
python-resource-management
Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/full-stack-skills/python-skills --skill python-resource-management- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads python-resource-management automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
来源声明:本技能内容源自 wshobson/agents (MIT License, Copyright (c) 2024 Seth Hobson;完整许可文本见同目录
LICENSE.txt)。 使用、修改与再分发须遵守该 MIT 许可条款并保留版权声明。
中文描述
本技能提供全栈开发相关的最佳实践和模式指导。
name: python-resource-management description: Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.
Python Resource Management
Manage resources deterministically using context managers. Resources like database connections, file handles, and network sockets should be released reliably, even when exceptions occur.
When to Use This Skill
- Managing database connections and connection pools
- Working with file handles and I/O
- Implementing custom context managers
- Building streaming responses with state
- Handling nested resource cleanup
- Creating async context managers
Core Concepts
1. Context Managers
The with statement ensures resources are released automatically, even on exceptions.
2. Protocol Methods
__enter__/__exit__ for sync, __aenter__/__aexit__ for async resource management.
3. Unconditional Cleanup
__exit__ always runs, regardless of whether an exception occurred.
4. Exception Handling
Return True from __exit__ to suppress exceptions, False to propagate them.
Quick Start
from contextlib import contextmanager
@contextmanager
def managed_resource():
resource = acquire_resource()
try:
yield resource
finally:
resource.cleanup()
with managed_resource() as r:
r.do_work()
Fundamental Patterns
Pattern 1: Class-Based Context Manager
Implement the context manager protocol for complex resources.
class DatabaseConnection:
"""Database connection with automatic cleanup."""
def __init__(self, dsn: str) -> None:
self._dsn = dsn
self._conn: Connection | None = None
def connect(self) -> None:
"""Establish database connection."""
self._conn = psycopg.connect(self._dsn)
def close(self) -> None:
"""Close connection if open."""
if self._conn is not None:
self._conn.close()
self._conn = None
def __enter__(self) -> "DatabaseConnection":
"""Enter context: connect and return self."""
self.connect()
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_val: BaseException | None,
exc_tb: TracebackType | None,
) -> None:
"""Exit context: always close connection."""
self.close()
# Usage with context manager (preferred)
with DatabaseConnection(dsn) as db:
result = db.execute(query)
# Manual management when needed
db = DatabaseConnection(dsn)
db.connect()
try:
result = db.execute(query)
finally:
db.close()
Pattern 2: Async Context Manager
For async resources, implement the async protocol.
class AsyncDatabasePool:
"""Async database connection pool."""
def __init__(self, dsn: str, min_size: int = 1, max_size: int = 10) -> None:
self._dsn = dsn
self._min_size = min_size
self._max_size = max_size
self._pool: asyncpg.Pool | None = None
async def __aenter__(self) -> "AsyncDatabasePool":
"""Create connection pool."""
self._pool = await asyncpg.create_pool(
self._dsn,
min_size=self._min_size,
max_size=self._max_size,
)
return self
async def __aexit__(
self,
exc_type: type[BaseException] | None,
exc_val: BaseException | None,
exc_tb: TracebackType | None,
) -> None:
"""Close all connections in pool."""
if self._pool is not None:
await self._pool.close()
async def execute(self, query: str, *args) -> list[dict]:
"""Execute query using pooled connection."""
async with self._pool.acquire() as conn:
return await conn.fetch(query, *args)
# Usage
async with AsyncDatabasePool(dsn) as pool:
users = await pool.execute("SELECT * FROM users WHERE active = $1", True)
Pattern 3: Using @contextmanager Decorator
Simplify context managers with the decorator for straightforward cases.
from contextlib import contextmanager, asynccontextmanager
import time
import structlog
logger = structlog.get_logger()
@contextmanager
def timed_block(name: str):
"""Time a block of code."""
start = time.perf_counter()
try:
yield
finally:
elapsed = time.perf_counter() - start
logger.info(f"{name} completed", duration_seconds=round(elapsed, 3))
# Usage
with timed_block("data_processing"):
process_large_dataset()
@asynccontextmanager
async def database_transaction(conn: AsyncConnection):
"""Manage database transaction."""
await conn.execute("BEGIN")
try:
yield conn
await conn.execute("COMMIT")
except Exception:
await conn.execute("ROLLBACK")
raise
# Usage
async with database_transaction(conn) as tx:
await tx.execute("INSERT INTO users ...")
await tx.execute("INSERT INTO audit_log ...")
Pattern 4: Unconditional Resource Release
Always clean up resources in __exit__, regardless of exceptions.
class FileProcessor:
"""Process file with guaranteed cleanup."""
def __init__(self, path: str) -> None:
self._path = path
self._file: IO | None = None
self._temp_files: list[Path] = []
def __enter__(self) -> "FileProcessor":
self._file = open(self._path, "r")
return self
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_val: BaseException | None,
exc_tb: TracebackType | None,
) -> None:
"""Clean up all resources unconditionally."""
# Close main file
if self._file is not None:
self._file.close()
# Clean up any temporary files
for temp_file in self._temp_files:
try:
temp_file.unlink()
except OSError:
pass # Best effort cleanup
# Return None/False to propagate any exception
Detailed worked examples and patterns
Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.
Best Practices Summary
- Always use context managers - For any resource that needs cleanup
- Clean up unconditionally -
__exit__runs even on exception - Don't suppress unexpectedly - Return
Falseunless suppression is intentional - Use @contextmanager - For simple resource patterns
- Implement both protocols - Support
withand manual management - Use ExitStack - For dynamic numbers of resources
- Accumulate efficiently - List + join, not string concatenation
- Track metrics - Time-to-first-byte matters for streaming
- Document behavior - Especially exception suppression
- Test cleanup paths - Verify resources are released on errors
能力边界
✅ 擅长处理
- 全栈开发相关的最佳实践
- 代码架构设计指导
- 性能优化建议
⚠️ 需要素材
- 具体的项目上下文
- 技术栈信息
❌ 超出范围
- 非技术领域的业务决策
- 具体的代码实现(仅提供指导)
工作流程
Step 1: 需求分析
理解项目需求和技术约束
Step 2: 架构设计
设计系统架构和组件划分
Step 3: 实现指导
提供具体的实现建议和代码模式
Step 4: 代码审查
审查代码质量和最佳实践遵循情况
Step 5: 优化建议
提供性能优化和改进建议
Gotchas
1. 过度设计
避免在简单场景中使用复杂的设计模式
2. 性能忽视
不要忽视性能优化,特别是在生产环境中
3. 安全考虑
始终考虑安全性和数据保护
4. 测试覆盖
确保有足够的测试覆盖,特别是边界情况
5. 文档维护
保持文档与代码同步更新
FAQ
What does the python-resource-management skill do?
Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.
How do I install python-resource-management?
Run npx skills add https://github.com/full-stack-skills/python-skills --skill python-resource-management in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-resource-management/ (personal) or .claude/skills/python-resource-management/ (project).
Which AI agents can use python-resource-management?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is python-resource-management safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains python-resource-management?
It is published by full-stack-skills in full-stack-skills/python-skills. It has 88 recorded installs.
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