Pattern Sweep - Compound Learning Acceleration Tool

Overview

The Pattern Sweep is a standalone automated pattern detection and learning acceleration system. It enables systematic discovery of code patterns, usage patterns, and coordination patterns across the entire codebase to accelerate development workflow optimization.

Note: As of August 18, 2025, Pattern Sweep has been decoupled from TLDR and operates as an independent tool.

Quick Start

# Simple runner (recommended)
./scripts/run_pattern_sweep.sh --verbose

# Direct execution with session log learning
./scripts/run_pattern_sweep.sh --learn-usage-patterns --verbose

# Advanced usage
PYTHONPATH=. python3 scripts/pattern_sweep.py --pattern-sweep-only --verbose

Pattern Categories

1. Code Patterns

• Async Patterns: async with session_factory(), context managers
• Repository Patterns: Repository instantiation and usage
• Test Patterns: Pytest markers, fixture usage
• Error Handling: Graceful degradation, try-except with logging
• Domain Patterns: Enum usage, workflow types

2. Usage Patterns

• Methodology Patterns: Systematic verification usage
• Success Patterns: Implementation completion markers
• Debugging Patterns: Root cause identification approaches

3. Performance Patterns

• Velocity Patterns: Rapid implementation timing detection
• Test Execution: TLDR timing analytics

4. Coordination Patterns

• Workflow Patterns: PM ticket completion tracking
• Agent Coordination: Multi-agent collaboration patterns

Results and Storage

Pattern data is stored in scripts/pattern_sweep_data.json with:

• Pattern Definitions: Description, category, confidence scoring
• Occurrence Data: File locations, frequency, examples
• History Tracking: Sweep timeline and pattern evolution
• Learning Data: New vs updated pattern detection

Standalone Operation

Pattern Sweep operates independently and can be run on-demand:

# Weekly pattern review (recommended)
./scripts/run_pattern_sweep.sh --learn-usage-patterns --verbose

# Quick pattern check
./scripts/run_pattern_sweep.sh

# Help and options
./scripts/run_pattern_sweep.sh --help

Performance

• Scan Scale: 1,000+ Python files + 200+ documentation files (after venv exclusion)
• Scan Duration: ~40 seconds for full codebase
• Pattern Detection: 9+ patterns across 4 categories
• Storage: ~500KB JSON with full pattern history

Weekly Pattern Review Process

Recommended workflow for compound learning acceleration:

1. Weekly Sweep: Run ./scripts/run_pattern_sweep.sh --learn-usage-patterns --verbose
2. Review Results: Analyze scripts/pattern_sweep_data.json for new patterns
3. Methodology Update: Incorporate high-confidence patterns into development practices
4. Documentation: Update process docs with discovered patterns

Compound Learning

Pattern Sweep enables compound learning acceleration by:

1. Automated Pattern Discovery: Identifies emerging patterns automatically
2. Confidence Scoring: Ranks patterns by frequency and distribution
3. Learning Persistence: Tracks pattern evolution over time
4. Methodology Enhancement: Enables systematic improvement of development practices

Examples

Top Detected Patterns (as of 2025-07-26)

1. Root cause identification pattern (314 occurrences, 0.83 confidence)
2. Async test marker pattern (220 occurrences, 1.00 confidence)
3. Systematic verification methodology (130 occurrences, 0.74 confidence)
4. Workflow type usage pattern (154 occurrences, 1.00 confidence)
5. Repository pattern instantiation (70 occurrences, 1.00 confidence)

Usage Pattern Examples

• Detection of “SYSTEMATIC VERIFICATION” methodology usage
• “✅ SUCCESS” implementation completion patterns
• “ROOT CAUSE” problem resolution patterns
• “PM-XXX COMPLETE” coordination patterns

Integration with Development Workflow

Pattern Sweep enhances the systematic verification methodology by:

• Pattern-First Development: Discover existing patterns before implementing
• Quality Assurance: Detect antipatterns and ensure consistency
• Velocity Intelligence: Learn from high-velocity implementation patterns
• Self-Improving Process: Enable methodology evolution through pattern analysis

Future Enhancements

Planned extensions include:

• Real-time Pattern Detection: Integration with file watchers
• Pattern Suggestions: AI-powered pattern recommendation
• Cross-Project Learning: Pattern sharing across repositories
• Performance Correlation: Link patterns to development velocity metrics