python-patterns

Pythonic イディオム、PEP 8標準、型ヒント、堅牢で効率的かつ保守可能なPythonアプリケーションを構築するためのベストプラクティス。

1.13x

Quality

37%

Does it follow best practices?

Impact

90%

1.13x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./docs/ja-JP/skills/python-patterns/SKILL.md

Quality

Discovery

32%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description identifies the Python coding standards domain and mentions specific concepts like PEP 8 and type hints, but lacks concrete action verbs describing what the skill actually does. The complete absence of a 'Use when...' clause significantly weakens its utility for skill selection. Being written entirely in Japanese also limits its discoverability for non-Japanese users.

Suggestions

Add an explicit 'Use when...' clause specifying triggers, e.g., 'Use when the user asks for Python code review, PEP 8 compliance, adding type hints, or improving code style.'

Replace abstract nouns with concrete action verbs, e.g., 'Reviews Python code for PEP 8 compliance, adds type annotations, refactors code to use Pythonic idioms, and suggests performance improvements.'

Include common English trigger terms alongside Japanese to improve discoverability, such as 'Python style', 'code quality', 'linting', 'refactor', 'clean code'.

Dimension	Reasoning	Score
Specificity	Names the domain (Python) and mentions some specific concepts like 'Pythonic idioms', 'PEP 8 standards', 'type hints', and 'best practices', but these are more like categories than concrete actions. No specific verbs describing what the skill does (e.g., 'refactors code', 'adds type annotations', 'lints for PEP 8 compliance').	2 / 3
Completeness	Describes 'what' at a high level (Python best practices, PEP 8, type hints) but completely lacks any 'when' clause or explicit trigger guidance. There is no 'Use when...' or equivalent statement, which per the rubric should cap completeness at 2, and since the 'what' is also vague, this scores a 1.	1 / 3
Trigger Term Quality	Includes relevant keywords like 'PEP 8', 'type hints' (型ヒント), 'Pythonic', and 'Python' that users might mention. However, it's in Japanese which limits discoverability for English-speaking users, and it misses common variations like 'code style', 'linting', 'code review', 'clean code', or 'refactor'.	2 / 3
Distinctiveness Conflict Risk	Somewhat specific to Python coding standards and idioms, which narrows the domain, but could easily overlap with general Python development skills, code review skills, or linting skills. The broad mention of 'building robust, efficient, and maintainable Python applications' is quite generic.	2 / 3
	Total	7 / 12 Passed

Implementation

42%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is a comprehensive but extremely verbose Python best practices reference that explains many concepts Claude already knows thoroughly. While the code examples are high-quality and executable, the sheer volume of well-known Python idioms (type hints, list comprehensions, context managers, decorators) wastes significant context window space. The content would benefit enormously from being condensed to only project-specific conventions or non-obvious patterns, with the bulk moved to reference files.

Suggestions

Reduce the body to ~50 lines covering only project-specific conventions or non-obvious decisions (e.g., 'always use ruff over pylint', specific pyproject.toml config), removing explanations of standard Python idioms Claude already knows.

Move detailed pattern examples (decorators, concurrency, data classes, etc.) into separate reference files like PATTERNS.md or EXAMPLES.md, linked from a concise overview.

Remove all 'good vs bad' comparisons for universally known Python practices (mutable defaults, bare except, isinstance vs type) — Claude knows these.

Add a brief workflow section for the primary use case (e.g., 'When writing new Python code: 1. Follow these conventions, 2. Run `ruff check .` and `mypy .`, 3. Fix all issues before committing') with explicit validation steps.

Dimension	Reasoning	Score
Conciseness	This is extremely verbose at ~500+ lines, covering topics Claude already knows well (basic Python idioms, PEP 8, type hints, list comprehensions, context managers, decorators, etc.). Nearly every section explains fundamental Python concepts that Claude has deep knowledge of. The 'good vs bad' pattern is repeated extensively for well-known idioms, wasting significant token budget.	1 / 3
Actionability	The skill provides fully executable, copy-paste ready code examples throughout every section. Commands for tooling (black, ruff, mypy, pytest) are concrete, and the pyproject.toml configuration is complete and usable.	3 / 3
Workflow Clarity	The skill is organized by topic with clear sections, but it's a reference/pattern guide rather than a workflow. There are no multi-step processes with validation checkpoints. The 'when to activate' section lists triggers but doesn't sequence how to apply the patterns during code review or refactoring.	2 / 3
Progressive Disclosure	This is a monolithic wall of text with no references to external files or any layered structure. All content—from basic type hints to concurrency patterns to project layout—is inlined in a single massive document with no progressive disclosure or navigation aids.	1 / 3
	Total	7 / 12 Passed

Validation

90%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (750 lines); consider splitting into references/ and linking	Warning

	Total	10 / 11 Passed

Repository: affaan-m/everything-claude-code
Commit: 841beea

Reviewed: 7 days ago

Table of Contents

Discovery Implementation Validation

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