# Ahgraber Skills Python Errors Reliability > Python Errors and Reliability - Skill: `tomevault-io/ahgraber-skills-python-errors-reliability` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ahgraber-skills-python-errors-reliability` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ahgraber-skills-python-errors-reliability/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ahgraber-skills-python-errors-reliability --- # Python Errors and Reliability ## Overview Design errors so callers can act and operators can diagnose quickly. Treat these recommendations as preferred defaults — when a default conflicts with project constraints, suggest a better-fit alternative and call out tradeoffs and compensating controls. ## When to Use - Implementing or reviewing timeout, deadline, or retry logic. - Translating exceptions across layer boundaries (e.g., infra → domain). - Classifying failures as retryable vs. permanent. - Adding idempotency guarantees to retried writes. - Diagnosing swallowed exceptions or silent failures in existing code. **When NOT to use:** - Pure data-transformation code with no I/O or failure modes. - Simple validation that raises immediately with no translation needed. - Performance tuning unrelated to failure handling (see `python-concurrency-performance`). ## Quick Reference - Preserve cause chains at translation boundaries. - Catch only what can be handled. - Keep timeout/deadline policy explicit. - Keep retry policy explicit and bounded. - Classify retryable vs. permanent failures with explicit policy data. - Keep idempotency expectations explicit for retried writes. ## Common Mistakes - **Swallowing exceptions** — bare `except: pass` or `except Exception` with no logging loses diagnostic context. - **Unbounded retries** — retrying without a maximum count or total deadline leads to cascading failures and resource exhaustion. - **Dropping the cause chain** — raising a new exception without `from original` discards the root cause operators need for diagnosis. - **Treating all errors as retryable** — retrying a 400 Bad Request or a validation error wastes resources and delays the real fix. - **Implicit timeouts** — relying on library defaults (or no timeout at all) produces unpredictable latency under failure conditions. ## Scope Note - Treat these recommendations as preferred defaults for common cases, not universal rules. - If a default conflicts with project constraints or worsens the outcome, suggest a better-fit alternative and explain why it is better for this case. - When deviating, call out tradeoffs and compensating controls (tests, observability, migration, rollback). ## Invocation Notice - Inform the user when this skill is being invoked by name: `python-design-modularity`. ## References - `references/error-strategy.md` - `references/retryability-classification.md` - `references/retries-timeouts.md` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/ahgraber) — claim your Tome and manage your conversions.