Agent skill

Design Smell Detector

by ArabelaTso in ArabelaTso/Skills-4-SE

Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells.

Apache-2.0Auto-check passedDevelopment

Install Design Smell Detector

skills CLI
$ npx skills add ArabelaTso/Skills-4-SE --skill design-smell-detector -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ArabelaTso/Skills-4-SE design-smell-detector --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ArabelaTso/Skills-4-SE.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/design-smell-detector .claude/skills/design-smell-detector && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
design-smell-detector
GitHub stars
253
Token cost
~3.2k tokens
SKILL.md length
846 words
Files
4 (incl. scripts, references)
Skills in repo
170
Repo updated
First seen
Licence
Apache-2.0

At a glance

Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells.

  • Works in 10 steps: Run Detection → Review Results → Prioritize Refactoring → …
  • Reviewing code architecture and design quality
  • SKILL.md covers Quick Start, Design Smells Detected, Detection Workflow and Common Design Smells, plus 4 more sections
  • Runs Python scripts from its folder; calls python and make

What it does

Design Smell Detector is an agent skill from ArabelaTso/Skills-4-SE. Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with…

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/refactoring_strategies.md`, `references/smell_catalog.md` and `scripts/detect_smells.py`).

It sits in Development, covering Refactoring, Design review and critique and Technical debt. The repository describes itself as: A curated list of 180+ useful Claude Skills for Software Engineering and resources for customizing AI for SE workflows. The licence is Apache-2.0.

When your agent uses it

  • Reviewing code architecture and design quality
  • Identifying refactoring opportunities
  • Detecting God classes
  • Classes with too many responsibilities

Example prompts

  • “/design-smell-detector”

Requirements

  • Python 3

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Run Detection
  2. Review Results
  3. Prioritize Refactoring
  4. Apply Refactoring
  5. Verify Improvements
  6. Detect Regularly
  7. Track Metrics Over Time
  8. Prioritize Strategically
  9. Refactor Incrementally
  10. Measure Improvements

What it can do on your machine

Read from SKILL.md and the folder at commit 4f38503. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • make

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Design Smell Detector loads about 3.2k tokens when it runs, and up to ~8.7k if it reads all its reference files. Until then it costs about 144 tokens; SKILL.md has 846 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~144
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ArabelaTso/Skills-4-SE at commit 4f38503, republished under its Apache-2.0 licence (© ArabelaTso). 846 words, ~3,221 tokens.

Download SKILL.mdSave it as .claude/skills/design-smell-detector/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
design-smell-detector
description
Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.

Design Smell Detector

Identify and address design quality issues in code through automated smell detection and refactoring guidance.

Quick Start

Detect Design Smells
bash
# Analyze a single file
python scripts/detect_smells.py src/app.py

# Analyze entire directory
python scripts/detect_smells.py src/

# Output as JSON
python scripts/detect_smells.py src/ --format json
Example Output
Found 5 design smell(s): 1 critical, 3 major, 1 minor

CRITICAL ISSUES:
  🔴 src/services.py:45 - God Class
     Class 'UserManager' has 25 methods (threshold: 20)
     💡 Split into multiple smaller, focused classes

MAJOR ISSUES:
  🟠 src/models.py:120 - Low Cohesion
     Class 'Order' has low cohesion (score: 0.25)
     💡 Group related methods/attributes or split class

  🟠 src/utils.py:89 - Long Method
     Method 'process_order' has 67 lines (threshold: 50)
     💡 Extract smaller methods or refactor

Design Smells Detected

Coupling Smells

High Coupling: Too many dependencies

  • Module imports > 20
  • Constructor dependencies > 10
  • Changes cascade across classes

Feature Envy: Method uses other class more than own

  • External access > internal access
  • Method should move to envied class

Inappropriate Intimacy: Classes too tightly coupled

  • Accessing private fields of other classes
  • Excessive use of getters/setters
Cohesion Smells

Low Cohesion: Class members unrelated

  • LCOM (Lack of Cohesion) > 0.7
  • Methods don't share instance variables
  • Class has multiple responsibilities

God Class: Class knows/does too much

  • Methods > 20
  • Attributes > 15
  • Lines of code > 500
Complexity Smells

High Cyclomatic Complexity: Too many decision points

  • Complexity > 10
  • Deeply nested conditionals
  • Many if/else, loops

Long Method: Method too long

  • Lines of code > 50
  • Multiple responsibilities
  • Hard to understand
Size Smells

Long Parameter List: Too many parameters

  • Parameters > 5
  • Related parameters not grouped
  • Method signature hard to understand

Large Module: Module too large

  • Classes > 20
  • Lines of code > 1000
  • Multiple responsibilities
Encapsulation Smells

Data Class: Only data, no behavior

  • Only getters/setters
  • No business logic
  • Missing encapsulation

Exposed Internal State: Implementation details exposed

  • Public mutable fields
  • Returns references to internal collections
  • Breaks encapsulation

Detection Workflow

1. Run Detection

Analyze codebase for design smells:

bash
python scripts/detect_smells.py src/
2. Review Results

Examine detected smells by severity:

  • Critical: God classes, severe coupling issues
  • Major: Low cohesion, long methods, high complexity
  • Minor: Long parameter lists, feature envy
3. Prioritize Refactoring

Focus on:

  • Critical issues first
  • Frequently changed code
  • High-impact, low-effort improvements
4. Apply Refactoring

Use refactoring strategies based on smell type.

See refactoring_strategies.md for detailed solutions.

5. Verify Improvements

Re-run detection to measure progress:

bash
python scripts/detect_smells.py src/

Compare metrics before/after.

Common Design Smells

God Class

Symptoms:

  • Too many methods (>20)
  • Too many attributes (>15)
  • Low cohesion
  • Multiple responsibilities

Example:

python
# ❌ God Class
class Application:
    # 30+ methods handling:
    # - User management
    # - Order processing
    # - Payment handling
    # - Reporting
    # - Email notifications
    # - File operations
    pass

Refactoring:

python
# ✅ Split by responsibility
class UserManager:
    pass

class OrderProcessor:
    pass

class PaymentHandler:
    pass

class ReportGenerator:
    pass
High Coupling

Symptoms:

  • Too many imports (>20)
  • Too many constructor dependencies
  • Changes cascade across classes

Example:

python
# ❌ High coupling
class UserService:
    def __init__(self):
        self.db = Database()
        self.cache = Cache()
        self.logger = Logger()
        self.validator = Validator()
        self.email = EmailService()
        self.sms = SMSService()
        # ... many more

Refactoring:

python
# ✅ Dependency injection
class UserService:
    def __init__(self, db, logger, notifier):
        self.db = db
        self.logger = logger
        self.notifier = notifier  # Abstraction
Low Cohesion

Symptoms:

  • Methods don't share attributes
  • Class does unrelated things
  • LCOM score > 0.7

Example:

python
# ❌ Low cohesion
class UserManager:
    def create_user(self):
        pass

    def send_email(self):
        pass

    def log_activity(self):
        pass

    def calculate_discount(self):
        pass

Refactoring:

python
# ✅ High cohesion - focused classes
class UserRepository:
    def create_user(self):
        pass

class EmailService:
    def send_email(self):
        pass

class ActivityLogger:
    def log_activity(self):
        pass
Long Method

Symptoms:

  • Method > 50 lines
  • Multiple responsibilities
  • Hard to understand

Example:

python
# ❌ Long method (100+ lines)
def process_order(order):
    # Validate (20 lines)
    # Calculate price (15 lines)
    # Save to DB (10 lines)
    # Send email (20 lines)
    # Update stats (10 lines)
    # Log activity (15 lines)
    pass

Refactoring:

python
# ✅ Extract methods
def process_order(order):
    validate_order(order)
    total = calculate_total(order)
    save_order(order, total)
    send_confirmation(order)
    update_statistics()
    log_activity(order)

Refactoring Strategies

Reduce Coupling

Dependency Injection:

python
# Inject dependencies instead of creating them
class OrderService:
    def __init__(self, db, logger):
        self.db = db
        self.logger = logger

Interface Abstraction:

python
# Depend on abstractions, not implementations
from abc import ABC, abstractmethod

class PaymentGateway(ABC):
    @abstractmethod
    def charge(self, amount):
        pass

class PaymentProcessor:
    def __init__(self, gateway: PaymentGateway):
        self.gateway = gateway
Improve Cohesion

Extract Class:

python
# Split into focused classes
class User:
    # User data and behavior

class UserRepository:
    # Database operations

class EmailService:
    # Email operations

Move Method:

python
# Move method to appropriate class
class Account:
    def get_formatted_balance(self):  # Moved from reporter
        return f"${self.balance:.2f}"
Reduce Complexity

Extract Method:

python
# Break complex method into smaller ones
def process_order(order):
    validate_order(order)
    calculate_total(order)
    save_order(order)

Replace Conditional with Polymorphism:

python
# Use inheritance instead of conditionals
class Customer:
    def calculate_price(self, base_price):
        return base_price

class PremiumCustomer(Customer):
    def calculate_price(self, base_price):
        return base_price * 0.9

For comprehensive refactoring strategies, see refactoring_strategies.md.

Metrics and Thresholds

Detection Thresholds
SmellMetricThreshold
God ClassMethods> 20
God ClassAttributes> 15
God ClassLOC> 500
High CouplingImports> 20
Low CohesionLCOM> 0.7
Long MethodLOC> 50
High ComplexityCyclomatic> 10
Long Parameter ListParameters> 5
Key Metrics

LCOM (Lack of Cohesion of Methods):

  • Measures how methods share instance variables
  • Range: 0.0 (high cohesion) to 1.0 (low cohesion)
  • Lower is better

Cyclomatic Complexity:

  • Number of linearly independent paths
  • Each if/while/for adds 1
  • Lower is better (< 10)

Fan-out (Coupling):

  • Number of dependencies
  • Lower is better (< 10)

Design Smell Catalog

For complete descriptions, examples, and solutions for all design smells, see smell_catalog.md.

Includes:

  • Coupling Smells: High coupling, Feature Envy, Inappropriate Intimacy
  • Cohesion Smells: Low cohesion, God Class
  • Complexity Smells: High complexity, Long Method
  • Size Smells: Long Parameter List, Large Module
  • Encapsulation Smells: Data Class, Exposed Internal State
Show full SKILL.md (326 more words)Show less

Best Practices

1. Detect Regularly

Integrate into workflow:

bash
# Pre-commit hook
python scripts/detect_smells.py src/

# CI/CD pipeline
make check-design-smells
2. Track Metrics Over Time

Monitor trends:

Sprint 1: 15 God classes, 45 long methods
Sprint 2: 12 God classes, 38 long methods
Sprint 3: 8 God classes, 25 long methods
3. Prioritize Strategically

Focus on:

  • Code changed frequently
  • Critical business logic
  • High-impact areas
4. Refactor Incrementally
  • Small, safe changes
  • Run tests after each change
  • Commit frequently
5. Measure Improvements

Before/after metrics:

Before: LCOM = 0.85 (low cohesion)
After:  LCOM = 0.25 (high cohesion)

Common Scenarios

Scenario 1: Legacy Codebase Modernization

Goal: Identify technical debt in legacy system

Approach:

  1. Run smell detection on entire codebase
  2. Generate metrics report
  3. Identify top 10 worst files
  4. Create refactoring backlog
  5. Tackle incrementally
Scenario 2: Code Review Enhancement

Goal: Automated design quality checks in PR reviews

Approach:

  1. Add smell detection to CI pipeline
  2. Fail build if critical smells introduced
  3. Report metrics in PR comments
  4. Require fixes before merge
Scenario 3: Architecture Assessment

Goal: Evaluate system architecture quality

Approach:

  1. Detect coupling and cohesion issues
  2. Identify God classes and large modules
  3. Analyze dependency structure
  4. Recommend architectural improvements

Integration with Development Workflow

Pre-commit Hook
bash
#!/bin/bash
# .git/hooks/pre-commit

python scripts/detect_smells.py src/
if [ $? -ne 0 ]; then
    echo "Critical design smells detected. Commit aborted."
    exit 1
fi
CI/CD Pipeline
yaml
# .github/workflows/quality.yml
name: Design Quality Check

on: [push, pull_request]

jobs:
  check-smells:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Detect design smells
        run: python scripts/detect_smells.py src/

      - name: Upload report
        uses: actions/upload-artifact@v3
        with:
          name: smell-report
          path: smell-report.json
IDE Integration

Most IDEs support similar analysis through plugins:

  • PyCharm: Built-in inspections for coupling/cohesion
  • VS Code: Python linting extensions
  • SonarLint: Real-time smell detection

Troubleshooting

False Positives

Problem: Legitimate design flagged as smell

Solution:

  • Understand context and constraints
  • Consider if threshold should be adjusted
  • Document reasons for exception
Overwhelming Results

Problem: Too many smells to address

Solution:

  • Filter by severity (critical first)
  • Focus on frequently changed code
  • Set incremental goals
Refactoring Breaks Tests

Problem: Tests fail after refactoring

Solution:

  • Ensure comprehensive test coverage first
  • Refactor incrementally
  • Run tests after each small change

Reference Documentation

Design Smell Catalog

See smell_catalog.md for:

  • Complete smell definitions
  • Detection criteria and thresholds
  • Code examples (before/after)
  • Metrics explanations (LCOM, cyclomatic complexity, fan-out)
  • All coupling, cohesion, complexity, size, and encapsulation smells
Refactoring Strategies

See refactoring_strategies.md for:

  • Coupling reduction strategies (DI, interfaces, facades)
  • Cohesion improvement (extract class, move method)
  • Complexity reduction (extract method, polymorphism)
  • Size reduction (split classes, parameter objects)
  • Encapsulation improvement
  • Refactoring workflow and best practices
  • Anti-patterns to avoid
  • Success metrics

© ArabelaTso, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/design-smell-detector of ArabelaTso/Skills-4-SE.

  • SKILL.md
  • references/refactoring_strategies.md
  • references/smell_catalog.md
  • scripts/detect_smells.py

Open the folder on GitHubat commit 4f38503

Compare with similar skills

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Code Refactoring Workflowluongnv89/claude-howto42k—~3.1kAutomated safety check: PassMIT
FIXME Resolvertailcallhq/forgecode7.6k—~1.1kAutomated safety check: PassApache-2.0

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Categories

Questions about Design Smell Detector

What does Design Smell Detector do?

Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Design Smell Detector is an agent skill from ArabelaTso/Skills-4-SE. Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells.

When should I use Design Smell Detector?

Design Smell Detector fits situations like: reviewing code architecture and design quality; identifying refactoring opportunities; detecting God classes; classes with too many responsibilities.

How do I install Design Smell Detector in Claude Code?

Run `npx skills add ArabelaTso/Skills-4-SE --skill design-smell-detector -a claude-code`. Or copy the skill folder (skills/design-smell-detector in ArabelaTso/Skills-4-SE) into .claude/skills/design-smell-detector in your project. Claude Code loads it when a task matches its description.

How do I install Design Smell Detector in Codex?

Run `npx skills add ArabelaTso/Skills-4-SE --skill design-smell-detector -a codex`. Or copy the skill folder (skills/design-smell-detector in ArabelaTso/Skills-4-SE) into .agents/skills/design-smell-detector in your project. Codex loads it when a task matches its description.

Can I use Design Smell Detector in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArabelaTso/Skills-4-SE --skill design-smell-detector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/design-smell-detector, .gemini/skills/design-smell-detector, .github/skills/design-smell-detector and .opencode/skills/design-smell-detector in your project.

What does Design Smell Detector need to run?

Going by SKILL.md and its folder, Design Smell Detector needs Python for the scripts in its folder and the command-line tools its instructions call (python and make). Our summary lists: Python 3.

Does Design Smell Detector access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Design Smell Detector safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Design Smell Detector use?

Design Smell Detector is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Design Smell Detector use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.5k tokens, read only when the agent opens those files.

What are the alternatives to Design Smell Detector?

Skills that share tags, products or a category with Design Smell Detector: Improve App (wondelai/skills, 2.4k stars), Systematic Code Refactoring (luongnv89/claude-howto, 42k stars), Code Simplification for ego-lite (citrolabs/ego-lite, 17k stars) and Code Refactoring Workflow (luongnv89/claude-howto, 42k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Design Smell Detector?

ArabelaTso (a GitHub user) maintains it in ArabelaTso/Skills-4-SE, which has 253 GitHub stars. The repository holds 170 skills in this directory. The repository was last updated on August 21, 2026.

Source: ArabelaTso/Skills-4-SE on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.