python-type-safety

Overview of python-type-safety skill

The python-type-safety skill is a focused guide for writing Python that survives static analysis, not just runtime testing. It is best for developers and coding agents who need to add or tighten type hints, introduce generics, define protocols, narrow unions safely, or move a codebase toward stricter mypy or pyright checks.

What python-type-safety is for

Use python-type-safety when your real goal is to make Python code easier to reason about before execution. The skill centers on practical type-safe patterns such as:

• annotating public APIs
• expressing optional values clearly
• preserving type information with generics
• defining structural interfaces with protocols
• using narrowing and guards instead of unsafe assumptions
• configuring strict checking workflows

Who gets the most value

This python-type-safety skill is a strong fit if you:

• maintain libraries or shared internal modules
• generate Python code with an AI assistant and want fewer hidden type errors
• are refactoring legacy Python toward modern typing
• need code that passes mypy or pyright with less trial and error

It is less useful if you only want a syntax reference. Its value is in choosing the right typing pattern for the job.

Why users install it instead of relying on a generic prompt

A generic prompt can add annotations, but it often stops at surface typing. python-type-safety is more useful because it pushes toward better decisions: explicit None handling, safer reusable abstractions, protocol-based interfaces, and stricter checker-friendly code. That matters most when using python-type-safety for Code Generation, where weak types can make generated code look correct while remaining fragile.

What to check before adopting

This skill appears to be documentation-only, with the practical guidance living in SKILL.md. There are no helper scripts or extra resources, so adoption is simple, but output quality depends on the quality of the prompt and the target code you provide. If your repo uses older Python versions, custom checker settings, or a gradual-typing strategy, include that context up front.

How to Use python-type-safety skill

Install context for python-type-safety

Add the skill from the repository:

npx skills add https://github.com/wshobson/agents --skill python-type-safety

Because the repository evidence points to a single SKILL.md file, there is little setup overhead. The main work is telling the agent what code, Python version, and checker constraints it must respect.

Read this file first

Start with:

• plugins/python-development/skills/python-type-safety/SKILL.md

That file contains the actual operating guidance. Since there are no support folders or scripts, you should not expect automation or repository-specific enforcement rules. Treat it as a pattern guide that needs to be grounded in your own codebase.

What input the skill needs to work well

For strong python-type-safety usage, provide:

• the Python version, such as 3.10 or 3.12
• your checker, such as mypy or pyright
• current checker strictness
• the exact code to update
• whether this is library code, app code, or generated code
• any important frameworks or serialization layers
• whether backward compatibility matters

Without this, the agent may choose valid syntax that does not match your environment or checker behavior.

Turn a rough goal into a strong prompt

Weak goal:

Add type hints to this file.

Better goal:

Use the python-type-safety skill to annotate all public functions in this module for Python 3.11. Target pyright strict mode. Prefer explicit return types, preserve existing behavior, avoid Any, and replace unsafe dict-shaped interfaces with Protocol or TypedDict where appropriate. Show the updated code and explain any places where runtime checks are needed for narrowing.

The stronger version improves output because it defines scope, checker target, style constraints, and expected tradeoffs.

Best workflow for python-type-safety for Code Generation

For python-type-safety for Code Generation, use this sequence:

1. Ask for the API shape first.
2. Ask the agent to propose types before full implementation.
3. Have it implement with explicit signatures.
4. Run or simulate checker feedback.
5. Iterate on ambiguous unions, None cases, and generic boundaries.

This avoids a common failure mode where generated code is written first and typed afterward, forcing awkward retrofits.

Practical prompt patterns that produce better code

Useful prompt fragments:

• “Annotate only public signatures; leave local inference alone unless it clarifies a union.”
• “Prefer Protocol over inheritance when consumers only need behavior.”
• “Use generics only where they preserve caller type information.”
• “Show where type narrowing happens and why it is checker-safe.”
• “If a return can be absent, use T | None and update call sites.”

These patterns keep the output aligned with what the skill is actually good at.

What the skill covers especially well

The upstream skill clearly emphasizes:

• type annotations
• generics
• protocols
• type narrowing
• strict type checking with mypy and pyright

That means it is best used for code shape and checker correctness, not for framework-specific plugin behavior unless you add your own repo context.

Common adoption blockers

Users typically get blocked by:

• legacy code with inconsistent types
• hidden None paths
• overuse of Any
• generic abstractions that are too clever for the actual use case
• checker config mismatches between local tools and CI

When using python-type-safety install in a real team workflow, plan for gradual adoption instead of trying to make an old codebase fully strict in one pass.

How to evaluate the first output

A good result from python-type-safety should:

• make public interfaces clearer
• reduce ambiguous return values
• remove obvious unsafe assumptions
• preserve type information across helper functions
• pass stricter checking with minimal suppression comments

A weak result usually adds many annotations but leaves the important uncertainty untouched.

python-type-safety skill FAQ

Is python-type-safety good for beginners

Yes, if you already know basic Python and want practical typing patterns rather than theory alone. The skill is usable by beginners, but it becomes more valuable once you have real code that needs safer interfaces or checker compliance.

When should I use python-type-safety instead of a normal coding prompt

Use python-type-safety when the quality bar includes static correctness, maintainable signatures, or checker-ready abstractions. If you just want a quick script and do not care about long-term safety, a normal prompt may be enough.

Does python-type-safety require mypy or pyright

No, but the skill is most valuable when paired with one of them. Without a checker, you still gain clearer contracts, but you lose the feedback loop that validates the typing choices.

Is this skill only for fully strict codebases

No. It also fits gradual typing. You can use it to annotate public APIs first, tighten high-risk modules, and introduce protocols or generics only where they pay off.

When is python-type-safety a poor fit

Skip it or narrow its role if:

• the code is short-lived throwaway automation
• the team will not run static type checkers
• runtime schema validation matters more than static types
• the code depends heavily on dynamic patterns that you do not want to refactor

Does it help with library design

Yes. python-type-safety guide value is especially strong for reusable libraries, where public signatures, generic containers, and protocol-based interfaces improve both developer experience and safety.

How to Improve python-type-safety skill

Give python-type-safety checker and version targets

The fastest way to improve output is to name:

• Python version
• checker tool
• strictness level
• allowed typing features

For example, whether you allow modern union syntax, Self, ParamSpec, or TypedDict materially changes what “good typing” looks like.

Provide code plus the error surface

Do not ask for typing in the abstract if you already have failures. A better request is:

Use python-type-safety on this module. Here is the code and these five pyright errors. Fix the types with the smallest API change possible.

That focuses the skill on actual blockers instead of generic cleanup.

Ask for rationale on protocols, generics, and unions

A common failure mode is overengineering. Improve results by asking the agent to justify advanced typing choices:

• Why is a Protocol better than a concrete base class here?
• Why is a generic type parameter needed?
• Why is this a union instead of a narrower model?

This keeps python-type-safety usage practical instead of academic.

Force explicit handling of None and narrowing

Many Python bugs come from implicit absence handling. Ask the agent to:

• mark nullable returns explicitly
• update call sites
• show the narrowing branch
• avoid unsafe casts unless unavoidable

This is one of the biggest quality upgrades the python-type-safety skill can deliver.

Iterate on public APIs before internal details

If the first pass is noisy, improve it in this order:

1. public functions and methods
2. shared data structures
3. protocols and interfaces
4. helper functions
5. local variables only where inference is unclear

That order produces better maintainability than annotating everything indiscriminately.

Compare generated output against your repo conventions

To improve python-type-safety in team use, ask the agent to match existing conventions:

• checker comments style
• import style for typing constructs
• preferred collection types
• whether to use Protocol, ABC, or concrete classes
• tolerance for cast and type: ignore

The skill is strongest when it adapts to your codebase instead of imposing a generic typing style.