
autonomous-development
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SKILL.md
name: autonomous-development description: Comprehensive autonomous development strategies including milestone planning, incremental implementation, auto-debugging, and continuous quality assurance for full development lifecycle management version: 1.0.0
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 โ Feature Breakdown โ 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:
โโ Single file modification
โโ Well-defined scope
โโ No external dependencies
โโ Existing patterns to follow
โโ Estimated: 10-20 minutes
Medium Milestone:
โโ Multiple file modifications
โโ Some external dependencies
โโ Integration with existing code
โโ Moderate complexity
โโ Estimated: 20-45 minutes
Complex Milestone:
โโ Multiple component changes
โโ New dependencies or frameworks
โโ Significant integration work
โโ Architectural considerations
โโ Estimated: 45-90 minutes
Expert Milestone:
โโ Major architectural changes
โโ Multiple system integrations
โโ Advanced algorithms or patterns
โโ Security-critical implementations
โโ 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:
โ
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)"
โ 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
โโ Core functionality only
โโ No error handling
โโ No optimization
โโ Purpose: Prove concept works
Phase 2: Error Handling
โโ Add try-catch blocks
โโ Add input validation
โโ Add logging
โโ Purpose: Make it robust
Phase 3: Optimization
โโ Performance improvements
โโ Memory optimization
โโ Caching if needed
โโ Purpose: Make it efficient
Phase 4: Polish
โโ Documentation
โโ Examples
โโ Edge case handling
โโ 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%)
โโ Fix obvious issues (typos, imports)
โโ Success rate: 70%
โโ Time: 30 seconds
Iteration 2: Pattern-Based Fix (confidence 70-90%)
โโ Apply known successful patterns
โโ Success rate: 50%
โโ Time: 1-2 minutes
Iteration 3: Analysis-Based Fix (confidence 50-70%)
โโ Deep error analysis
โโ Root cause investigation
โโ Success rate: 30%
โโ Time: 3-5 minutes
Iteration 4: Alternative Approach (confidence 30-50%)
โโ Try different implementation
โโ Success rate: 20%
โโ Time: 5-10 minutes
Iteration 5: Last Attempt (confidence < 30%)
โโ Aggressive fixes
โโ Success rate: 10%
โโ Time: 10-15 minutes
If all iterations fail โ Manual intervention required
Common Fix Patterns
Connection Retry Pattern
# 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
# 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
# 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
# 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 Consistency Validation
Cross-File Parameter Validation
Critical validation checklist:
1. Function Signatures
โ Parameter names match between definition and calls
โ Parameter order consistent
โ Default values aligned
2. Configuration Files
โ Config keys match code usage
โ Environment variables consistent
โ No undefined config references
3. Type Consistency
โ Type hints present and correct
โ Return types specified
โ Type conversions explicit
4. API Contracts
โ Request parameters match backend expectations
โ Response structure consistent
โ Error codes standardized
5. Database Schemas
โ Column names match model attributes
โ Data types aligned
โ Foreign key constraints correct
Validation Automation Pattern
# Automated parameter validation
def validate_function_calls(codebase):
issues = []
# Extract all function definitions
definitions = extract_function_definitions(codebase)
# Extract all function calls
calls = extract_function_calls(codebase)
for call in calls:
definition = definitions.get(call.function_name)
if not definition:
issues.append({
'type': 'undefined_function',
'function': call.function_name,
'location': call.location
})
continue
# Check parameter count
if len(call.args) != len(definition.params):
issues.append({
'type': 'parameter_count_mismatch',
'function': call.function_name,
'expected': len(definition.params),
'actual': len(call.args)
})
# Check parameter names (for keyword args)
for arg_name in call.kwargs:
if arg_name not in definition.param_names:
issues.append({
'type': 'undefined_parameter',
'function': call.function_name,
'parameter': arg_name
})
return issues
Quality Assurance Patterns
Quality Score Calculation
Quality Score (0-100):
Code Quality (40 points):
โโ Syntax correctness (10)
โโ Style compliance (10)
โโ Code complexity (10)
โโ Best practices (10)
Test Quality (30 points):
โโ Test coverage (15)
โโ Test success rate (10)
โโ Test quality (5)
Documentation Quality (20 points):
โโ Docstrings (10)
โโ Comments (5)
โโ Examples (5)
Security Quality (10 points):
โโ No vulnerabilities (5)
โโ Secure patterns (5)
Thresholds:
โโ 85-100: Excellent (production-ready)
โโ 70-84: Good (acceptable)
โโ 50-69: Fair (needs improvement)
โโ 0-49: Poor (not acceptable)
Auto-Fix Priority System
Fix Priority Order:
Priority 1 (Always fix):
โโ Syntax errors
โโ Import errors
โโ Undefined variables
โโ Type errors (obvious)
โโ Success rate: 95%+
Priority 2 (Usually fix):
โโ Style violations
โโ Missing docstrings
โโ Unused imports
โโ Simple complexity issues
โโ Success rate: 80-95%
Priority 3 (Suggest fix):
โโ Complex refactoring
โโ Performance optimizations
โโ Architecture improvements
โโ Success rate: 60-80%
Priority 4 (Report only):
โโ Design decisions
โโ Major refactoring
โโ Architectural changes
โโ Requires human judgment
Testing Strategies for Autonomous Development
Test Generation Priorities
Test Priority Matrix:
Critical Path Tests (Must have):
โโ Core functionality tests
โโ Error handling tests
โโ Edge case tests
โโ Coverage target: 100%
Integration Tests (Should have):
โโ Component integration
โโ External service integration
โโ End-to-end workflows
โโ Coverage target: 80%
Performance Tests (Nice to have):
โโ Load tests
โโ Stress tests
โโ Benchmark tests
โโ Coverage target: 50%
Test-First Development Pattern
For autonomous development:
1. Generate Test Cases First
- Based on requirements
- Cover happy path and edge cases
- Include error scenarios
2. Implement to Pass Tests
- Write minimal code to pass
- Refactor after passing
- Maintain test coverage
3. Expand Tests as Needed
- Add tests for bugs found
- Add tests for edge cases discovered
- Keep tests up-to-date
Requirements Verification Patterns
Acceptance Criteria Validation
Verification Checklist Template:
Functional Requirements:
โโ [ ] Feature X implemented
โโ [ ] Feature Y working
โโ [ ] All specified behaviors present
โโ [ ] Edge cases handled
Non-Functional Requirements:
โโ [ ] Performance targets met
โโ [ ] Security requirements satisfied
โโ [ ] Scalability considered
โโ [ ] Maintainability ensured
Quality Requirements:
โโ [ ] Tests passing (100%)
โโ [ ] Code quality โฅ 85/100
โโ [ ] Documentation complete
โโ [ ] No critical issues
User Experience:
โโ [ ] Easy to use
โโ [ ] Clear error messages
โโ [ ] Good documentation
โโ [ ] Examples provided
Integration with Learning System
Pattern Storage for Development
{
"dev_pattern": {
"requirement_type": "mqtt_integration",
"complexity": "medium",
"successful_approach": {
"milestone_count": 5,
"milestone_sequence": [
"dependencies",
"core_logic",
"integration",
"testing",
"documentation"
],
"avg_milestone_time": 9.7,
"total_time": 48.5
},
"common_issues": [
{
"issue": "certificate_path_mismatch",
"frequency": 0.65,
"fix": "use_relative_paths",
"success_rate": 0.95
},
{
"issue": "connection_timeout",
"frequency": 0.45,
"fix": "add_retry_logic",
"success_rate": 0.88
}
],
"quality_metrics": {
"avg_code_quality": 92,
"avg_test_coverage": 91,
"avg_security_score": 94
},
"skill_effectiveness": {
"code-analysis": 0.94,
"testing-strategies": 0.91,
"security-patterns": 0.88
}
}
}
Best Practices
DO's
โ Break Down Complexity
- Decompose requirements into small, manageable milestones
- Each milestone should be independently testable
- Commit each working milestone
โ Validate Continuously
- Run tests after each change
- Check parameter consistency frequently
- Validate type safety throughout
โ Debug Systematically
- Start with high-confidence fixes
- Use pattern-based approaches
- Learn from failures
โ Document Progressively
- Document as you implement
- Keep documentation synchronized
- Include usage examples
โ Learn from Experience
- Store successful patterns
- Record failed approaches
- Optimize based on learnings
DON'Ts
โ Don't Skip Validation
- Never commit without tests passing
- Don't ignore parameter mismatches
- Don't skip quality checks
โ Don't Implement Everything at Once
- Avoid big-bang implementation
- Don't commit non-working code
- Don't skip incremental commits
โ Don't Ignore Patterns
- Don't repeat failed approaches
- Don't ignore learned patterns
- Don't make same mistakes twice
โ Don't Compromise Quality
- Don't accept quality score < 70
- Don't skip security validation
- Don't skip documentation
Advanced Patterns
Parallel Milestone Execution
When milestones are independent:
Sequential (slower):
Milestone 1 โ Milestone 2 โ Milestone 3
Total time: 30 minutes
Parallel (faster):
Milestone 1 โโ
Milestone 2 โโผโ Sync โ Milestone 4
Milestone 3 โโ
Total time: 12 minutes
Use parallel execution for:
- Independent components
- Test generation
- Documentation updates
- Multiple bug fixes
Adaptive Planning Pattern
Adjust plan based on execution:
Initial Plan:
โโ Milestone 1: 15 min (estimated)
โโ Milestone 2: 20 min (estimated)
โโ Milestone 3: 15 min (estimated)
โโ Total: 50 minutes
After Milestone 1 (took 25 min):
โโ Reason: Unexpected complexity
โโ Adjust remaining estimates: +10 min each
โโ New total: 70 minutes
โโ Re-evaluate approach if needed
The Autonomous Development skill provides comprehensive guidance for managing full development lifecycles with minimal human intervention, ensuring high quality and continuous improvement through learning.
Score
Total Score
Based on repository quality metrics
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