warp-task-master
Version:
BETA: Experimental Task Master fork with Warp AI integration and human-readable profile names. For production use, see task-master-ai.
97 lines (74 loc) • 2.14 kB
Markdown
Enhanced auto-implementation with intelligent code generation and testing.
Arguments: $ARGUMENTS
## Intelligent Auto-Implementation
Advanced implementation with context awareness and quality checks.
### 1. **Pre-Implementation Analysis**
Before starting:
- Analyze task complexity and requirements
- Check codebase patterns and conventions
- Identify similar completed tasks
- Assess test coverage needs
- Detect potential risks
### 2. **Smart Implementation Strategy**
Based on task type and context:
**Feature Tasks**
1. Research existing patterns
2. Design component architecture
3. Implement with tests
4. Integrate with system
5. Update documentation
**Bug Fix Tasks**
1. Reproduce issue
2. Identify root cause
3. Implement minimal fix
4. Add regression tests
5. Verify side effects
**Refactoring Tasks**
1. Analyze current structure
2. Plan incremental changes
3. Maintain test coverage
4. Refactor step-by-step
5. Verify behavior unchanged
### 3. **Code Intelligence**
**Pattern Recognition**
- Learn from existing code
- Follow team conventions
- Use preferred libraries
- Match style guidelines
**Test-Driven Approach**
- Write tests first when possible
- Ensure comprehensive coverage
- Include edge cases
- Performance considerations
### 4. **Progressive Implementation**
Step-by-step with validation:
```
Step 1/5: Setting up component structure ✓
Step 2/5: Implementing core logic ✓
Step 3/5: Adding error handling ⚡ (in progress)
Step 4/5: Writing tests ⏳
Step 5/5: Integration testing ⏳
Current: Adding try-catch blocks and validation...
```
### 5. **Quality Assurance**
Automated checks:
- Linting and formatting
- Test execution
- Type checking
- Dependency validation
- Performance analysis
### 6. **Smart Recovery**
If issues arise:
- Diagnostic analysis
- Suggestion generation
- Fallback strategies
- Manual intervention points
- Learning from failures
### 7. **Post-Implementation**
After completion:
- Generate PR description
- Update documentation
- Log lessons learned
- Suggest follow-up tasks
- Update task relationships
Result: High-quality, production-ready implementations.