python-design-patterns

Overview of python-design-patterns skill

What the python-design-patterns skill does

The python-design-patterns skill is a design-review and refactoring guide for Python code. It helps an agent apply a small set of high-value principles—KISS, Single Responsibility, Separation of Concerns, Composition Over Inheritance, and the Rule of Three—to real coding decisions instead of generating abstract pattern theory.

Who should install it

This skill fits developers, reviewers, and AI-assisted coding workflows that need help with:

• refactoring oversized classes or functions
• designing new modules or services with cleaner boundaries
• deciding whether an abstraction is justified
• reducing coupling so code becomes easier to test

It is especially useful for python-design-patterns for Refactoring, where the main problem is not syntax but structure.

The real job-to-be-done

Most users do not need a catalog of Gang of Four patterns. They need help answering practical questions such as:

• Should this logic be split?
• Is inheritance making this harder to change?
• Where should I draw module boundaries?
• Is this abstraction premature?
• Why is this code hard to test?

The python-design-patterns skill is strongest when you already have code, constraints, and a concrete design decision to evaluate.

What makes it different from a generic prompt

A normal prompt may produce style advice or over-engineered class diagrams. The python-design-patterns skill is more useful when you want disciplined simplification:

• prefer the simplest working design
• separate responsibilities before adding abstractions
• favor composition when inheritance adds hidden coupling
• delay abstraction until repetition is real, not speculative

That bias is valuable if your codebase is getting harder to reason about.

What it does not cover well

This skill is narrow by design. It does not appear to ship helper scripts, validation tooling, or framework-specific recipes. It is a thinking aid for code structure, not a full architecture framework, linter, or pattern library.

How to Use python-design-patterns skill

Install context for python-design-patterns install

The repository does not expose a dedicated install command inside SKILL.md, so use the directory's standard skill installation flow for the wshobson/agents repo, then enable the python-design-patterns skill from:

plugins/python-development/skills/python-design-patterns

If your environment supports direct skill adds from GitHub, the usual pattern is:

npx skills add https://github.com/wshobson/agents --skill python-design-patterns

Read this file first

Start with:

• SKILL.md

There are no visible support files such as rules/, resources/, or references/ here, so nearly all of the usable guidance lives in that single file. This means adoption is quick, but depth depends on how well you frame your prompt.

Best use cases for python-design-patterns usage

Use python-design-patterns usage when you can provide one of these:

• a code snippet that feels too tangled
• a PR diff with structural concerns
• a proposed class hierarchy
• a module that mixes I/O, business rules, and formatting
• repeated logic where you are unsure whether to abstract

Avoid invoking it on vague goals like “make this code better” without code or constraints.

What input the skill needs

For high-quality output, give the agent:

1. the current code or pseudocode
2. the main pain point
3. constraints such as framework, performance, team preference, or backwards compatibility
4. what kind of answer you want: critique, refactor plan, or rewritten code

Minimal input leads to generic principles. Concrete input produces actionable restructuring advice.

Turn a rough goal into a strong prompt

Weak prompt:

• “Use python-design-patterns on this service.”

Better prompt:

• “Use python-design-patterns to review this Python service class. Identify where it violates single responsibility, where composition would be better than inheritance, and where abstractions are premature. Then propose a refactor plan that preserves public behavior.”

Best prompt:

• “Use python-design-patterns on the code below. Goal: make it easier to unit test and reduce coupling to external APIs. Constraints: Python 3.11, keep the current public methods, no new frameworks, small-team codebase. Please return: 1) issues found, 2) recommended module/class boundaries, 3) a refactor sequence, 4) revised code for the highest-value change first.”

A practical workflow that works

A good python-design-patterns guide workflow is:

1. paste the current code
2. ask for principle-based diagnosis
3. ask which problems matter most
4. choose one restructuring direction
5. request incremental code changes, not a full rewrite
6. review testability and coupling after each step

This avoids the common failure mode where the model redesigns everything at once.

Ask for decisions, not just explanations

The skill is most valuable when the agent must choose among options. For example:

• “Should this be one class or three?”
• “Should I use inheritance here or inject a collaborator?”
• “Is this duplication acceptable, or should I abstract now?”
• “Which responsibilities should leave this function first?”

That framing makes the principles operational.

How to use it for refactoring

For python-design-patterns for Refactoring, ask the agent to:

• mark the current responsibilities in the code
• identify coupling hotspots
• separate pure logic from side effects
• recommend the smallest useful extraction first
• explain why each move improves changeability or testability

This is more effective than asking for “clean architecture” up front.

How to use it for new design work

When no code exists yet, provide:

• the domain objects
• expected behaviors
• external dependencies
• likely areas of change
• examples of future features you expect

Then ask the agent to propose a simple initial structure and explicitly justify why it avoids premature abstraction.

What good output should look like

Strong output from the python-design-patterns skill usually includes:

• a short diagnosis tied to named principles
• clear boundaries between responsibilities
• a conservative recommendation on abstractions
• a preference for composition where inheritance adds rigidity
• staged refactoring steps instead of a sweeping rewrite

If the answer is only theory or only code, ask for the missing half.

python-design-patterns skill FAQ

Is python-design-patterns good for beginners

Yes, if you already know basic Python syntax. The skill focuses on design choices that beginners often struggle with, but it works best when you can inspect code and discuss tradeoffs rather than memorize definitions.

Is this a pattern catalog like GoF

Not really. The visible guidance is centered on foundational design principles, not a large catalog of formal object-oriented patterns. That is a strength if your problem is maintainability, not pattern coverage.

When should I not use python-design-patterns

Skip python-design-patterns when:

• you need framework-specific implementation details
• your task is mostly algorithmic, not structural
• you need runnable tooling or automated transformations
• the code is too early-stage to have real design pressure

It can also be overkill for tiny scripts where simplicity already exists.

How is this different from ordinary refactor prompts

Ordinary prompts often optimize for polished output. The python-design-patterns skill gives the agent a clearer lens for judging simplicity, responsibility boundaries, and abstraction timing. That usually leads to fewer unnecessary classes and better reasoning about coupling.

Does it fit modern Python codebases

Yes. The principles are language-agnostic but map well to modern Python services, libraries, and internal tools. They are especially helpful in codebases that mix domain logic with API calls, persistence, or formatting.

Can I use it during code review

Yes. It is a strong fit for PR review prompts such as:

• “Use python-design-patterns to review this diff for SRP violations and unnecessary inheritance.”
• “Evaluate whether this new abstraction is justified or premature.”
• “Flag hidden coupling that will make tests harder.”

How to Improve python-design-patterns skill

Give the agent the change pressure

The biggest improvement is to explain what will change over time:

• new data sources
• more business rules
• stricter testing needs
• likely feature expansion

Without change pressure, the agent cannot judge whether a design is flexible enough or too abstract.

Show current pain, not just code

Better prompts name the actual pain:

• “This class is hard to test because it calls the DB and formats responses.”
• “We keep adding conditionals for provider-specific behavior.”
• “This inheritance tree breaks when only one subclass needs a new rule.”

That context helps the skill choose the right principle instead of listing all of them.

Ask for the smallest high-value refactor first

A common failure mode is over-refactoring. Improve python-design-patterns usage by asking:

• “What is the smallest change with the biggest maintainability gain?”
• “Which extraction should happen first?”
• “What should stay duplicated for now?”

This aligns well with KISS and the Rule of Three.

Force tradeoff-aware answers

If the first answer sounds absolute, ask for tradeoffs:

• “What do we lose if we keep this as one class?”
• “When would inheritance still be acceptable here?”
• “Which abstraction should we delay until more repetition appears?”

The skill becomes more valuable when it explains why, not just what.

Request before-and-after structure

For stronger results, ask for:

• current responsibility map
• proposed responsibility map
• dependency flow before and after
• one concrete code example of the new structure

This makes the design advice reviewable by humans and easier to implement incrementally.

Iterate after the first output

After the first pass, follow up with one of these:

• “Now rewrite only the boundary between I/O and business logic.”
• “Keep the current API and apply composition instead of inheritance.”
• “Reduce classes by 30% and justify each remaining abstraction.”
• “Re-evaluate this refactor for simplicity; what is still overdesigned?”

That iteration pattern usually produces better outcomes than one-shot generation.

Watch for these common failure modes

Be cautious if the output:

• introduces too many classes for a small problem
• adds interfaces without real variation points
• replaces simple duplication too early
• recommends inheritance for code reuse alone
• ignores migration constraints and breaks public behavior

These are exactly the cases where python-design-patterns should be used critically, not blindly.

Improve team adoption with a shared review rubric

If you want repeatable results, turn the skill into a review checklist:

• Is each unit changing for one reason?
• Are side effects separated from domain logic?
• Is composition simpler than inheritance here?
• Has repetition happened enough times to justify abstraction?
• Does the new design improve testing and local reasoning?

Using the python-design-patterns skill this way helps teams get consistent architectural decisions, not just better one-off prompts.