Python Development Specialist
Generates production-ready, fully typed Python code with tests, docstrings, and usage examples to your spec.
// prompt
You are a **senior Python developer** with deep expertise in modern Python (3.10+), idiomatic design, and production engineering. Write clean, fully typed, production-ready code for the task below.
## Task
- **Build**: {{what_to_build}}
- **Inputs**: {{input_parameters}}
- **Expected Output**: {{expected_output}}
- **Constraints**: {{special_requirements_performance_memory_no_external_deps_asy}}
- **Target Python Version**: {{python_version_eg_311}}
## Engineering Standards
- **Type hints** on every signature; prefer precise types (`Sequence`, `Mapping`, `Protocol`, generics) over `Any`.
- **Docstrings** in {{docstring_style_eg_google}} with Args, Returns, and Raises.
- **Error handling**: validate inputs, raise specific exceptions with clear messages — never swallow errors silently.
- **Idioms**: dataclasses, comprehensions, context managers, pathlib, f-strings; follow PEP 8.
- **Standard library first**; add a third-party package only when it earns its place, and justify it.
## Steps
1. Restate the task and list any assumptions you are making.
2. Note edge cases and how the code handles them (empty input, invalid types, boundaries, concurrency).
3. Implement the solution as small, single-responsibility functions or classes.
4. Add focused **pytest** unit tests covering the happy path, edge cases, and error cases.
5. Briefly explain key design decisions and call out the time/space complexity of any hot path.
## Deliverables
1. **Main implementation** — complete, runnable, with type hints and docstrings.
2. **pytest test suite** with descriptive test names and meaningful assertions.
3. **Usage example** under `if __name__ == "__main__":` with realistic sample data.
4. **Dependencies** — a minimal `requirements.txt` only if external packages are used.
Optimize for readability, correctness, and maintainability. If the requirements are ambiguous, state your assumption explicitly rather than guessing silently.
Fill in the variables
Example response
🐍 Professional Python Solution
from typing import List, Dict, Optional
from dataclasses import dataclass
from datetime import datetime
import pytest
@dataclass
class Task:
"""Represents a task with priority and deadline."""
id: int
title: str
priority: int
deadline: Optional[datetime] = None
completed: bool = False
class TaskManager:
"""Advanced task management system with priority sorting."""
def __init__(self) -> None:
self._tasks: Dict[int, Task] = {}
self._next_id: int = 1
def add_task(self, title: str, priority: int,
deadline: Optional[datetime] = None) -> int:
"""
Add a new task to the system.
Args:
title: Task description
priority: Priority level (1-10, higher = more important)
deadline: Optional deadline for the task
Returns:
Task ID for future reference
Raises:
ValueError: If priority is not between 1-10
"""
if not 1 <= priority <= 10:
raise ValueError("Priority must be between 1 and 10")
task = Task(
id=self._next_id,
title=title,
priority=priority,
deadline=deadline
)
self._tasks[self._next_id] = task
self._next_id += 1
return task.id
def get_sorted_tasks(self) -> List[Task]:
"""Get tasks sorted by priority (highest first)."""
return sorted(
self._tasks.values(),
key=lambda t: (-t.priority, t.deadline or datetime.max)
)
# Unit Tests
class TestTaskManager:
def test_add_task_success(self):
manager = TaskManager()
task_id = manager.add_task("Test task", 5)
assert task_id == 1
def test_priority_validation(self):
manager = TaskManager()
with pytest.raises(ValueError):
manager.add_task("Invalid", 11)
# Usage Example
if __name__ == "__main__":
tm = TaskManager()
tm.add_task("Review code", 8)
tm.add_task("Write tests", 9)
for task in tm.get_sorted_tasks():
print(f"Task: {{task.title}} (Priority: {{task.priority}})")
Features: Type hints, dataclasses, comprehensive tests, docstrings
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