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Code Quality

  • 51 installs
  • 7 repo stars
  • Updated January 15, 2026
  • eyadsibai/ltk

Helps with ai & agent building tasks.

About

code quality is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • code quality
  • AI & Agent Building
  • AI-coding skill

Code Quality by the numbers

  • 51 all-time installs (skills.sh)
  • Ranked #7,219 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Jul 30, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs51
repo stars7
Last updatedJanuary 15, 2026
Repositoryeyadsibai/ltk

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Code Quality Analysis

Comprehensive code quality analysis skill covering style, complexity, dead code detection, and type checking.

Core Capabilities

Style Compliance

Check adherence to language-specific style guides:

Python (PEP8/Black):

  • Line length (88 chars for Black, 79 for PEP8)
  • Indentation (4 spaces)
  • Import ordering (standard, third-party, local)
  • Naming conventions (snake_case for functions/variables, PascalCase for classes)
  • Whitespace usage

Style check commands:

# Black formatting check
black --check --diff .

# Flake8 linting
flake8 --max-line-length=88 .

# isort import sorting
isort --check-only --diff .

Complexity Metrics

Measure and report code complexity:

Cyclomatic Complexity:

  • Number of independent paths through code
  • Target: < 10 per function
  • Warning: 10-20
  • Critical: > 20

Cognitive Complexity:

  • Mental effort to understand code
  • Accounts for nesting depth and control flow

Function Length:

  • Target: < 50 lines
  • Warning: 50-100 lines
  • Critical: > 100 lines

Nesting Depth:

  • Target: < 4 levels
  • Warning: 4-6 levels
  • Critical: > 6 levels

Complexity analysis:

# Using radon for Python
radon cc . -a -s  # Cyclomatic complexity
radon mi .        # Maintainability index
radon hal .       # Halstead metrics

Dead Code Detection

Identify unused code elements:

Unused Imports:

# Python - using autoflake
autoflake --check --remove-all-unused-imports .

# Using flake8 with F401
flake8 --select=F401 .

Unused Variables:

  • Local variables assigned but never read
  • Function parameters ignored
  • Class attributes never accessed

Unused Functions/Classes:

  • Defined but never called
  • Private methods not used internally
  • Dead code branches (always false conditions)

Unreachable Code:

  • Code after return/raise/break/continue
  • Branches with impossible conditions
  • Deprecated code still in codebase

Type Hint Analysis

Validate type annotations:

Missing Type Hints:

# Using mypy
mypy --strict .

# Check specific strictness levels
mypy --disallow-untyped-defs .
mypy --disallow-incomplete-defs .

Type Errors:

  • Incompatible types in assignments
  • Wrong argument types
  • Missing return types
  • Generic type issues

Type Coverage:

  • Percentage of code with type annotations
  • Target: > 80% coverage

Quality Metrics Dashboard

When analyzing a codebase, report:

MetricValueTargetStatus
Style ComplianceX%> 95%Pass/Fail
Avg Cyclomatic ComplexityX< 10Pass/Fail
Max Function LengthX lines< 50Pass/Fail
Dead CodeX items0Pass/Fail
Type CoverageX%> 80%Pass/Fail

Analysis Workflow

Full Quality Analysis

To perform comprehensive quality check:

1. Style check: Run linters and formatters 2. Complexity analysis: Calculate metrics 3. Dead code scan: Find unused elements 4. Type check: Validate annotations 5. Report generation: Summarize findings

Quick Quality Check

For rapid assessment of changes:

1. Get changed files from git diff 2. Run focused linting on changed files 3. Check complexity of modified functions 4. Validate types in touched code

Language-Specific Guidance

Python

Recommended tooling:

  • Black (formatting)
  • isort (import sorting)
  • flake8 (linting)
  • mypy (type checking)
  • radon (complexity)
  • vulture (dead code)

Configuration (pyproject.toml):

[tool.black]
line-length = 88
target-version = ['py311']

[tool.isort]
profile = "black"
line_length = 88

[tool.mypy]
python_version = "3.11"
strict = true

JavaScript/TypeScript

Recommended tooling:

  • ESLint (linting)
  • Prettier (formatting)
  • TypeScript compiler (type checking)

Configuration (.eslintrc.json):

{
  "extends": ["eslint:recommended"],
  "rules": {
    "complexity": ["warn", 10],
    "max-depth": ["warn", 4],
    "max-lines-per-function": ["warn", 50]
  }
}

Common Quality Issues

High Priority Fixes

1. Unused imports: Remove immediately 2. Type errors: Fix type mismatches 3. High complexity: Refactor into smaller functions 4. Long functions: Extract logical blocks

Medium Priority

1. Style violations: Apply formatter 2. Missing type hints: Add annotations 3. Moderate complexity: Consider refactoring 4. Unused variables: Remove or use

Low Priority

1. Minor style preferences: Team decision 2. Optional type hints: Add gradually 3. Documentation gaps: Address incrementally

Output Format

Present findings organized by severity:

Errors (must fix):

  • Type errors
  • Syntax issues
  • Critical complexity

Warnings (should fix):

  • Unused code
  • High complexity
  • Missing types in public APIs

Info (consider):

  • Style suggestions
  • Optimization opportunities
  • Best practice recommendations

Integration

Coordinate with other skills:

  • refactoring skill: For complexity reduction
  • documentation skill: For missing docstrings
  • security-scanning skill: For security-related quality issues

Related skills

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