pattern-learning

What this skill does

## Overview

This skill provides the framework for autonomous pattern learning and recognition at the project level. It enables Claude agents to:
- Automatically detect and store successful task execution patterns
- Build a knowledge base of project-specific approaches
- Recommend skills and strategies based on historical success
- Continuously improve through self-assessment and adaptation

## Pattern Recognition System

### Automatic Pattern Detection

**Task Categorization**:
Automatically classify tasks into categories:
- `refactoring`: Code restructuring and improvement
- `bug-fix`: Error resolution and debugging
- `feature`: New functionality implementation
- `optimization`: Performance improvements
- `documentation`: Docs creation and updates
- `testing`: Test suite development
- `security`: Security analysis and fixes

**Context Extraction**:
Automatically extract context from:
- Programming languages used (file extensions)
- Frameworks detected (package.json, requirements.txt, etc.)
- Project structure patterns (MVC, microservices, etc.)
- Complexity indicators (file count, LOC, dependencies)

### Pattern Storage Structure

**Directory Setup**:
```
.claude-patterns/
├── patterns.json                   # Main pattern database
├── skill-effectiveness.json       # Skill performance metrics
└── task-history.json              # Complete task execution log
```

**Pattern Data Model**:
```json
{
  "version": "1.0.0",
  "project_context": {
    "detected_languages": ["python", "javascript"],
    "frameworks": ["flask", "react"],
    "project_type": "web-application"
  },
  "patterns": [
    {
      "id": "pattern-001",
      "timestamp": "2025-10-20T10:30:00Z",
      "task_type": "refactoring",
      "task_description": "Refactor authentication module",
      "context": {
        "language": "python",
        "framework": "flask",
        "module": "authentication",
        "complexity": "medium"
      },
      "execution": {
        "skills_used": ["code-analysis", "quality-standards"],
        "agents_delegated": ["code-analyzer", "quality-controller"],
        "approach": "Extract method refactoring with pattern matching",
        "duration_seconds": 120
      },
      "outcome": {
        "success": true,
        "quality_score": 96,
        "tests_passing": true,
        "standards_compliance": 98,
        "documentation_complete": true
      },
      "lessons_learned": "Security-critical modules benefit from quality-controller validation",
      "reuse_count": 5
    }
  ],
  "skill_effectiveness": {
    "code-analysis": {
      "total_uses": 45,
      "successful_uses": 42,
      "success_rate": 0.93,
      "avg_quality_contribution": 15,
      "recommended_for": ["refactoring", "bug-fix", "optimization"]
    },
    "testing-strategies": {
      "total_uses": 30,
      "successful_uses": 27,
      "success_rate": 0.90,
      "avg_quality_contribution": 20,
      "recommended_for": ["testing", "feature", "bug-fix"]
    }
  },
  "agent_effectiveness": {
    "code-analyzer": {
      "total_delegations": 38,
      "successful_completions": 36,
      "success_rate": 0.95,
      "avg_execution_time": 85
    }
  }
}
```

## Skill Auto-Selection Algorithm

### Decision Process

**Step 1: Analyze Current Task**
```
Input: Task description
Output: Task type, context, complexity

Process:
1. Extract keywords and intent
2. Scan project files for context
3. Classify task type
4. Determine complexity level (low/medium/high)
```

**Step 2: Query Pattern Database**
```
Input: Task type, context
Output: Recommended skills, agents, approach

Process:
1. Load patterns.json
2. Filter patterns by task_type match
3. Filter patterns by context similarity
4. Rank by success_rate * reuse_count
5. Extract top 3 most successful patterns
```

**Step 3: Skill Selection**
```
Input: Top patterns, skill effectiveness data
Output: Ordered list of skills to load

Process:
1. Aggregate skills from top patterns
2. Weight by skill effectiveness scores
3. Filter by task type recommendation
4. Return ordered list (highest effectiveness first)
```

### Selection Examples

**Example 1: Refactoring Task**
```
Task: "Refactor user authentication module"

Analysis:
- Type: refactoring
- Context: authentication (security-critical)
- Language: Python (detected)
- Complexity: medium

Pattern Query Results:
- Pattern-001: refactoring + auth → success_rate: 0.96
- Pattern-015: refactoring + security → success_rate: 0.94
- Pattern-023: refactoring + Python → success_rate: 0.91

Skill Selection:
1. code-analysis (appeared in all 3 patterns, avg effectiveness: 0.93)
2. quality-standards (appeared in 2/3 patterns, avg effectiveness: 0.88)
3. pattern-learning (for continuous improvement)

Auto-Load: code-analysis, quality-standards, pattern-learning
```

**Example 2: Testing Task**
```
Task: "Add unit tests for payment processing"

Analysis:
- Type: testing
- Context: payment (critical business logic)
- Language: JavaScript (detected)
- Complexity: high

Pattern Query Results:
- Pattern-042: testing + payment → success_rate: 0.89
- Pattern-051: testing + JavaScript → success_rate: 0.92

Skill Selection:
1. testing-strategies (effectiveness: 0.90)
2. quality-standards (for test quality)
3. pattern-learning (for continuous improvement)

Auto-Load: testing-strategies, quality-standards, pattern-learning
```

## Pattern Storage Workflow

### Automatic Storage Process

**During Task Execution**:
1. Monitor task progress and decisions
2. Record skills loaded and agents delegated
3. Track execution metrics (time, resources)
4. Capture approach and methodology

**After Task Completion**:
1. Run quality assessment
2. Calculate quality score
3. Determine success/failure
4. Extract lessons learned
5. Store pattern to database
6. Update skill effectiveness metrics
7. Update agent effectiveness metrics

### Storage Implementation

**Auto-Create Pattern Directory - WITH SAFETY VALIDATION**:
```javascript
// 🚨 CRITICAL: Always validate content before applying cache_control
function safeExecuteOperation(operation, fallbackContent) {
  try {
    const result = operation();
    // Validate result before using
    if (result !== null && result !== undefined && String(result).trim().length > 0) {
      return result;
    }
  } catch (error) {
    console.log("Operation failed, using fallback");
  }
  // Always return meaningful fallback
  return fallbackContent || "Pattern initialization in progress...";
}

// Executed automatically by orchestrator with safety checks
const dirExists = safeExecuteOperation(() => exists('.claude-patterns/'), false);
if (!dirExists) {
  safeExecuteOperation(() => create_directory('.claude-patterns/'));
  safeExecuteOperation(() => create_file('.claude-patterns/patterns.json', '{"version":"1.0.0","patterns":[]}'));
  safeExecuteOperation(() => create_file('.claude-patterns/skill-effectiveness.json', '{}'));
  safeExecuteOperation(() => create_file('.claude-patterns/task-history.json', '[]'));
}
```

**Store New Pattern - WITH COMPREHENSIVE SAFETY**:
```javascript
// 🚨 CRITICAL: Safe pattern storage with full validation
function store_pattern(task_data, execution_data, outcome_data) {
  // Validate inputs first
  if (!task_data || !execution_data || !outcome_data) {
    console.log("Invalid pattern data, skipping storage");
    return "Pattern data incomplete - storage skipped";
  }

  try {
    const pattern = {
      id: generate_id() || `pattern_${Date.now()}`,
      timestamp: now() || new Date().toISOString(),
      task_type: task_data.type || "unknown",
      task_description: task_data.description || "Task completed",
      context: extract_context(task_data) || {},
      execution: execution_data,
      outcome: outcome_data,
      lessons_learned: analyze_lessons(execution_data, outcome_data) || "Task completed successfully",
      reuse_count: 0
    }

    // Load existing patterns safely
    const db = safeLoadPatterns('.claude-patterns/patterns.json');
    if (!db) {