Pattern Miner

Discover patterns in your own data files with local analysis.

Overview

Pattern Miner helps you identify recurring patterns, associations, and anomalies in your own data files. All processing happens locally on your machine.

Features

• Pattern Recognition: Identify recurring themes in your data
• Association Discovery: Find items that frequently occur together
• Anomaly Detection: Spot unusual patterns that deviate from norms
• Local Processing: All analysis runs on your device, no data upload

When to Use

• Analyzing your own structured data files (CSV, JSON)
• Finding patterns in task lists or notes
• Identifying trends in personal datasets
• Discovering associations in your own records

Usage

Basic Analysis

# Analyze a JSON file
python3 scripts/analyze.py ~/data/my-tasks.json

Requirements

pip install numpy scikit-learn pandas

Scripts

• scripts/analyze.py - Analyze JSON files for patterns

Configuration

Create a config file at ~/.pattern-miner/config.json:

{
  "minConfidence": 0.6,
  "minFrequency": 3,
  "analysisTypes": ["cluster", "association"]
}

Input Formats

Supported file formats:

• JSON (array of objects)
• CSV (with headers)
• JSONL (one JSON object per line)

Output

Analysis results include:

• Discovered patterns with confidence scores
• Association rules with support metrics
• Anomaly flags for unusual items
• Exportable reports (JSON, CSV)

Privacy & Security

✅ Your data stays on your device

• No external API calls
• No data uploaded to cloud services
• No access to system files or shell history
• Only reads files you explicitly specify

Technical Information

| Attribute | Value | |-----------|-------| | Skill ID | pattern-miner | | Version | 2.0.1 | | Author | harrylabsj | | License | MIT-0 |

Requirements

• Python 3.8+
• numpy, scikit-learn, pandas (for analysis)

Limitations

• Only analyzes files you explicitly provide
• Does not access system logs or shell history
• Does not read OpenClaw session data
• Requires structured data files (JSON/CSV)