code-smell-detector

Identify and report code smells indicating poor design or maintainability issues in Python code, including duplicate code, magic numbers, hardcoded values, God classes, feature envy, inappropriate intimacy, data clumps, primitive obsession, and long parameter lists. Use when conducting code quality audits, preparing for refactoring, improving codebase maintainability, or performing design reviews. Produces markdown reports with severity ratings, locations, descriptions, and specific refactoring recommendations with before/after examples. Triggers when users ask to find code smells, identify design issues, suggest refactorings, improve code quality, or detect maintainability problems.

1.43x

Quality

88%

Does it follow best practices?

Impact

93%

1.43x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Quality

Content

77%

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

This is a well-structured, highly actionable skill with clear workflows and excellent concrete examples. However, it suffers from verbosity - the full report template and extensive code examples should be moved to reference files rather than inline. The skill would benefit from being more concise in the main file while leveraging its reference documents more heavily.

Suggestions

Move the full report template (from '## Code Smell Analysis Report' through the end of the template) to a separate reference file like 'report-template.md' and link to it

Condense the 'Tips for Effective Smell Detection' and 'Common False Positives' sections - Claude already understands these concepts and they add ~50 lines of content that could be 10 lines or removed

Consider moving the detailed before/after refactoring examples to refactoring-patterns.md and keeping only one brief example inline

Dimension	Reasoning	Score
Conciseness	The skill is comprehensive but overly verbose in places. The full report template with extensive before/after examples could be condensed or moved to a reference file. Some sections like 'Tips for Effective Smell Detection' and 'Common False Positives' contain guidance Claude likely already knows.	2 / 3
Actionability	Provides fully executable bash commands, Python code examples, and specific tool invocations. The before/after refactoring examples are concrete and copy-paste ready. Detection strategies include real grep patterns and tool commands.	3 / 3
Workflow Clarity	Clear 6-step workflow with explicit sequencing from scope definition through detection, categorization, refactoring identification, report generation, and presentation. Includes validation through external tools (radon, pylint) and clear decision points.	3 / 3
Progressive Disclosure	References smell-patterns.md and refactoring-patterns.md appropriately, but the main file is too long. The full report template (150+ lines) and extensive before/after examples should be in separate reference files. The inline content overwhelms the overview structure.	2 / 3
	Total	10 / 12 Passed

Description

100%

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

This is an excellent skill description that hits all the key criteria. It provides specific capabilities with named code smell types, includes comprehensive trigger terms users would naturally use, explicitly states both what it does and when to use it, and carves out a distinct niche in Python code quality analysis. The description is thorough without being padded with fluff.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'identify and report code smells', names specific smell types (duplicate code, magic numbers, God classes, feature envy, etc.), and describes output format (markdown reports with severity ratings, locations, refactoring recommendations with before/after examples).	3 / 3
Completeness	Clearly answers both what (identify code smells, produce markdown reports with specific details) AND when with explicit 'Use when...' and 'Triggers when...' clauses covering multiple scenarios like code audits, refactoring prep, and design reviews.	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'code smells', 'design issues', 'refactoring', 'code quality', 'maintainability problems', 'code quality audits', 'design reviews'. Also includes specific smell names users might search for.	3 / 3
Distinctiveness Conflict Risk	Clear niche focused specifically on Python code smell detection and design issue identification. The specific smell types, Python focus, and detailed output format (markdown with before/after examples) make it distinct from general code review or linting skills.	3 / 3
	Total	12 / 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 (609 lines); consider splitting into references/ and linking	Warning

	Total	10 / 11 Passed

Repository: ArabelaTso/Skills-4-SE
Commit: 0f00a4f

Reviewed: 4 months ago

Table of Contents

Discovery Implementation Validation

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