Python Resource Management

SkillFiles & storage

This skill helps an AI agent write Python code that safely manages files, connections, and cleanup. It covers context managers, cleanup patterns, and streaming, so the agent can handle resources without leaks or half-finished work. Use it when you need code that opens and closes things reliably, or that builds streaming responses while keeping accumulated state in order.

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Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Have a Python project where the agent writes or edits code.

Python Resource ManagementStart free

What your AI can do with it

  • Write context managers for files, sockets, and database connections
  • Add cleanup logic that runs even when errors interrupt a block
  • Build streaming responses that accumulate state across chunks
  • Replace manual open and close calls with with statements
  • Handle nested resources so each one is released in the right order

Getting started

  1. Have a Python project where the agent writes or edits code.
  2. Add the skill to your agent's available skills or configuration.
  3. Ask the agent to manage a file, connection, or stream in a task.
  4. Review the generated code for context managers and cleanup paths.

What this skill tells your AI

The instructions your AI receives, as published by wshobson/agents in plugins/python-development/skills/python-resource-management/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars
40k
Forks
4k
Last commit
Sep 2026

Questions

What does this skill do for my agent?
It gives the agent patterns for context managers, cleanup, and streaming so it can write Python that opens and closes resources safely.
When should I use this skill?
Use it when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.
Does it cover async context managers?
The skill covers context managers, cleanup patterns, and streaming. It does not state support for async context managers.
Can it help with database connection pools?
It helps with managing connections and cleanup logic, which applies to connection pools, but it does not list pool-specific features.
Advanced
Item type
skill
Key
python-resource-management
Source
github.com/wshobson/agents