python-resource-management

Overview of python-resource-management skill

What the python-resource-management skill does

The python-resource-management skill helps an agent produce Python code that acquires, uses, and releases resources predictably. Its focus is not generic Python style. It is specifically about context managers, cleanup guarantees, exception-safe teardown, nested resource handling, async resource lifecycles, and streaming patterns that keep state under control.

Who should use it

This skill is best for backend engineers, API developers, and automation teams working with files, database connections, sockets, temporary resources, background streams, or any code path where leaking resources causes production issues. It is especially useful for python-resource-management for Backend Development when requests open connections, stream data, or need deterministic cleanup under failure.

The real job-to-be-done

Most users do not need a lecture on with. They need working code for one of these jobs:

• convert fragile try/finally cleanup into a safer context manager
• design a custom resource wrapper with __enter__ and __exit__
• build async resource handling with async with
• decide when to suppress exceptions versus re-raise them
• manage multiple resources cleanly without tangled teardown logic
• implement streaming responses that accumulate or release state correctly

What makes this skill different from a generic Python prompt

A normal prompt may generate Python that "works" in the happy path but misses teardown edge cases. The python-resource-management skill is stronger when the hard part is lifecycle correctness: cleanup on exceptions, ordering of release, async protocol methods, and contextlib-based patterns. That makes it more useful than a broad coding prompt when failures, partial writes, or long-lived handles matter.

What to know before you install

This skill is a compact guidance file, not a full package with helper scripts or examples spread across many files. That is good for fast adoption, but it also means output quality depends heavily on the prompt you give the agent. If you describe the resource type, lifecycle rules, sync or async mode, and failure behavior, the skill is much more likely to produce code you can ship.

How to Use python-resource-management skill

How to install the python-resource-management skill

Use the skill from the wshobson/agents repository:

npx skills add https://github.com/wshobson/agents --skill python-resource-management

After install, load it in your agent workflow the same way you load other repository skills. If your environment supports skill auto-selection, describe the task in terms of resource lifetime, cleanup, streaming, or context manager design so the agent can trigger python-resource-management naturally.

Read this file first

Start with:

• plugins/python-development/skills/python-resource-management/SKILL.md

There are no extra scripts, rules, or reference folders in this skill, so nearly all of the usable guidance is in that one file. Read it before assuming the skill covers retries, pooling strategy, or framework-specific integrations.

What input the skill needs to work well

For strong python-resource-management usage, provide:

• resource type: file, DB connection, socket, temporary directory, lock, stream
• sync or async execution model
• acquisition step
• required cleanup step
• whether cleanup must happen on every exception
• whether any exception should be suppressed
• whether resources are nested or dynamically created
• whether output is a reusable abstraction or one local code block

Weak input:

• "Make this cleaner."

Strong input:

• "Refactor this async FastAPI endpoint so an httpx.AsyncClient is opened once per request, closed even on cancellation, and the streaming response does not retain the whole payload in memory."

Turn a rough goal into a strong prompt

A good prompt for this skill usually has four parts:

1. current code or current pattern
2. lifecycle requirements
3. failure behavior
4. desired abstraction level

Example:

Use the python-resource-management skill.

I have Python code that opens a file, writes partial results, and also uses a DB session. Refactor it so:
- both resources are managed deterministically
- DB cleanup still happens if file writing fails
- exceptions are not suppressed
- the result should use standard library patterns where possible
- show the final code and explain why the cleanup order is safe

This is better than asking for "best practices" because it gives the agent a concrete resource graph and an exception policy.

Best use cases for python-resource-management for Backend Development

This skill is a strong fit when you need to:

• wrap DB sessions or transactions in a context manager
• ensure files and temp resources are always closed
• implement request-scoped clients
• create async cleanup around network calls
• stream data chunk by chunk without holding full state unnecessarily
• replace ad hoc teardown logic spread across multiple functions

It is less useful when the task is mainly ORM design, performance tuning, or framework routing with little resource-lifecycle complexity.

Core patterns the skill is designed to generate

Expect the skill to steer toward patterns such as:

• with and async with
• contextlib.contextmanager
• contextlib.asynccontextmanager
• class-based context managers with __enter__ / __exit__
• class-based async managers with __aenter__ / __aexit__
• explicit cleanup in finally
• careful exception propagation or suppression decisions

That focus is why the python-resource-management skill is more precise than a broad Python assistant for lifecycle-heavy code.

Practical workflow for using the skill

A reliable workflow is:

1. identify every resource acquired in the code path
2. note who owns each resource and when ownership ends
3. decide sync vs async protocol
4. state whether exceptions must propagate
5. ask the agent to refactor into one of the supported patterns
6. review cleanup order and cancellation behavior
7. test forced-failure paths, not just success paths

If you skip step 2, the agent may generate code that closes a shared resource too early or wraps the wrong scope.

Prompt examples that improve output quality

Use prompts like these:

Use python-resource-management to convert this `try/finally` block into a reusable context manager. Keep the public call site simple and do not suppress exceptions.

Use python-resource-management to design an async context manager for a WebSocket client. The connection must close on timeout, cancellation, or normal exit.

Use python-resource-management to restructure this generator-based streaming response so chunk production is incremental and all file handles are released after iteration stops early.

These work because they specify the lifecycle guarantee, which is the skill's real subject.

Common adoption blockers

The usual blockers are not installation issues. They are design ambiguities:

• not knowing who owns cleanup
• mixing shared and local resources in one context manager
• asking for framework-specific behavior without naming the framework
• failing to say whether exception suppression is acceptable
• forgetting async cancellation paths

If the first answer looks generic, the prompt probably lacked one of those details.

What this skill does not replace

The python-resource-management guide does not replace:

• framework docs for dependency injection or lifespan hooks
• driver-specific rules for transaction boundaries
• load testing for stream backpressure
• security review around temp files, credentials, or socket handling

Use this skill to structure lifecycle-safe code, then validate with the actual library or framework you deploy.

python-resource-management skill FAQ

Is python-resource-management good for beginners?

Yes, if you already know basic Python syntax and want help using with, async with, or contextlib correctly. It is less beginner-friendly if you are still learning what a file handle, DB session, or async client actually does, because the quality of the result depends on understanding the resource being managed.

When should I use this instead of a normal coding prompt?

Use python-resource-management when the main risk is leaked resources, incorrect teardown, or messy nested cleanup. If your task is just "write a Python function," a normal prompt is enough. If your task is "make sure this connection always closes even when streaming fails," this skill is the better choice.

Does the python-resource-management skill cover async code?

Yes. The source guidance explicitly includes async context managers and the __aenter__ / __aexit__ protocol. If your code uses asyncio, web handlers, async database clients, or streaming coroutines, say so directly in the prompt.

Can it help with streaming responses?

Yes. This is one of the more useful reasons to install it. The skill specifically mentions building streaming responses with accumulated state, which makes it relevant when you need incremental output without leaking handles or retaining too much memory.

Is this skill opinionated about exception suppression?

It explains the core rule: returning True from __exit__ suppresses an exception, while False propagates it. That is useful, but you still need to tell the agent what behavior you want. In backend systems, silent suppression is often the wrong default unless you have a very clear recovery policy.

Is python-resource-management enough for database pooling?

Not by itself. It can help model connection or session cleanup, but it is not a substitute for pool configuration, driver tuning, or transaction semantics. Pair it with your DB library docs.

When is this skill a poor fit?

Skip it when the task is mainly about:

• business logic
• framework routing
• schema design
• performance profiling unrelated to resource lifetime
• code that does not acquire or release meaningful resources

In those cases, the python-resource-management install decision is easy: you probably do not need this skill yet.

How to Improve python-resource-management skill

Give the agent ownership boundaries

The fastest way to improve results with python-resource-management is to state who owns each resource. For example:

• "The function creates the file handle and must close it."
• "The DB session is injected and must not be closed here."
• "The HTTP client should live for one request only."

Without ownership boundaries, generated code often closes shared resources at the wrong layer.

Specify cleanup order explicitly

For nested resources, cleanup order matters. Tell the agent what must be released first and why. Example:

Use python-resource-management. The temp file must flush before the upload client closes, and the DB transaction should only commit after the upload succeeds.

This avoids superficially tidy code with unsafe teardown sequencing.

Ask for failure-path reasoning, not just code

A strong follow-up request is:

Explain what happens on success, on an exception inside the block, and on an exception during cleanup.

That forces the agent to reason about the actual lifecycle instead of generating a decorative context manager.

Provide sync or async mode up front

Many weak outputs come from not specifying execution mode. If the real code is async, say so in the first sentence. Otherwise the model may generate a sync with pattern that looks plausible but does not fit your stack.

Include one realistic code sample

Even a partial sample improves the skill's output more than a long abstract description. The model can then preserve call-site ergonomics, naming, and exception flow while applying the resource management pattern correctly.

Watch for these common failure modes

Review first-pass output for:

• cleanup happening in the wrong scope
• exception suppression when you did not ask for it
• wrapping a shared dependency in a local context manager
• using sync context managers for async resources
• streaming code that still accumulates all data in memory
• custom context managers where plain contextlib would be simpler

These are the places where a second prompt usually pays off.

Ask for the simplest safe pattern

If you want maintainable code, say that explicitly. Example:

Use the simplest safe standard-library pattern. Prefer `contextlib.contextmanager` unless a class-based context manager is clearly needed.

That often improves readability and avoids overengineering.

Iterate with targeted corrections

After the first answer, improve it with narrow feedback instead of restarting broadly:

• "Do not suppress exceptions."
• "Make this asynccontextmanager."
• "The caller owns the session; remove session closing."
• "Refactor to support two nested resources."
• "Keep streaming incremental; do not buffer the full response."

This kind of iteration gets more value from the python-resource-management skill than repeatedly asking for a full rewrite.

Pair generated code with tests that force cleanup

To improve confidence, ask the agent for tests that simulate failures during use and during teardown. Resource-management code often looks correct until cancellation, mid-stream failure, or nested exception paths are exercised.

Use python-resource-management as a refactoring tool

The best ongoing use of python-resource-management is not only greenfield generation. It is also strong for reviewing existing backend code that already works but hides cleanup bugs. Feed the current implementation to the agent and ask it to identify lifecycle risks before proposing a rewrite. That usually produces more trustworthy output than asking for a fresh implementation from scratch.