⚙️ Web Worker Event Processor

About This Tool

Purpose

Process large volumes of Claude Code hook events in the background using Web Workers. Analyze patterns, detect errors, compare agent behavior, and identify anomalies without blocking the UI.

Features

• Background Processing: Uses Web Workers to analyze thousands of events without freezing the UI
• Pattern Detection: Identifies common tool usage sequences and workflows
• Error Correlation: Finds tools that frequently fail together
• Session Analysis: Calculates productivity metrics and session duration
• Agent Comparison: Compares behavior across multiple agents/sessions
• Anomaly Detection: Identifies unusual patterns and outliers
• Real-time Progress: Shows analysis progress with percentage updates
• Sample Data Included: Test with realistic hook event scenarios

Web Research Integration

Source: MDN Web Workers API Documentation

Techniques Applied:

• Worker Communication via postMessage: Main thread sends event data and receives analysis results through structured message passing
• Background Processing Pattern: Web Worker performs heavy computation (pattern matching, statistics) without blocking the UI thread
• Worker Lifecycle Management: Proper worker initialization, termination, and error handling for robust processing

Usage

1. Click "Load Sample Data" to test with example hook events, or upload your own JSON/JSONL file
2. Select an analysis type (Pattern Detection, Error Correlation, etc.)
3. Click "Start Analysis" to spawn a Web Worker and begin background processing
4. Watch the progress bar update in real-time as the worker processes events
5. Review results displayed in organized cards when analysis completes
6. Try different analysis types to explore various insights

Hook Event Structure

Events follow this structure:

{
  "source_app": "demo-agent",
  "session_id": "abc123",
  "hook_event_type": "PreToolUse|PostToolUse|UserPromptSubmit|Notification|Stop|SubagentStop",
  "payload": {
    "tool_name": "Bash",
    "tool_input": {...},
    "tool_output": {...}
  },
  "timestamp": 1696867200000
}

Analysis Types

• Pattern Detection: Finds common tool sequences (e.g., "Read → Edit → Bash")
• Error Correlation: Identifies tools that fail together frequently
• Session Analysis: Calculates duration, tool counts, and productivity metrics
• Agent Comparison: Compares tool usage across different source apps/sessions
• Anomaly Detection: Finds outliers in timing, error rates, or usage patterns
• Complete Analysis: Runs all analyses in parallel for comprehensive insights