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RClean

A high-performance Rust-based disk cleanup tool that finds duplicate files and storage outliers.

Features

• Duplicate Detection: Find duplicate files using MD5 hashing with parallel processing
• Similar File Detection: Identify similar files using fuzzy matching algorithms
• Storage Outliers: Detect large files, hidden space consumers, and file patterns
• Cluster Analysis: Find groups of similar large files using DBSCAN clustering
• Fast Performance: Leverages Rust's parallelization with Rayon
• Multiple Output Formats: Table, JSON, CSV reports
• MCP Support: Can be used as an MCP (Model Context Protocol) server

Quality Standards

PMAT-Certified Quality Gates - Following Production Manufacturing and Assembly Technology principles:

Metric	Target	Status	Verification
Technical Debt Gauge (TDG)	≤ 1.0	✅	make quality-gate
Cyclomatic Complexity	≤ 20	✅	make lint
SATD Comments	0	✅	make lint
Test Coverage	≥ 80%	✅	make coverage
Lint Violations	0	✅	make lint
Security Vulnerabilities	0	✅	make security-audit
Documentation Coverage	100%	✅	make test-doc

Testing Strategy

• 🧪 126+ Total Tests: Comprehensive multi-layer testing approach
• 🔗 Integration Tests: End-to-end workflow validation
• 🎲 Property Tests: Mathematical invariant verification (proptest)
• 📚 Documentation Tests: 6+ executable examples in docs
• 📋 Example Tests: Real-world usage demonstrations
• 🚀 Performance Tests: Efficiency and scalability validation

Quality Automation

• 🔄 Continuous Integration: Cross-platform testing (Linux, macOS, Windows)
• 🛡️ Security Scanning: Automated vulnerability detection
• 📊 Coverage Monitoring: Comprehensive test coverage analysis
• 🔍 Code Quality: Zero-tolerance linting with clippy
• 🏗️ Release Pipeline: Automated binary builds and publishing
• ✅ Documentation: All public APIs documented with examples
• ✅ Coverage: High test coverage with comprehensive edge case testing

Installation

Requirements

• Rust: 1.70+ (MSRV - Minimum Supported Rust Version)
• Platform: Linux, macOS, Windows (x86_64)
• Memory: 512MB+ RAM recommended for large datasets

Method 1: From crates.io (Recommended)

# Install latest stable release
cargo install rclean

# Verify installation
rclean --version

Method 2: From GitHub Releases

Download pre-built binaries from Releases:

# Linux/macOS
curl -L https://github.com/paiml/rclean/releases/latest/download/rclean-x86_64-unknown-linux-gnu.tar.gz | tar xz
sudo mv rclean /usr/local/bin/

# Or using wget
wget https://github.com/paiml/rclean/releases/latest/download/rclean-x86_64-unknown-linux-gnu.tar.gz
tar xf rclean-x86_64-unknown-linux-gnu.tar.gz
sudo mv rclean /usr/local/bin/

Method 3: From Source (Development)

# Clone repository
git clone https://github.com/paiml/rclean.git
cd rclean

# Build and install (debug)
cargo install --path .

# Or build release version
make build-release
sudo cp target/release/rclean /usr/local/bin/

Method 4: Development Setup

# Clone and setup development environment
git clone https://github.com/paiml/rclean.git
cd rclean

# Install development dependencies
rustup component add rustfmt clippy
cargo install cargo-audit cargo-tarpaulin

# Verify development setup
make quality-gate

Verification

# Check installation
rclean --version
# Expected: rclean 0.1.2

# Run basic test
rclean --help

# Test with current directory
rclean

Quick Start

# Scan current directory for duplicates
rclean

# Scan specific directory
rclean /path/to/directory

# Filter by pattern
rclean ~/Documents --pattern "*.pdf" --pattern-type glob

# Generate CSV report
rclean . --csv duplicate_report.csv

# Find similar files (fuzzy matching) with 70% similarity threshold
rclean ~/Documents --similarity 70

Storage Outliers Detection (NEW!)

Find files that are consuming disproportionate disk space:

# Find large file outliers
rclean outliers /path --min-size 100MB

# Find hidden space consumers (node_modules, .git, etc.)
rclean outliers ~ --check-hidden --format json

# Find file patterns (backups, logs, etc.)
rclean outliers . --check-patterns

# Export outliers report
rclean outliers . --csv outliers_report.csv

# Combine all features
rclean outliers ~ --min-size 50MB --check-hidden --check-patterns --top 50

# Enable clustering to find groups of similar large files
rclean outliers /path --cluster --cluster-similarity 80 --min-cluster-size 3

Outliers Detection Features:

• Statistical Analysis: Files that are X standard deviations larger than the mean
• Hidden Consumers: Detects node_modules, .git, .cache, and other known space hogs
• Pattern Detection: Finds groups of similar files (backup-001, backup-002, etc.)
• Cluster Analysis: Uses DBSCAN to find clusters of similar large files (e.g., different versions of the same document)
• Smart Recommendations: Provides cleanup suggestions for each type of outlier

Fuzzy Matching (Similarity Detection)

Find files that are similar but not identical:

# Find files with 70% or higher similarity
rclean ~/Documents --similarity 70

# Find similar Python files
rclean ~/code --pattern "*.py" --pattern-type glob --similarity 80

# Generate CSV report including similar files
rclean . --similarity 60 --csv similarity_report.csv

Use Cases:

• Different versions of documents (v1, v2, draft, final)
• Slightly modified code files
• Images with minor edits
• Reports with small updates

Advanced Pattern Matching

RClean supports ripgrep-style pattern matching:

Pattern Types

• Literal (default): Simple string contains matching

  rclean search --path . --pattern ".txt"

• Glob: Shell-style patterns

  rclean search --path . --pattern "*.txt" --pattern-type glob
  rclean search --path . --pattern "**/*.rs" --pattern-type glob

• Regex: Full regular expression support

  rclean search --path . --pattern "test_.*\.rs$" --pattern-type regex

Additional Options

• --hidden: Include hidden files
• --no-ignore: Ignore .gitignore rules
• --max-depth <N>: Maximum directory depth to traverse

MCP Server Mode

RClean can run as an MCP server for integration with AI assistants:

# Run as MCP server
rclean  # Will auto-detect MCP mode when piped

Building and Development

Quality Standards ✅

All lint checks now pass! The project follows PMAT (Production Manufacturing and Assembly Technology) quality standards with zero tolerance for warnings.

# Build and test
make all

# Development commands
make format        # Format code
make lint          # Run clippy linting (FIXED - passes cleanly!)
make lint-extreme  # Run extreme linting with PMAT standards
make test          # Run all tests
make test-examples # Run example tests (NEW!)

# Build variants
make build-release # Release build for production

# Quality assurance
make quality-gate  # Run all quality checks
make format-check  # Verify formatting

Recent Improvements (v0.1.1)

• ✅ Fixed all clippy warnings - make lint now passes without errors
• ✅ Added example test coverage - New make test-examples target
• ✅ Improved code quality - Reduced function complexity with better abstractions
• ✅ Enhanced CI/CD readiness - All quality gates pass consistently

OS X Install

• Install rust via rustup
• Add to ~/.cargo/config

[target.x86_64-apple-darwin]
rustflags = [
  "-C", "link-arg=-undefined",
  "-C", "link-arg=dynamic_lookup",
]

[target.aarch64-apple-darwin]
rustflags = [
  "-C", "link-arg=-undefined",
  "-C", "link-arg=dynamic_lookup",
]

• run make all in rclean directory

License

MIT