python-error-handling

Overview of python-error-handling skill

What the python-error-handling skill does

The python-error-handling skill teaches an agent how to design safer Python code around validation, exceptions, and failure recovery. It is most useful when you want more than “wrap it in try/except”: the real job is to make code fail early, fail clearly, and remain debuggable in APIs, data pipelines, batch jobs, and domain logic.

Who should install python-error-handling

Best-fit users are developers and agent workflows that regularly generate or refactor Python code where bad inputs, external dependencies, or partial failures matter. If you build request handlers, service layers, ETL jobs, CLI tools, or typed domain models, this skill is a better fit than a generic debugging prompt.

What makes this skill different from a normal coding prompt

A normal prompt often produces reactive error handling after the fact. The python-error-handling skill is opinionated around a few high-value patterns: fail-fast validation, meaningful exception types, preserving exception context, and handling batch failures without losing successful work. That makes it especially useful for robustness work, not just syntax fixes.

What users usually care about first

Before installing, most users want to know whether the skill will:

• improve debugging clarity
• reduce vague Exception usage
• help structure validation logic
• avoid all-or-nothing batch behavior
• produce code that is easier to maintain under real failures

Based on the source, those are exactly the core concerns it addresses.

What this skill does not try to cover

The repository evidence shows a focused, single-file skill with no supporting scripts or rule packs. That means python-error-handling is guidance-heavy rather than automation-heavy. Expect design patterns and code generation direction, not a runnable framework, linter, or packaged library.

How to Use python-error-handling skill

python-error-handling install context

Install the skill from the wshobson/agents repository:

npx skills add https://github.com/wshobson/agents --skill python-error-handling

Because the skill lives at plugins/python-development/skills/python-error-handling, it is best treated as a targeted Python robustness skill you invoke when creating or revising code paths where failures need deliberate design.

Read this file first

Start with:

• SKILL.md

There are no extra resources/, rules/, or helper scripts in this skill, so most of the value is in understanding the patterns in SKILL.md and applying them to your codebase with concrete context.

Best times to call python-error-handling

Use python-error-handling when you are:

• adding validation to function inputs or API parameters
• converting external data into stricter internal types
• designing custom exception classes
• improving error messages for operators or callers
• handling per-record failures in imports or batch jobs
• refactoring broad except Exception blocks into clearer flows

If your task is purely performance tuning or business logic generation, this skill is probably not the main driver.

What input the skill needs from you

The skill works much better when you provide:

• the function or class being changed
• expected input types and invalid cases
• whether the caller is an API, CLI, worker, or library
• whether failures should stop the whole process or be collected
• any existing exception classes or framework conventions

Without that context, the output may be technically valid but poorly aligned with your system’s error semantics.

Turn a rough goal into a strong prompt

Weak prompt:

Add error handling to this Python function.

Stronger prompt:

Use the python-error-handling skill to refactor this Python batch import function. Validate inputs before network calls, raise specific exceptions instead of generic ones, preserve original exception context with chaining, and return separate success/failure results so one bad record does not abort the whole batch. Keep it suitable for a FastAPI service.

That stronger version gives the agent the four decisions this skill is built around: where to validate, what to raise, how to preserve context, and whether partial failure is acceptable.

Prompt pattern for validation-heavy code

For input validation tasks, provide the contract explicitly:

Apply python-error-handling to this function. Enforce required fields, type/range checks, and clear user-facing error messages. Report multiple validation issues together where practical, and fail before database or HTTP calls.

This tends to produce better output than simply asking for “safer code,” because the skill emphasizes fail-fast validation and actionable messages.

Prompt pattern for debugging and incident follow-up

For python-error-handling for Debugging, ask the agent to preserve traceability, not just silence errors:

Use python-error-handling to review this stack trace and function. Identify where context is being lost, replace broad catches with specific exceptions, chain exceptions with 'raise ... from ...', and improve messages so operators can tell input errors from downstream service failures.

This is a better fit than a generic debugging request when the problem is poor observability of failures.

Suggested workflow in practice

A practical python-error-handling usage workflow:

1. Identify the boundary: API input, file parse, external response, or batch record.
2. Define what counts as invalid input versus operational failure.
3. Ask the skill to add early validation and specific exception types.
4. Decide whether a single failure should abort or be recorded and continued.
5. Review messages for both developers and end users.
6. Test one happy path, one validation failure, and one downstream exception path.

This sequence mirrors the actual priorities surfaced in the skill.

What good output looks like

Strong output from python-error-handling usually has these traits:

• validation before expensive work
• clear exception names and messages
• no silent swallowing of useful context
• exception chaining when re-raising
• distinct handling for per-item failures in loops or batch processing

If the generated code only adds a blanket try/except with logging, the skill was not really applied well.

Common adoption blockers

The main blocker is not installation but under-specification. Users often omit the intended failure policy. The skill cannot infer whether your system should:

• stop on first error
• collect many validation issues
• continue processing valid items
• hide internal details from end users
• expose domain-specific exception classes

State those choices up front or the result will be generic.

python-error-handling skill FAQ

Is python-error-handling suitable for beginners?

Yes, if you already write basic Python functions and want better habits around validation and exceptions. The patterns are conceptually straightforward: validate early, raise the right error, keep context, and decide how batch failures should behave.

When is python-error-handling better than an ordinary prompt?

It is better when reliability matters more than quick code generation. A generic prompt may fix a bug; the python-error-handling skill is better for designing failure behavior that remains understandable six months later.

Does python-error-handling require a specific framework?

No. The guidance is framework-agnostic. It can be applied in plain Python, web handlers, workers, data pipelines, or libraries. You should still tell the agent whether you are using FastAPI, Django, Click, or another stack so messages and exception boundaries fit your environment.

Is this a package or just guidance?

This is a skill document, not a Python package. There are no bundled scripts or reference files in the skill folder. Install it to improve agent behavior and code generation, not to import a runtime dependency.

When should I not use python-error-handling?

Skip it when the task is unrelated to failure design, such as pure algorithm implementation, UI work, or one-off scripting where robust error semantics are not worth the added structure. It is also less useful if your organization already enforces strict exception conventions through internal frameworks.

Can python-error-handling help with batch jobs?

Yes. One of its clearest strengths is partial failure handling: keeping successes and failures separate so a single bad item does not collapse the whole run. That is a strong fit for imports, sync jobs, and record-by-record processing.

How to Improve python-error-handling skill

Give python-error-handling concrete failure boundaries

The fastest way to improve output is to define where validation ends and operational handling begins. Tell the agent which errors are caller mistakes, which are domain rule violations, and which come from dependencies like HTTP, files, or databases.

Provide examples of bad inputs

If you want high-quality validation code, include real invalid cases:

• empty strings
• out-of-range numbers
• missing keys
• malformed dates
• duplicate identifiers

This pushes the python-error-handling output toward explicit checks and better messages instead of vague guards.

Specify whether to aggregate or fail immediately

A common failure mode is ambiguity between “report all issues” and “stop now.” The skill supports both styles, but the prompt must choose. Aggregation is often better for forms, payload validation, and bulk imports; immediate failure is often better for internal helper functions.

Ask for exception hierarchy decisions

If the codebase is non-trivial, ask the agent to propose or align with an exception hierarchy. That avoids a flat mix of ValueError, RuntimeError, and generic catches. Example:

Use python-error-handling to define domain-specific exceptions for validation, not-found, and external service failure cases, and show where each should be raised.

Improve messages for two audiences

Better outputs distinguish between:

• developer-facing diagnostic detail
• user-facing actionable guidance

Ask the skill to keep internal context for logs and traces while returning cleaner messages to callers where needed. This is especially important in APIs and CLIs.

Force context preservation during refactors

If you are replacing existing try/except logic, explicitly request exception chaining. Otherwise agents sometimes simplify code in ways that lose the original traceback. For debugging-heavy workflows, preserving cause chains is one of the highest-value improvements.

Iterate after the first draft

After the first result, ask targeted follow-ups instead of “make it better.” Good second-pass prompts include:

• Which exceptions are still too generic?
• Where are we validating too late?
• How should partial failures be reported to callers?
• What branch would be hardest to debug in production?

This makes the python-error-handling guide far more useful than a one-shot generation pass.

Review generated code for over-handling

A subtle failure mode is adding too many try/except blocks. Good python-error-handling usage is not about catching everything; it is about catching errors at the right boundary and letting unexpected failures surface when appropriate. If the output hides bugs, simplify it.