delete(path: str, size: str, description: str) -> bool:
    """
    Ask user to confirm deletion.

    Args:
        path: File/directory path
        size: Human-readable size
        description: What this file/directory is

    Returns:
        True if user confirms, False otherwise
    """
    print(f"\n🗑️  Confirm Deletion")
    print(f"━━━━━━━━━━━━━━━━━━")
    print(f"Path:        {path}")
    print(f"Size:        {size}")
    print(f"Description: {description}")

    response = input("\nDelete this item? [y/N]: ").strip().lower()
    return response == 'y'

For batch operations:

def batch_confirm(items: list) -> list:
    """
    Show all items, ask for batch confirmation.

    Returns list of items user approved.
    """
    print("\n📋 Items to Delete:")
    print("━━━━━━━━━━━━━━━━━━")
    for i, item in enumerate(items, 1):
        print(f"{i}. {item['path']} ({item['size']})")

    print("\nOptions:")
    print("  'all'    - Delete all items")
    print("  '1,3,5'  - Delete specific items by number")
    print("  'none'   - Cancel")

    response = input("\nYour choice: ").strip().lower()

    if response == 'none':
        return []
    elif response == 'all':
        return items
    else:
        # Parse numbers
        indices = [int(x.strip()) - 1 for x in response.split(',')]
        return [items[i] for i in indices if 0 <= i < len(items)]

Step 6: Verify Results

After cleanup, verify the results and report back:

# Compare before/after
df -h /

# Calculate space recovered
# (handled by scripts/cleanup_report.py)

Report format:

✅ Cleanup Complete!

Before: 450 GB used (90%)
After:  385 GB used (77%)
━━━━━━━━━━━━━━━━━━━━━━━━
Recovered: 65 GB

Breakdown:
- System caches:        45 GB
- Downloads:            12 GB
- Homebrew cache:        5 GB
- Application remnants:  3 GB

⚠️ Notes:
- Some applications may take longer to launch on first run
- Deleted items cannot be recovered unless you have Time Machine backup
- Consider running this cleanup monthly

💡 Maintenance Tips:
- Set up automatic Homebrew cleanup: `brew cleanup` weekly
- Review Downloads folder monthly
- Enable "Empty Trash Automatically" in Finder preferences

Bonus: Dockerfile Optimization Discoveries

During image analysis, if you discover oversized images, suggest multi-stage build optimization:

# Before: 884 MB (full build environment in final image)
FROM node:20
COPY . .
RUN npm ci && npm run build
CMD ["node", "dist/index.js"]

# After: ~150 MB (only runtime in final image)
FROM node:20 AS builder
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:20-slim
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
CMD ["node", "dist/index.js"]

Key techniques: multi-stage builds, slim/alpine base images, .dockerignore, layer ordering.

⚠️ Safety Guidelines

Always Preserve

Never delete these without explicit user instruction:

• ~/Documents, ~/Desktop, ~/Pictures content
• Active project directories
• Database files (*.db, *.sqlite)
• Configuration files for active apps
• SSH keys, credentials, certificates
• Time Machine backups

⚠️ Require Sudo Confirmation

These operations require elevated privileges. Ask user to run commands manually:

• Clearing /Library/Caches (system-wide)
• Clearing /var/log (system logs)
• Clearing /private/var/folders (system temp)

Example prompt:

⚠️ This operation requires administrator privileges.

Please run this command manually:
  sudo rm -rf /Library/Caches/*

⚠️ You'll be asked for your password.

💡 Backup Recommendation

Before executing any cleanup >10GB, recommend:

💡 Safety Tip:
Before cleaning XX GB, consider creating a Time Machine backup.

Quick backup check:
  tmutil latestbackup

If no recent backup, run:
  tmutil startbackup

Troubleshooting

"Operation not permitted" errors

macOS may block deletion of certain system files due to SIP (System Integrity Protection).

Solution: Don't force it. These protections exist for security.

App crashes after cache deletion

Rare but possible. Solution: Restart the app, it will regenerate necessary caches.

Docker cleanup removes important data

Prevention: Always list Docker volumes before cleanup:

docker volume ls
docker volume inspect <volume_name>

Resources

scripts/

• analyze_caches.py - Scan and categorize cache directories
• find_app_remnants.py - Detect orphaned application data
• analyze_large_files.py - Find large files with smart filtering
• analyze_dev_env.py - Scan development environment resources
• safe_delete.py - Interactive deletion with confirmation
• cleanup_report.py - Generate before/after reports

references/

• cleanup_targets.md - Detailed explanations of each cleanup target
• mole_integration.md - How to use Mole alongside this skill
• safety_rules.md - Comprehensive list of what to never delete

Usage Examples

Example 1: Quick Cache Cleanup

User request: "My Mac is running out of space, can you help?"

Workflow:

1. Run quick diagnosis
2. Identify system caches as quick win
3. Present findings: "45 GB in ~/Library/Caches"
4. Explain: "These are safe to delete, apps will regenerate them"
5. Ask confirmation
6. Execute: rm -rf ~/Library/Caches/*
7. Report: "Recovered 45 GB"

Example 2: Development Environment Cleanup

User request: "I'm a developer and my disk is full"

Workflow:

1. Run scripts/analyze_dev_env.py
2. Present Docker + npm + Homebrew findings
3. Explain each category
4. Provide cleanup commands with explanations
5. Let user execute (don't auto-execute Docker cleanup)
6. Verify results

Example 3: Finding Large Files

User request: "What's taking up so much space?"

Workflow:

1. Run scripts/analyze_large_files.py --threshold 100MB
2. Present top 20 large files with context
3. Categorize: videos, datasets, archives, disk images
4. Let user decide what to delete
5. Execute confirmed deletions
6. Suggest archiving to external drive

Best Practices

1. Start Conservative: Begin with obviously safe targets (caches, trash)
2. Explain Everything: Users should understand what they're deleting
3. Show Examples: List 3-5 example files from each category
4. Respect User Pace: Don't rush through confirmations
5. Document Results: Always show before/after space usage
6. Educate: Include maintenance tips in final report
7. Integrate Tools: Suggest Mole for users who prefer GUI

When NOT to Use This Skill

• User wants automatic/silent cleanup (against safety-first principle)
• User needs Windows/Linux cleanup (macOS-specific skill)
• User has <10% disk usage (no cleanup needed)
• User wants to clean system files requiring SIP disable (security risk)

In these cases, explain limitations and suggest alternatives.