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

83%

Does it follow best practices?

Impact

93%

1.43x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Quality

Content

67%Weight 40%Scale 1-5

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

The body is highly actionable with concrete commands and complete examples, and it structures references well. Its main weakness is verbosity: the embedded full example report and explanation of familiar concepts consume significant context that could be trimmed or moved to a reference file.

Suggestions

Move the full worked example report (the five before/after smell examples) into a reference file such as references/report-template.md and keep only a compact template skeleton inline.

Trim explanations of concepts Claude already knows (e.g., Single Responsibility Principle, tell-don't-ask, what magic numbers are) and keep only the detection/refactoring specifics.

Add an explicit validation checkpoint between detection and reporting (e.g., confirm each flagged smell against the false-positive criteria before including it in the report) to strengthen the workflow.

Dimension	Reasoning	Score
Conciseness	The ~600-line body is noticeably verbose: a full inline example report (~300 lines of five worked smells with before/after code) plus explanation of concepts Claude already knows (SRP, tell-don't-ask, what magic numbers are), matching the verbose-but-accurate anchor rather than the lean one.	2 / 5
Actionability	Provides copy-paste-ready bash commands (find/grep/radon/pylint), a bundled script invocation with arguments, and complete before/after Python examples covering common cases, matching the fully-executable anchor.	5 / 5
Workflow Clarity	A clear six-step sequence (scope, detect, categorize, refactor, report, present) with concrete commands and a false-positive verification lens, but lacks explicit validation checkpoints between steps, placing it just below the top anchor.	4 / 5
Progressive Disclosure	Well-signaled one-level-deep references to real files (smell-patterns.md, refactoring-patterns.md) and a bundled script with clear navigation, but the large inline example report is content that could partly live in a reference file, keeping it below the top anchor.	4 / 5
	Total	15 / 20 Passed

Description

100%Weight 40%Scale 1-5

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 is strong across all dimensions: it states concrete capabilities, names specific smell types, and gives explicit trigger guidance in third person. It is comprehensive without relying on vague language.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ("Identify and report code smells", "Produces markdown reports with severity ratings, locations, descriptions, and specific refactoring recommendations with before/after examples") and enumerates a comprehensive set of smell types, matching the comprehensive-coverage anchor.	5 / 5
Completeness	Explicitly answers both what (identify/report smells, produce markdown reports with severity and refactoring recommendations) and when via concrete "Use when..." and "Triggers when..." clauses, matching the top anchor.	5 / 5
Trigger Term Quality	Provides rich natural trigger phrases users would actually say ("find code smells", "identify design issues", "suggest refactorings", "improve code quality", "conducting code quality audits", "design reviews") including synonyms, matching the comprehensive-coverage anchor.	5 / 5
Distinctiveness Conflict Risk	Targets a clear niche (Python code-smell detection) with named smells and specific triggers, giving it minimal overlap risk with unrelated skills.	5 / 5
	Total	20 / 20 Passed

Validation

93%

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 — 15 / 16 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	15 / 16 Passed

Repository: ArabelaTso/Skills-4-SE
Path: skills/code-smell-detector/SKILL.md
Commit: 4f38503

Reviewed: 7 days ago

Table of Contents

Discovery Implementation Validation

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