autonomous-development

What this skill does

## Overview

The Autonomous Development skill provides comprehensive strategies, patterns, and best practices for managing full development lifecycles autonomously - from user requirements to production-ready implementation with minimal human intervention.

## When to Apply

Use Autonomous Development strategies when:
- Implementing features from high-level requirements
- Managing complex multi-phase development projects
- Need to maintain quality while developing autonomously
- Implementing with continuous testing and validation
- Debugging and fixing issues automatically
- Ensuring parameter consistency and type safety

## Milestone Planning Strategies

### Requirements Decomposition

**Pattern: Feature-to-Milestone Mapping**

```
User Requirement → Feature Breakdown → Milestone Plan

Example: "Add MQTT broker with certificate support"

Decomposition:
1. Dependencies & Configuration (Simple)
   - Install required libraries
   - Create configuration module
   - Time: 10-15 minutes

2. Core Functionality (Medium)
   - Implement main feature logic
   - Add error handling
   - Time: 20-30 minutes

3. Integration & Testing (Medium)
   - Write unit tests
   - Write integration tests
   - Time: 15-25 minutes

4. Documentation (Simple)
   - API documentation
   - Usage examples
   - Time: 10-15 minutes
```

**Complexity Assessment Matrix**

```
Simple Milestone:
├─ Single file modification
├─ Well-defined scope
├─ No external dependencies
├─ Existing patterns to follow
└─ Estimated: 10-20 minutes

Medium Milestone:
├─ Multiple file modifications
├─ Some external dependencies
├─ Integration with existing code
├─ Moderate complexity
└─ Estimated: 20-45 minutes

Complex Milestone:
├─ Multiple component changes
├─ New dependencies or frameworks
├─ Significant integration work
├─ Architectural considerations
└─ Estimated: 45-90 minutes

Expert Milestone:
├─ Major architectural changes
├─ Multiple system integrations
├─ Advanced algorithms or patterns
├─ Security-critical implementations
└─ Estimated: 90+ minutes
```

### Milestone Sequencing

**Pattern: Dependency-First Ordering**

```
Order milestones to minimize dependencies:

1. Foundation Layer
   - Dependencies
   - Configuration
   - Data models

2. Core Logic Layer
   - Business logic
   - Core algorithms
   - Main functionality

3. Integration Layer
   - API endpoints
   - External integrations
   - Service connections

4. Quality Layer
   - Testing
   - Documentation
   - Validation
```

## Incremental Development Patterns

### Commit-Per-Milestone Strategy

**Pattern: Working State Commits**

```
Each milestone must result in a working state:

✅ Good Milestone:
- Feature partially complete but functional
- All tests pass for implemented functionality
- No breaking changes to existing code
- Commit: "feat: add user authentication (phase 1/3)"

❌ Bad Milestone:
- Feature incomplete and non-functional
- Tests failing
- Breaking changes uncommitted
- Half-implemented logic
```

**Conventional Commit Format**

```
<type>(<scope>): <description>

[optional body]

[optional footer]

Types:
- feat: New feature
- fix: Bug fix
- refactor: Code refactoring
- test: Adding tests
- docs: Documentation
- chore: Maintenance
- perf: Performance improvement

Examples:
feat(mqtt): add broker connection with SSL
fix(auth): correct token validation logic
test(api): add integration tests for user endpoints
docs(readme): update installation instructions
```

### Progressive Enhancement Pattern

```
Start simple, enhance progressively:

Phase 1: Basic Implementation
├─ Core functionality only
├─ No error handling
├─ No optimization
└─ Purpose: Prove concept works

Phase 2: Error Handling
├─ Add try-catch blocks
├─ Add input validation
├─ Add logging
└─ Purpose: Make it robust

Phase 3: Optimization
├─ Performance improvements
├─ Memory optimization
├─ Caching if needed
└─ Purpose: Make it efficient

Phase 4: Polish
├─ Documentation
├─ Examples
├─ Edge case handling
└─ Purpose: Make it production-ready
```

## Auto-Debugging Strategies

### Error Classification System

```
Error Categories and Fix Strategies:

1. Syntax Errors (100% auto-fixable)
   - Missing colons, brackets, quotes
   - Indentation errors
   - Strategy: Parse and fix immediately

2. Import Errors (95% auto-fixable)
   - Missing imports
   - Incorrect module paths
   - Strategy: Auto-add imports, fix paths

3. Type Errors (90% auto-fixable)
   - Type mismatches
   - Type hint violations
   - Strategy: Add type conversions or fix hints

4. Name Errors (85% auto-fixable)
   - Undefined variables
   - Typos in names
   - Strategy: Fix typos or add definitions

5. Logic Errors (60% auto-fixable)
   - Wrong algorithm
   - Incorrect conditions
   - Strategy: Analyze and refactor logic

6. Integration Errors (70% auto-fixable)
   - Connection failures
   - API mismatches
   - Strategy: Add retry logic, fix endpoints

7. Performance Errors (40% auto-fixable)
   - Timeouts
   - Memory issues
   - Strategy: Optimize algorithms, add caching
```

### Debug Loop Pattern

```
Maximum 5 iterations per issue:

Iteration 1: Quick Fix (confidence > 90%)
├─ Fix obvious issues (typos, imports)
├─ Success rate: 70%
└─ Time: 30 seconds

Iteration 2: Pattern-Based Fix (confidence 70-90%)
├─ Apply known successful patterns
├─ Success rate: 50%
└─ Time: 1-2 minutes

Iteration 3: Analysis-Based Fix (confidence 50-70%)
├─ Deep error analysis
├─ Root cause investigation
├─ Success rate: 30%
└─ Time: 3-5 minutes

Iteration 4: Alternative Approach (confidence 30-50%)
├─ Try different implementation
├─ Success rate: 20%
└─ Time: 5-10 minutes

Iteration 5: Last Attempt (confidence < 30%)
├─ Aggressive fixes
├─ Success rate: 10%
└─ Time: 10-15 minutes

If all iterations fail → Manual intervention required
```

### Common Fix Patterns

**Connection Retry Pattern**

```python
# Problem: Connection refused
# Fix: Add exponential backoff retry

import time
from functools import wraps

def with_retry(max_attempts=3, backoff_factor=2):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_attempts):
                try:
                    return func(*args, **kwargs)
                except ConnectionError as e:
                    if attempt == max_attempts - 1:
                        raise
                    delay = backoff_factor ** attempt
                    time.sleep(delay)
            return None
        return wrapper
    return decorator

@with_retry(max_attempts=3)
def connect_to_service():
    # Connection logic
    pass
```

**Type Conversion Pattern**

```python
# Problem: Type mismatch (str vs int)
# Fix: Add safe type conversion

def safe_int(value, default=0):
    try:
        return int(value)
    except (ValueError, TypeError):
        return default

# Usage
user_id = safe_int(request.params.get('user_id'))
```

**Null Safety Pattern**

```python
# Problem: NoneType attribute error
# Fix: Add null checks

# Bad
result = data.get('user').get('name')

# Good
result = data.get('user', {}).get('name', 'Unknown')

# Better
user = data.get('user')
result = user.get('name', 'Unknown') if user else 'Unknown'
```

**Parameter Validation Pattern**

```python
# Problem: Invalid parameters
# Fix: Add validation decorator

from functools import wraps
from typing import get_type_hints

def validate_params(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        hints = get_type_hints(func)
        for param_name, param_type in hints.items():
            if param_name in kwargs:
                value = kwargs[param_name]
                if not isinstance(value, param_type):
                    raise TypeError(
                        f"{param_name} must be {param_type}, "
                        f"got {type(value)}"
                    )
        return func(*args, **kwargs)
    return wrapper

@validate_params
def create_user(name: str, age: int) -> dict:
    return {'name': name, 'age': age}
```

## Parameter C