@nathan-gage/python-best-practices

Python software engineering guidelines from real PR review patterns. This skill should be used when writing, reviewing, or refactoring Python code — especially dataclasses, service interfaces, error handling, and type annotations. Triggers on tasks involving Python modules, API design, data modeling, type safety, exception handling, or refactoring for maintainability.

View in AI SkillSafe app
28 scan findings
0 downloads
0 stars
0 demos
SKILL.md
namepython-best-practices
descriptionPython software engineering guidelines from real PR review patterns. This skill should be used when writing, reviewing, or refactoring Python code — especially dataclasses, service interfaces, error handling, and type annotations. Triggers on tasks involving Python modules, API design, data modeling, type safety, exception handling, or refactoring for maintainability. For asyncio and concurrency guidance, see the python-async-best-practices skill.
licenseMIT

Python Best Practices

Guidelines for writing and reviewing Python. 75 rules across 8 categories, prioritized by impact.

A rule match is a signal, not a verdict. Most rules are design preferences for new code, not bugs to fix across the repo — check the rule's impact level before flagging in review or refactoring stable code.

Quick-reference lines are triggers, not licenses: before applying a rule as a review finding or a transformation, open the rule file and check its counter-signal — the marker-opened paragraph (**When ...** / **Scope:** / **Preserve ...**) saying when NOT to apply it.

When to Apply

  • Writing new Python modules, functions, classes, or data models
  • Reviewing code for correctness or type safety
  • Refactoring patterns in code that's being edited anyway

Avoid applying these rules as a blanket sweep across stable code — the churn rarely pays off.

Impact Levels

  • CRITICAL — prevents a real bug class (data corruption, swallowed cancellations, insecure defaults). Fix when found.
  • HIGH — meaningful correctness or maintainability win. Worth fixing in most contexts.
  • MEDIUM — good practice; clarity or drift prevention. Apply to new code; don't churn stable code.
  • LOW-MEDIUM / LOW — style or micro-optimizations. Apply opportunistically.

Python Version Baseline

Rules assume Python 3.11+. Rules depending on higher versions call it out inline:

  • warnings.deprecated() — 3.13+
  • zoneinfo — 3.9+
  • Union types in isinstance() — 3.10+
  • assert_never — 3.11+ (backport via typing_extensions)
  • PEP 695 type statement and generic syntax — 3.12+ (noted inline in types-modern-syntax)

Rules tagged applicability:pydantic are Pydantic-specific.

Rule Categories by Priority

Priority Category Impact Prefix
1 Data Modeling HIGH data-
2 Error Handling MEDIUM-HIGH error-
3 Type Safety MEDIUM-HIGH types-
4 API Design MEDIUM api-
5 Code Simplification LOW-MEDIUM simplify-
6 Performance LOW-MEDIUM perf-
7 Naming LOW-MEDIUM naming-
8 Imports & Structure LOW imports-

Section impact is a typical-case label; individual rules range one level above or below — check the rule file.

Quick Reference

Data Modeling (data-)

  • data-mutable-defaults — Never def f(items=[]); use None + body construction or default_factory
  • data-derive-dont-store — Compute booleans from state; don't cache flags that mirror each other
  • data-mutation-contract — One unambiguous contract per function: mutate (new-info returns fine) or return new — never the mutated object as if fresh
  • data-aware-datetimes — Timezone-aware datetime.now(timezone.utc); utcnow() is deprecated
  • data-discriminated-unions — Tag variants instead of optional-field bags
  • data-explicit-variants — Concrete classes per mode beat is_thread / is_edit flags
  • data-phased-composition — Group co-present optionals into one nested optional
  • data-encapsulate-mutable-state — Trap mutable state in the narrowest clear scope
  • data-sentinel-when-none-is-valid — Private sentinel when None is a meaningful value
  • data-newtype-for-idsNewType('UserId', str) so IDs aren't interchangeable
  • data-delete-dead-variants — Remove union arms that aren't constructed
  • data-reject-bool-as-intbool subclasses int; reject it explicitly before numeric checks

Error Handling (error-)

  • error-specific-exceptions — Catch specific types; never bare except: or except BaseException: (breaks Ctrl-C and async cancellation); except Exception: is cancellation-safe on 3.8+
  • error-context-managerswith / async with for files, locks, sessions
  • error-assert-debug-onlyassert vanishes under -O; not for runtime contracts
  • error-validate-at-boundaries — Fail fast at system edges before expensive work
  • error-trust-validated-state — Trust immutable, locally-constructed state
  • error-consolidate-try-except — Merge blocks with the same catch and handling
  • error-assert-never-exhaustivenesstyping.assert_never for exhaustiveness
  • error-raise-from-for-chainsraise NewErr(...) from original to preserve causality
  • error-inherit-base-exceptions — New exceptions inherit existing bases for compatibility
  • error-log-exception-contextlogger.exception(...) inside except; keep the traceback in the log
  • error-repr-in-messagesf"tool {name!r}" for identifiers in error text
  • error-match-types-not-messages — Classify by exception type and status code, never message substrings

Type Safety (types-)

  • types-fix-errors-not-ignore — Fix type errors; # type: ignore is a last resort
  • types-avoid-any — Protocols, TypeVars, unions over Any
  • types-typeddict-over-dict-anyTypedDict / dataclass when structure is known
  • types-literal-for-fixed-setsLiteral["a", "b"] for fixed strings
  • types-fix-types-not-cast — Fix the definition; cast() only when runtime genuinely narrows
  • types-isinstance-for-narrowingisinstance() over hasattr / type(x).__name__
  • types-narrow-to-runtime-reality — Annotations match what control flow actually allows
  • types-trust-the-checker — Drop runtime checks the types already enforce
  • types-remove-redundant-optional — Drop | None when values are guaranteed present
  • types-type-checking-importsif TYPE_CHECKING: for optional or heavy imports
  • types-modern-syntaxX | None, list[str]; not Optional / Union / typing.List
  • types-sequence-over-list-paramsSequence / Mapping for read-only params; list is invariant

API Design (api-)

  • api-required-before-optional — Required fields before optional (Python enforces this)
  • api-keyword-only-params* marker for optional/config params
  • api-no-boolean-flag-paramsLiteral / Enum over True, False soup
  • api-immutable-transforms — Return new collections; don't mutate inputs
  • api-model-cohesion — Flat models; no duplicate or single-key-wrapped fields
  • api-underscore-for-private_prefix for internals; exclude from __all__
  • api-deprecated-aliaseswarnings.deprecated() (3.13+) for renamed APIs
  • api-no-private-access — Don't reach into _prefixed names from outside the module
  • api-instance-vs-module-fn — Pick the namespace that matches ownership

Code Simplification (simplify-)

  • simplify-early-return — Return early; don't nest the happy path
  • simplify-extract-after-duplication — Second copy is the decision point; third is the safe default
  • simplify-cached-property@cached_property on immutable instances; not thread-safe
  • simplify-comprehensions — Comprehensions over for + .append()
  • simplify-any-all-builtinsany() / all() over manual flag + break
  • simplify-fallback-orx or default when falsy values aren't semantic
  • simplify-flatten-nested-ifif cond1 and cond2: when no intervening code
  • simplify-inline-single-use-vars — Drop intermediates used once
  • simplify-remove-dead-code — Delete commented-out code; git preserves history

Performance (perf-)

  • perf-set-for-membershipset for repeated in checks
  • perf-dict-index-over-nested-loops — Build a dict for lookups
  • perf-lru-cache-pure-fnsfunctools.lru_cache / functools.cache on pure functions
  • perf-generator-over-list — Stream with generators when memory or latency matters
  • perf-combine-iterations — Fuse filter + map into one pass
  • perf-compile-regex-module-level — Compile static regex at module scope; matters in tight loops
  • perf-type-adapter-constant — Module-scope TypeAdapter (applicability: pydantic)
  • perf-isinstance-tuple-syntax — Tuple form is marginally faster; profiled hot paths only

Naming (naming-)

  • naming-rename-on-behavior-change — Rename when behavior changes; stale names mislead
  • naming-consistent-terminology — Same concept, same word across code/docs/errors
  • naming-specific-over-generictoolset_id; not bare id
  • naming-drop-redundant-prefixesToolConfig.description; not ToolConfig.tool_description
  • naming-upper-case-constantsMAX_RETRIES; _ prefix for internal
  • naming-no-type-suffixes — No _dict / _list suffixes; types annotate types

Imports & Structure (imports-)

  • imports-no-side-effects — Modules must be cheap to import — no network/model/env reads at import
  • imports-top-of-file — Imports at the top; documented exceptions for circular / optional / deferred
  • imports-optional-dependenciestry / except ImportError with install hints
  • imports-scope-helpers-to-usage — Define helpers near where they're used
  • imports-remove-unused — Delete unused imports
  • imports-no-duplicates — One import per name
  • imports-lightweight-init — Parent __init__.py runs on every submodule import; keep heavy/optional deps out

How to Use

Read individual rule files for detail:

rules/data-mutable-defaults.md
rules/error-specific-exceptions.md

Each rule has:

  • Impact level in frontmatter
  • Brief explanation
  • Incorrect example
  • Correct example
  • Optional note on edge cases

For the full compiled guide with all rules expanded: AGENTS.md.

Embed badges

Add these to your README to show the skill's verification status.

SkillSafe verified badge
Verified badge
[![SkillSafe verified badge](https://api.skillsafe.ai/v1/badge/@nathan-gage/python-best-practices/verified)](https://skillsafe.ai/skill/@nathan-gage/python-best-practices/)
Installs badge
Installs badge
[![Installs badge](https://api.skillsafe.ai/v1/badge/@nathan-gage/python-best-practices/installs)](https://skillsafe.ai/skill/@nathan-gage/python-best-practices/)
Scan badge
Scan badge
[![Scan badge](https://api.skillsafe.ai/v1/badge/@nathan-gage/python-best-practices/scan)](https://skillsafe.ai/skill/@nathan-gage/python-best-practices/)
Eval pass rate badge
Eval pass rate
[![Eval pass rate badge](https://api.skillsafe.ai/v1/badge/@nathan-gage/python-best-practices/eval)](https://skillsafe.ai/skill/@nathan-gage/python-best-practices/)