Diskman

Features

• 🔍 Smart Scan - Analyze directory sizes with link type detection
• 🧠 Intelligent Analysis - Rule-based + AI-powered recommendations
• 🔄 Safe Migration - Move directories using symbolic links
• 🧹 Smart Clean - Safe cleanup with risk evaluation
• 🤖 AI-Ready - Built-in AI analysis (OpenAI/DeepSeek/Qwen compatible)
• 🔌 MCP Integration - AI agent automation via MCP protocol
• 🔒 Accurate Statistics - Correctly handles symlinks/junctions to avoid double-counting

Install

# Core functionality
pip install diskman

# With MCP support (for AI agents)
pip install "diskman[mcp]"

# With AI support (for AI-powered analysis)
pip install "diskman[ai]"

# With everything
pip install "diskman[all]"

Quick Start

CLI Usage

# Scan directory
diskman scan ~/project

# Scan user profile for large directories
diskman profile

# Analyze a directory (get recommendations)
diskman analyze ~/.cache

# Migrate directory with symbolic link
diskman migrate ~/.conda /data/.conda

# Clean directory (dry run by default)
diskman clean ~/temp

# Check link status
diskman link ~/.cache

Python API

from diskman import DirectoryScanner, DirectoryAnalyzer, DirectoryMigrator

# Scan
scanner = DirectoryScanner()
result = scanner.scan_user_profile()

for info in result.directories[:10]:
    print(f"{info.size_mb:.0f} MB - {info.path}")

# Analyze
analyzer = DirectoryAnalyzer()
analysis = analyzer.analyze(result.directories[0])

print(f"Action: {analysis.recommended_action.value}")
print(f"Risk: {analysis.risk_level.value}")
print(f"Reason: {analysis.reason}")

# Migrate
migrator = DirectoryMigrator()
result = migrator.migrate("~/.conda", "/data/.conda")

AI-Powered Analysis

import asyncio
from diskman import DirectoryScanner, AIService, AIConfig

async def analyze_with_ai():
    # Configure AI (supports OpenAI-compatible APIs)
    ai = AIService(AIConfig(
        api_key="your-api-key",
        base_url="https://api.deepseek.com",  # or OpenAI, Qwen, etc.
        model="deepseek-chat",
    ))
    
    # Scan
    scanner = DirectoryScanner()
    result = scanner.scan_user_profile()
    
    # AI Analysis
    ai_result = await ai.analyze(
        directories=result.directories[:30],
        user_context="I'm a Python developer",
        target_drive="D:\\",
    )
    
    print(ai_result["summary"])
    for rec in ai_result["recommendations"]:
        print(f"{rec['path']}: {rec['action']} - {rec['reason']}")

asyncio.run(analyze_with_ai())

MCP Integration (for AI Agents)

Add to your MCP client configuration:

{
  "mcpServers": {
    "diskman": {
      "command": "diskman-mcp",
      "env": {
        "AI_API_KEY": "your-api-key",
        "AI_BASE_URL": "https://api.deepseek.com",
        "AI_MODEL": "deepseek-chat"
      }
    }
  }
}

Available MCP Tools:

Architecture

diskman/
├── operations/       # File system operations
│   ├── scanner.py   # Directory scanning with link detection
│   ├── migrator.py  # Migration with symbolic links
│   └── cleaner.py   # Safe cleanup
│
├── analysis/         # Directory analysis
│   ├── analyzer.py  # Rule-based recommendations
│   └── rules/       # Built-in analysis rules
│
├── ai/              # AI-powered analysis
│   ├── service.py   # AI service wrapper
│   └── providers/   # OpenAI-compatible providers
│
├── mcp/             # MCP server for AI agents
└── cli.py           # Command-line interface

Smart Analysis Mode

analyze_directories automatically chooses the best analysis method:

This ensures the tool always works, with or without AI configuration.

Two Analysis Modes

Rule-Based Analysis (Default)

• 40+ built-in rules for common directories
• Pattern-based heuristics for unknown directories
• Risk assessment: safe/low/medium/high/critical
• Action recommendations: can_delete, can_move, keep, review

AI-Powered Analysis

• Context-aware recommendations
• Natural language explanations
• Supports any OpenAI-compatible API:
  • OpenAI (gpt-4o-mini, gpt-4o)
  • DeepSeek (deepseek-chat)
  • Qwen (qwen-turbo, qwen-plus)
  • Local models (Ollama, vLLM)

Configuration

Parameter Passing (Recommended)

from diskman import AIService, AIConfig
from diskman.mcp import create_mcp_server

# Python API
ai = AIService(AIConfig(
    api_key="your-api-key",
    base_url="https://api.deepseek.com",
    model="deepseek-chat",
))

# MCP Server
mcp = create_mcp_server(AIConfig(
    api_key="your-api-key",
    base_url="https://api.deepseek.com",
    model="deepseek-chat",
))

Environment Variables

Examples

Find large directories

from diskman import DirectoryScanner

scanner = DirectoryScanner()
result = scanner.scan_user_profile()

print(f"Total: {result.total_size_gb:.1f} GB")
for info in result.directories[:20]:
    print(f"{info.size_mb:>8.0f} MB  {info.path}")

Get cleanup recommendations

from diskman import DirectoryScanner, DirectoryAnalyzer

scanner = DirectoryScanner()
analyzer = DirectoryAnalyzer()

result = scanner.scan_user_profile()

for info in result.directories[:10]:
    analysis = analyzer.analyze(info)
    if analysis.recommended_action.value == "can_delete":
        print(f"✓ {info.path}")
        print(f"  {info.size_mb:.0f} MB - {analysis.reason}")

Migrate a directory

from diskman import DirectoryMigrator

migrator = DirectoryMigrator()

# Move conda environment to D drive
result = migrator.migrate(
    source=r"C:\Users\you\.conda",
    target=r"D:\migrated\.conda"
)

if result.success:
    print(f"Done! Created {result.link_type}")

Accurate Space Statistics

Diskman correctly handles symbolic links and junctions:

• Symlinks/Junctions: Report 0 size by default (data is on target drive)
• Normal directories: Report actual size
• count_link_target=True: Include symlink target size if needed

# Default: symlinks show 0 size (accurate C: drive usage)
info = scanner.scan_directory("C:\\Users\\you\\LinkedFolder")

# Include target size for total data analysis
info = scanner.scan_directory("C:\\Users\\you\\LinkedFolder", count_link_target=True)

License

MIT