How do I use Python Engineering on Zeplik?

Sign in to Zeplik and ask in plain language, or type /python-engineering in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.

Which AI model does the Python Engineering skill use?

Any model you choose. Zeplik works across every model in one chat, so the Python Engineering skill runs on your preferred model for the task.

Where does the Python Engineering skill come from?

The Python Engineering skill is adapted from the open-source wshobson/agents project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.

How much does the Python Engineering skill cost?

Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

Python Engineering

Software development skill, available on Zeplik

Python Engineering is a ready-to-run software development skill on Zeplik. Not for step-by-step bug hunts (use debugging-strategies). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.

The Python Engineering skill loads automatically when your request matches it, or you can invoke it directly by typing /python-engineering in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.

What the Python Engineering skill can do

Design idiomatic Python code structure, typing, and module layout
Set up packaging, pyproject.toml, and uv based dependency workflows
Implement async concurrency, resilience patterns, and error handling
Write pytest suites and profile performance bottlenecks in Python code

Try these prompts on Zeplik

Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.

How the Python Engineering skill works

/python-engineering

Umbrella skill for writing excellent Python: idiomatic style, project and module structure, packaging and distribution, static typing, async and concurrency, testing, error handling, resilience, observability, and performance work. The body is a dispatch table; the depth lives in references/. Pick the matching reference(s), read them, then answer. This lane is about designing and writing good Python -- a step-by-step hunt for a specific misbehaving bug belongs to debugging-strategies.

Answer the request at its size. "Write a function that merges intervals" gets the function and maybe a line on the approach — not a packaged module with a test suite, type stubs, and a pyproject.toml. The references are lookup material for you; mine the relevant patterns and write the code, don't transcribe a reference back or bolt on structure/typing/tests the user didn't ask for. Reach for full runnable scaffolding only when they're actually building a package or service.

Dispatch table

Intent	Reference
asyncio, async/await, concurrent I/O, event loop patterns	references/async-python-patterns.md (+ --details)
Code review checklist for common Python anti-patterns	references/python-anti-patterns.md
Task queues, workers, background jobs, event-driven processing	references/python-background-jobs.md (+ --details)
Style, linting, formatting, naming, docstrings	references/python-code-style.md
Config management, env vars, pydantic-settings, secrets handling	references/python-configuration.md (+ --details)
Design patterns: KISS, SRP, composition over inheritance	references/python-design-patterns.md (+ --details)
Validation, exception hierarchies, partial failure handling	references/python-error-handling.md (+ --details)
Structured logging, metrics, tracing in Python services	references/python-observability.md (+ --details)
Packaging, pyproject.toml, publishing to PyPI	references/python-packaging.md (+ --details, --advanced-patterns)
Profiling, bottlenecks, memory, optimization techniques	references/python-performance-optimization.md (+ --details, --advanced-patterns)
Project layout, module architecture, public API design	references/python-project-structure.md
Retries, backoff, timeouts, fault-tolerant decorators	references/python-resilience.md (+ --details)
Context managers, cleanup, streaming, resource lifetimes	references/python-resource-management.md (+ --details)
pytest, fixtures, mocking, TDD, test suite design	references/python-testing-patterns.md (+ --details, --advanced-patterns)
Type hints, generics, protocols, strict type checking	references/python-type-safety.md (+ --details)
uv package manager, fast dependency and venv workflows	references/uv-package-manager.md (+ --advanced-patterns)

How to work

Read the matching reference file(s) before answering; pull in the --details or --advanced-patterns companions when the base reference defers to them.
If the framework or runtime context is ambiguous (for example "async patterns" without knowing whether it is FastAPI, scripts, or workers; or "testing" without knowing the test runner), ask ONE clarifying question first.
Produce complete, runnable artifacts: full modules, pyproject.toml files, pytest suites, typed interfaces. When verification requires the user's environment, walk them through the commands to run and ask them to paste the output back.
Combine references freely -- a "production-ready service" question often spans structure, typing, error handling, and testing.
If the user is chasing one specific bug through reproduction and bisection, route them to debugging-strategies instead of answering here.

Usage

/python-engineering $ARGUMENTS

How to use the Python Engineering skill

Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Python Engineering skill right away.
Describe your software development task
Ask in plain language, or type /python-engineering to invoke the skill directly. Zeplik recognizes the Python Engineering skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.

Source and credit

Author: anthropic
License: MIT

Adapted from the open-source wshobson/agents project and tuned to run natively on Zeplik. View source on GitHub.

Frequently asked questions

What is the Python Engineering skill?: Python Engineering is a ready-to-run software development skill on Zeplik. Not for step-by-step bug hunts (use debugging-strategies). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
How do I use Python Engineering on Zeplik?: Sign in to Zeplik and ask in plain language, or type /python-engineering in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
Which AI model does the Python Engineering skill use?: Any model you choose. Zeplik works across every model in one chat, so the Python Engineering skill runs on your preferred model for the task.
Where does the Python Engineering skill come from?: The Python Engineering skill is adapted from the open-source wshobson/agents project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
How much does the Python Engineering skill cost?: Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

Related software development skills

More on Zeplik

Try Python Engineering on Zeplik

Every model, one chat. Bring the Python Engineering skill into your next conversation and let the assistant do the work.

Browse all skills