New to Claude Skills? Learn how to install them →

Pwshobson on GitHub

Python Resource Management

Free

Efficiently manage resources with context managers.

by wshobson38.7k stars on wshobson/agents
1 views
Updated Jul 18, 2026
Get this skill

Free · Opens the source repo

What Python Resource Management does

This skill provides a structured approach to managing resources in Python using context managers. It is particularly useful for developers who need to ensure that resources such as database connections, file handles, and network sockets are properly released, even in the event of exceptions. By utilizing context managers, you can simplify your code and make it more reliable, as the skill ensures that cleanup logic is executed automatically.

The core of this skill revolves around implementing the context manager protocol, which includes the __enter__ and __exit__ methods for synchronous management, and __aenter__ and __aexit__ for asynchronous scenarios. This allows you to create both synchronous and asynchronous context managers that handle resource acquisition and release seamlessly. Additionally, it emphasizes unconditional cleanup, ensuring that resources are released regardless of whether an exception occurs during execution.

This skill is ideal for developers working on applications that require robust resource management, such as web applications that maintain database connections or applications that handle file I/O. The patterns provided in this skill guide you through creating custom context managers for various use cases, including nested resource management and async context managers for handling asynchronous tasks effectively.

By adopting these patterns, you can enhance the reliability and maintainability of your code, reducing the risk of resource leaks and making your applications more resilient. This skill is a valuable addition for any developer looking to improve their resource management practices in Python.

When to use it

Use this skill when managing database connections, file handles, or when implementing custom cleanup logic in your applications.

When not to use it

This skill may not be suitable for simple scripts where resource management is not a concern or where manual management is sufficient.

What you can build with it

Database Connection Management

Use the skill to create a context manager that automatically opens and closes database connections, ensuring resources are released even if an error occurs.

File Handling

Implement context managers for file operations to guarantee that files are properly closed after processing, preventing resource leaks.

Asynchronous Resource Management

Utilize async context managers to manage resources in asynchronous applications, such as connection pools for web servers.

How to install Python Resource Management

View source

1. Install with the skills CLI

npx skills add wshobson/agents/python-resource-management --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by wshobson

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

  1. Always use context managers - For any resource that needs cleanup
  2. Clean up unconditionally - __exit__ runs even on exception
  3. Don't suppress unexpectedly - Return False unless suppression is intentional
  4. Use @contextmanager - For simple resource patterns
  5. Implement both protocols - Support with and manual management
  6. Use ExitStack - For dynamic numbers of resources
  7. Accumulate efficiently - List + join, not string concatenation
  8. Track metrics - Time-to-first-byte matters for streaming
  9. Document behavior - Especially exception suppression
  10. Test cleanup paths - Verify resources are released on errors

Frequently asked questions about Python Resource Management

Similar skills