intent-drift

A skill that uses the Intent Alignment Engine to analyze intent drift in AI-assisted development.

🎯 Purpose

Detects when AI coding agents begin solving a different problem than originally requested by monitoring alignment between:

• Original goals
• Current execution plans
• File changes and behavior

🛠️ Features

• Evidence-based drift detection using multiple providers
• Explainable assessments with detailed evidence tracking
• Real-time monitoring and timeline tracking
• Pluggable architecture for custom evidence providers
• Type-safe with comprehensive validation
• Exportable reports in multiple formats

🚀 Quick Start

# Import into any Claude Code agent
cd ~/.claude/skills/intent-drift
./analyze-code

Usage Examples

# Basic usage
/intent-drift
--original-goal "Reduce application memory usage"
--current-plan "Optimize startup performance"
--context "Edited: main.py, startup.py"

# With auto-collection of git context
/intent-drift
--original-goal "Improve response time"
--current-plan "Add database indexing"
--auto-context

📊 Analysis Output

Intent Alignment Report

Overall Alignment: 68%
Status: Moderate Drift
Confidence: 89%

Evidence:
✓ Goal partially overlaps
✓ Constraints remain satisfied
⚠ Edited files primarily affect startup logic
⚠ Implementation no longer targets memory allocation

Risk: High - additional work unlikely to improve memory usage

Recommendation: Pause and confirm alignment before continuing

🏗️ Architecture

intent-drift/
├── __init__.py              # Skill entrypoint
├── analyzer.py              # Core analysis logic
├── providers/               # Evidence providers
├── exporters/               # Report exporters (text, markdown, json)
├── config/                  # Configuration defaults
├── docs/                    # Usage documentation
└── examples/                # Usage examples

🔧 Configuration

Required Configuration

# config/defaults.yaml
analysis:
  threshold: 75              # Minimum alignment score (%)
  confidence: 80            # Minimum confidence (%)
  providers:
    enabled:               # Which providers to use
      - goal_provider
      - constraint_provider
      - execution_provider
      - scope_provider
  
  evidence_providers:
    goal_provider:
      weight: 0.25
      thresholds:
        match_score: 80
        drift_score: 60
    
    constraint_provider:
      weight: 0.20
      thresholds:
        violation_score: 90
        partial_compliance: 70

Customization

# Edit config file
nano ~/.claude/skills/intent-drift/config/user.yaml

# Reset to defaults
./analyze-code --reset-config

📁 Integration

With Git Repos

Automatically analyzes:

• Git diffs between commit points
• File modification patterns
• Commit message trends
• Branch divergence

With Codebase Features

Analyzes:

• Type checking evidence
• Build system outputs
• Test coverage changes
• Performance metrics

🔌 Extending the Skill

Adding New Evidence Providers

# New providers go in providers/
class CustomEvidenceProvider:
    def __init__(self):
        self.name = "custom_provider"
        self.weight = 0.15
    
    def collect(self, context):
        # Implementation
        return [Evidence(...)]

Custom Export Formats

# New exporters go in exporters/
class CsvExporter:
    def export(self, report, output_path):
        # CSV implementation
        pass

📚 Documentation

See the docs/ directory for:

• Architecture Overview
• Evidence Providers
• Configuration Reference
• Customization Guide
• API Reference

🤝 Contributing

See CONTRIBUTING.md for:

• Code style guidelines
• Testing requirements
• Documentation standards

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Based on the Intent Alignment Engine by Shaurya Gangrade.