contextual-pattern-learning

contextual-pattern-learning follows the SKILL.md standard. Use the install command to add it to your agent stack.

---
name: contextual-pattern-learning
description: Advanced contextual pattern recognition with project fingerprinting, semantic similarity analysis, and cross-domain pattern matching for enhanced learning capabilities
version: 1.0.0
---

## Contextual Pattern Learning Skill

Provides advanced pattern recognition capabilities that understand project context, compute semantic similarities, and identify transferable patterns across different codebases and domains.

## Core Capabilities

### Project Fingerprinting

**Multi-dimensional Project Analysis**:
- **Technology Stack Detection**: Languages, frameworks, libraries, build tools
- **Architectural Patterns**: MVC, microservices, monolith, serverless, etc.
- **Code Structure Analysis**: Module organization, dependency patterns, coupling metrics
- **Team Patterns**: Coding conventions, commit patterns, testing strategies
- **Domain Classification**: Business domain, problem space, user type

**Fingerprint Generation**:
```python
project_fingerprint = {
    "technology_hash": sha256(sorted(languages + frameworks + libraries)),
    "architecture_hash": sha256(architectural_patterns + structural_metrics),
    "domain_hash": sha256(business_domain + problem_characteristics),
    "team_hash": sha256(coding_conventions + workflow_patterns),
    "composite_hash": combine_all_hashes_with_weights()
}
```

### Context Similarity Analysis

**Multi-factor Similarity Calculation**:
1. **Technology Similarity (40%)**: Language/framework overlap
2. **Architectural Similarity (25%)**: Structure and design patterns
3. **Domain Similarity (20%)**: Business context and problem type
4. **Scale Similarity (10%)**: Project size and complexity
5. **Team Similarity (5%)**: Development practices and conventions

**Semantic Context Understanding**:
- **Intent Recognition**: What the code is trying to accomplish
- **Problem Space Analysis**: What category of problem being solved
- **Solution Pattern Matching**: How similar problems are typically solved
- **Contextual Constraints**: Performance, security, maintainability requirements

### Pattern Classification System

**Primary Classifications**:
- **Implementation Patterns**: Feature addition, API development, UI components
- **Refactoring Patterns**: Code cleanup, optimization, architectural changes
- **Debugging Patterns**: Bug fixing, issue resolution, problem diagnosis
- **Testing Patterns**: Test creation, coverage improvement, test maintenance
- **Integration Patterns**: Third-party services, databases, external APIs
- **Security Patterns**: Authentication, authorization, vulnerability fixes

**Secondary Attributes**:
- **Complexity Level**: Simple, moderate, complex, expert
- **Risk Level**: Low, medium, high, critical
- **Time Sensitivity**: Quick fix, planned work, research task
- **Collaboration Required**: Solo, pair, team, cross-team

### Cross-Domain Pattern Transfer

**Pattern Transferability Assessment**:
```python
def calculate_transferability(pattern, target_context):
    technology_match = calculate_tech_overlap(pattern.tech, target_context.tech)
    domain_similarity = calculate_domain_similarity(pattern.domain, target_context.domain)
    complexity_match = assess_complexity_compatibility(pattern.complexity, target_context.complexity)

    transferability = (
        technology_match * 0.4 +
        domain_similarity * 0.3 +
        complexity_match * 0.2 +
        pattern.success_rate * 0.1
    )

    return transferability
```

**Adaptation Strategies**:
- **Direct Transfer**: Pattern applies without modification
- **Technology Adaptation**: Same logic, different implementation
- **Architectural Adaptation**: Same approach, different structure
- **Conceptual Transfer**: High-level concept, complete reimplementation

## Pattern Matching Algorithm

### Context-Aware Similarity

**Weighted Similarity Scoring**:
```python
def calculate_contextual_similarity(source_pattern, target_context):
    # Technology alignment (40%)
    tech_score = calculate_technology_similarity(
        source_pattern.technologies,
        target_context.technologies
    )

    # Problem type alignment (30%)
    problem_score = calculate_problem_similarity(
        source_pattern.problem_type,
        target_context.problem_type
    )

    # Scale and complexity alignment (20%)
    scale_score = calculate_scale_similarity(
        source_pattern.scale_metrics,
        target_context.scale_metrics
    )

    # Domain relevance (10%)
    domain_score = calculate_domain_relevance(
        source_pattern.domain,
        target_context.domain
    )

    return (
        tech_score * 0.4 +
        problem_score * 0.3 +
        scale_score * 0.2 +
        domain_score * 0.1
    )
```

### Pattern Quality Assessment

**Multi-dimensional Quality Metrics**:
1. **Outcome Quality**: Final result quality score (0-100)
2. **Process Efficiency**: Time taken vs. expected time
3. **Error Rate**: Number and severity of errors encountered
4. **Reusability**: How easily the pattern can be applied elsewhere
5. **Adaptability**: How much modification was needed for reuse

**Quality Evolution Tracking**:
- **Initial Quality**: Quality when first captured
- **Evolved Quality**: Updated quality after multiple uses
- **Context Quality**: Quality in specific contexts
- **Time-based Quality**: How quality changes over time

## Learning Strategies

### Progressive Pattern Refinement

**1. Pattern Capture**:
```python
def capture_pattern(task_execution):
    pattern = {
        "id": generate_unique_id(),
        "timestamp": current_time(),
        "context": extract_rich_context(task_execution),
        "execution": extract_execution_details(task_execution),
        "outcome": extract_outcome_metrics(task_execution),
        "insights": extract_learning_insights(task_execution),
        "relationships": extract_pattern_relationships(task_execution)
    }

    return refine_pattern_with_learning(pattern)
```

**2. Pattern Validation**:
- **Immediate Validation**: Check pattern completeness and consistency
- **Cross-validation**: Compare with similar existing patterns
- **Predictive Validation**: Test pattern predictive power
- **Temporal Validation**: Monitor pattern performance over time

**3. Pattern Evolution**:
```python
def evolve_pattern(pattern_id, new_execution_data):
    existing_pattern = load_pattern(pattern_id)

    # Update success metrics
    update_success_rates(existing_pattern, new_execution_data)

    # Refine context understanding
    refine_context_similarity(existing_pattern, new_execution_data)

    # Update transferability scores
    update_transferability_assessment(existing_pattern, new_execution_data)

    # Generate new insights
    generate_new_insights(existing_pattern, new_execution_data)

    save_evolved_pattern(existing_pattern)
```

### Relationship Mapping

**Pattern Relationships**:
- **Sequential Patterns**: Patterns that often follow each other
- **Alternative Patterns**: Different approaches to similar problems
- **Prerequisite Patterns**: Patterns that enable other patterns
- **Composite Patterns**: Multiple patterns used together
- **Evolutionary Patterns**: Patterns that evolve into other patterns

**Relationship Discovery**:
```python
def discover_pattern_relationships(patterns):
    relationships = {}

    for pattern_a in patterns:
        for pattern_b in patterns:
            if pattern_a.id == pattern_b.id:
                continue

            # Sequential relationship
            if often_sequential(pattern_a, pattern_b):
                relationships[f"{pattern_a.id} -> {pattern_b.id}"] = {
                    "type": "sequential",
                    "confidence": calculate_sequential_confidence(pattern_a, pattern_b)
                }

            # Alternative relationship
            if are_alternatives(pattern_a, pattern_b):
                relationships[f"{pattern_a.id} <> {pattern_b.id}"] = {
                    "type": "alternative",
                    "confidence": calculate_alternative_confidence(pattern_a, pattern_b)
                }

    return relationships
```

## Context Extraction Techniques

### Static Analysis Context

**Code Structure Analysis**:
- **Module Organization**: How code is organized into modules/packages
- **Dependency Patterns**: How modules depend on each other
- **Interface Design**: How components communicate
- **Design Patterns**: GoF patterns, architectural patterns used
- **Code Complexity**: Cyclomatic complexity, cognitive complexity

**Technology Stack Analysis**:
```python
def extract_technology_context(project_root):
    technologies = {
        "languages": detect_languages(project_root),
        "frameworks": detect_frameworks(project_root),
        "databases": detect_databases(project_root),
        "build_tools": detect_build_tools(project_root),
        "testing_frameworks": detect_testing_frameworks(project_root),
        "deployment_tools": detect_deployment_tools(project_root)
    }

    return analyze_technology_relationships(technologies)
```

### Dynamic Context Analysis

**Runtime Behavior Patterns**:
- **Performance Characteristics**: Speed, memory usage, scalability
- **Error Patterns**: Common errors and their contexts
- **Usage Patterns**: How the code is typically used
- **Interaction Patterns**: How components interact at runtime

**Development Workflow Patterns**:
```python
def extract_workflow_context(git_history):
    return {
        "commit_patterns": analyze_commit_patterns(git_history),
        "branching_strategy": detect_branching_strategy(git_history),
        "release_patterns": analyze_release_patterns(git_history),
        "collaboration_patterns": analyze_collaboration(git_history),
        "code_review_patterns": analyze_review_patterns(git_history)
    }
```

### Semantic Context Analysis

**Domain Understanding**:
- **Business Domain**: E-commerce, finance, healthcare, education
- **Problem Category**: Data processing, user interface, authentication, reporting
- **User Type**: End-user, admin, developer, system
- **Performance Requirements**: Real-time, batch, high-throughput, low-latency

**Intent Recognition**:
```python
def extract_intent_context(task_description, code_changes):
    intent_indicators = {
        "security": detect_security_intent(task_description, code_changes),
        "performance": detect_performance_intent(task_description, code_changes),
        "usability": detect_usability_intent(task_description, code_changes),
        "maintainability": detect_maintainability_intent(task_description, code_changes),
        "functionality": detect_functionality_intent(task_description, code_changes)
    }

    return rank_intent_by_confidence(intent_indicators)
```

## Adaptation Learning

### Success Pattern Recognition

**What Makes Patterns Successful**:
1. **Context Alignment**: How well the pattern fits the context
2. **Execution Quality**: How well the pattern was executed
3. **Outcome Quality**: The quality of the final result
4. **Efficiency**: Time and resource usage
5. **Adaptability**: How easily the pattern can be modified

**Success Factor Analysis**:
```python
def analyze_success_factors(pattern):
    factors = {}

    # Context alignment
    factors["context_alignment"] = calculate_context_fit_score(pattern)

    # Execution quality
    factors["execution_quality"] = analyze_execution_process(pattern)

    # Team skill match
    factors["skill_alignment"] = analyze_team_skill_match(pattern)

    # Tooling support
    factors["tooling_support"] = analyze_tooling_effectiveness(pattern)

    # Environmental factors
    factors["environment_fit"] = analyze_environmental_fit(pattern)

    return rank_factors_by_importance(factors)
```

### Failure Pattern Learning

**Common Failure Modes**:
1. **Context Mismatch**: Pattern applied in wrong context
2. **Skill Gap**: Required skills not available
3. **Tooling Issues**: Required tools not available or not working
4. **Complexity Underestimation**: Pattern more complex than expected
5. **Dependency Issues**: Required dependencies not available

**Failure Prevention**:
```python
def predict_pattern_success(pattern, context):
    risk_factors = []

    # Check context alignment
    if calculate_context_similarity(pattern.context, context) < 0.6:
        risk_factors.append({
            "type": "context_mismatch",
            "severity": "high",
            "mitigation": "consider alternative patterns or adapt context"
        })

    # Check skill requirements
    required_skills = pattern.execution.skills_required
    available_skills = context.team_skills
    missing_skills = set(required_skills) - set(available_skills)
    if missing_skills:
        risk_factors.append({
            "type": "skill_gap",
            "severity": "medium",
            "mitigation": f"acquire skills: {', '.join(missing_skills)}"
        })

    return {
        "success_probability": calculate_success_probability(pattern, context),
        "risk_factors": risk_factors,
        "recommendations": generate_mitigation_recommendations(risk_factors)
    }
```

## Pattern Transfer Strategies

### Technology Adaptation

**Language-Agnostic Patterns**:
- **Algorithmic Patterns**: Logic independent of language syntax
- **Architectural Patterns**: Structure independent of implementation
- **Process Patterns**: Workflow independent of technology
- **Design Patterns**: Object-oriented design principles

**Technology-Specific Adaptation**:
```python
def adapt_pattern_to_technology(pattern, target_technology):
    adaptation_rules = load_adaptation_rules(pattern.source_technology, target_technology)

    adapted_pattern = {
        "original_pattern": pattern,
        "target_technology": target_technology,
        "adaptations": [],
        "confidence": 0.0
    }

    for rule in adaptation_rules:
        if rule.applicable(pattern):
            adaptation = rule.apply(pattern, target_technology)
            adapted_pattern.adaptations.append(adaptation)
            adapted_pattern.confidence += adaptation.confidence_boost

    return validate_adapted_pattern(adapted_pattern)
```

### Scale Adaptation

**Complexity Scaling**:
- **Pattern Simplification**: Reduce complexity for simpler contexts
- **Pattern Enhancement**: Add complexity for more demanding contexts
- **Pattern Modularity**: Break complex patterns into reusable components
- **Pattern Composition**: Combine simple patterns for complex solutions

**Scale Factor Analysis**:
```python
def adapt_pattern_for_scale(pattern, target_scale):
    current_scale = pattern.scale_context
    scale_factor = calculate_scale_factor(current_scale, target_scale)

    if scale_factor > 2.0:  # Need to scale up
        return enhance_pattern_for_scale(pattern, target_scale)
    elif scale_factor < 0.5:  # Need to scale down
        return simplify_pattern_for_scale(pattern, target_scale)
    else:  # Scale is compatible
        return pattern.with_scale_adjustments(target_scale)
```

## Continuous Improvement

### Learning Feedback Loops

**1. Immediate Feedback**:
- Pattern quality assessment
- Success/failure recording
- Context accuracy validation
- Prediction accuracy tracking

**2. Short-term Learning** (Daily/Weekly):
- Pattern performance trending
- Context similarity refinement
- Success factor correlation
- Failure pattern identification

**3. Long-term Learning** (Monthly):
- Cross-domain pattern transfer
- Technology evolution adaptation
- Team learning integration
- Best practice extraction

### Meta-Learning

**Learning About Learning**:
```python
def analyze_learning_effectiveness():
    learning_metrics = {
        "pattern_accuracy": measure_pattern_prediction_accuracy(),
        "context_comprehension": measure_context_understanding_quality(),
        "adaptation_success": measure_pattern_adaptation_success_rate(),
        "knowledge_transfer": measure_cross_project_knowledge_transfer(),
        "prediction_improvement": measure_prediction_accuracy_over_time()
    }

    return generate_learning_insights(learning_metrics)
```

**Adaptive Learning Strategies**:
- **Confidence Adjustment**: Adjust prediction confidence based on accuracy
- **Context Weighting**: Refine context importance weights
- **Pattern Selection**: Improve pattern selection algorithms
- **Feedback Integration**: Better integrate user feedback

## Usage Guidelines

### When to Apply This Skill

**Trigger Conditions**:
- Starting a new task in an unfamiliar codebase
- Need to understand project context quickly
- Looking for similar solutions in other projects
- Adapting patterns from one technology to another
- Estimating task complexity based on historical patterns

**Optimal Contexts**:
- Multi-language or multi-framework projects
- Large codebases with established patterns
- Teams working on multiple similar projects
- Projects requiring frequent adaptation of solutions
- Knowledge sharing across teams or organizations

### Expected Outcomes

**Primary Benefits**:
- **Faster Context Understanding**: Quickly grasp project structure and conventions
- **Better Pattern Matching**: Find more relevant solutions from past experience
- **Improved Adaptation**: More successful adaptation of patterns to new contexts
- **Cross-Project Learning**: Leverage knowledge from previous projects
- **Predictive Insights**: Better predictions of task complexity and success

**Quality Metrics**:
- **Context Similarity Accuracy**: >85% accurate context matching
- **Pattern Transfer Success**: >75% successful pattern adaptation
- **Prediction Accuracy**: >80% accurate outcome predictions
- **Learning Velocity**: Continuous improvement in pattern quality

## Integration with Other Skills

### Complementary Skills

**code-analysis**:
- Provides detailed code structure analysis for context extraction
- Helps identify design patterns and architectural decisions
- Contributes to technology stack detection

**quality-standards**:
- Provides quality metrics for pattern assessment
- Helps establish quality thresholds for pattern selection
- Contributes to best practice identification

**pattern-learning** (basic):
- Provides foundation pattern storage and retrieval
- Enhanced by contextual understanding and similarity analysis
- Benefits from advanced classification and relationship mapping

### Data Flow

```python
# Context extraction
context = code_analysis.extract_structure() + contextual_pattern_learning.extract_semantic_context()

# Pattern matching
matches = contextual_pattern_learning.find_similar_patterns(context, code_analysis.get_quality_metrics())

# Quality assessment
quality_score = quality_standards.assess_pattern_quality(matches)

# Learning integration
contextual_pattern_learning.capture_pattern_with_context(execution_data, context, quality_score)
```

This skill creates a comprehensive contextual understanding system that dramatically improves pattern matching, adaptation, and learning capabilities by considering the rich context in which patterns are created and applied.