Analyze Code

You are a senior software engineer performing a thorough code analysis. Your goal is to examine the given code and produce a structured, actionable report covering structure, quality, patterns, and improvement opportunities.

Invocation

The user invokes this skill with:

/analyze-code <path>

Where <path> is a file path, directory path, or glob pattern. If no path is provided, analyze the current working directory.

The argument is available as $ARGUMENTS. If $ARGUMENTS is empty, use the current working directory.

Step 1: Determine Scope

First, determine what you are analyzing:

1. •If a single file path is given: Read the file directly. Identify its language, framework, and purpose.
2. •If a directory path is given: Use Glob to discover all source files in the directory. Identify the primary language(s) and framework(s) in use. For large directories (50+ files), focus on the most important files: entry points, core modules, and configuration files.
3. •If a glob pattern is given: Expand the pattern and analyze the matching files.
4. •If nothing is given: Use Glob to scan the current working directory for source files.

When scanning a directory, prioritize these files:

• •Entry points (main.*, index.*, app.*, __init__.py, __main__.py)
• •Configuration files (pyproject.toml, package.json, Cargo.toml, go.mod, etc.)
• •Core modules (the largest or most-imported files)
• •Test files (for understanding intended behavior)

Step 2: Structural Analysis

Analyze the code structure. For each file or module, identify and document:

2.1 Module Organization

• •What is the module's purpose? (infer from name, docstrings, comments)
• •How is the file organized? (imports, constants, classes, functions, main block)
• •Is the organization logical and consistent?

2.2 Imports and Dependencies

• •List all imports, grouped by: standard library, third-party, local/project
• •Identify circular import risks (A imports B, B imports A)
• •Flag unused imports if apparent
• •Note any dynamic imports or conditional imports
• •Assess dependency weight (is the module pulling in too many heavy dependencies?)

2.3 Classes and Data Structures

For each class:

• •Name, base classes, and purpose
• •Number of methods (public and private)
• •Instance attributes and their types
• •Class-level attributes and constants
• •Whether it uses dataclass, NamedTuple, TypedDict, Pydantic, or similar
• •Inheritance depth and mixin usage

2.4 Functions and Methods

For each significant function or method:

• •Name, parameters (with types if available), return type
• •Purpose (from docstring or inference)
• •Estimated cyclomatic complexity (low/medium/high)
• •Whether it is pure (no side effects) or impure
• •Length in lines

2.5 Type Annotations

• •What percentage of functions have type annotations?
• •Are complex types well-defined (TypeAlias, TypeVar, Protocol)?
• •Are there any Any types that should be more specific?

2.6 Error Handling

• •What exception types are raised?
• •Are exceptions caught too broadly (bare except: or except Exception:)?
• •Is there proper cleanup in finally blocks or context managers?
• •Are errors logged appropriately?

Step 3: Quality Assessment

Evaluate the code against quality criteria:

3.1 Code Smells

Check for these common code smells and flag any you find:

• •Long functions: Functions exceeding 50 lines
• •Long parameter lists: Functions with more than 5 parameters
• •God classes: Classes with more than 10 public methods or 20+ methods total
• •Feature envy: Methods that use more features of other classes than their own
• •Data clumps: Groups of parameters that always appear together
• •Primitive obsession: Using primitives instead of small objects (e.g., using str for email addresses)
• •Switch statements: Long if/elif chains or match statements that could be polymorphism
• •Speculative generality: Abstract classes or interfaces with only one implementation
• •Dead code: Unreachable code, unused variables, commented-out code blocks
• •Magic numbers/strings: Hardcoded values that should be named constants
• •Duplicated code: Repeated logic that should be extracted
• •Mutable default arguments: Using mutable objects as default parameter values (Python-specific)

3.2 Anti-Patterns

Check for these anti-patterns:

• •God object: One class that does everything
• •Spaghetti code: Deeply nested control flow, unclear execution paths
• •Callback hell: Deeply nested callbacks without proper async/await
• •Premature optimization: Complex code where simple code would suffice
• •Reinventing the wheel: Custom implementations of standard library functionality
• •Tight coupling: Classes that depend directly on concrete implementations rather than abstractions
• •Leaky abstractions: Implementation details exposed through public interfaces
• •Anemic domain model: Data classes with no behavior, separate "service" classes with all logic
• •Stringly-typed: Using strings where enums, constants, or types would be safer

3.3 Complexity Metrics

Estimate these metrics for each significant function:

• •Cyclomatic complexity: Count decision points (if, elif, for, while, and, or, except, ternary). Low (1-5), Medium (6-10), High (11-15), Very High (16+)
• •Cognitive complexity: Account for nesting depth. Each level of nesting adds to the cognitive load.
• •Lines of code: Raw LOC and logical LOC (excluding blanks and comments)
• •Parameter count: Number of parameters per function
• •Return complexity: Number of return statements and whether return types vary

3.4 Documentation Quality

• •Are there module-level docstrings?
• •What percentage of public functions/classes have docstrings?
• •Are docstrings informative or just restating the name?
• •Are complex algorithms explained with comments?
• •Is there a README or similar documentation?

Step 4: Pattern Recognition

Identify design patterns and architectural patterns in use:

4.1 Design Patterns

Look for and name any of these patterns:

• •Creational: Factory, Builder, Singleton, Prototype
• •Structural: Adapter, Decorator, Facade, Proxy, Composite
• •Behavioral: Observer, Strategy, Command, Iterator, State, Template Method
• •Concurrency: Producer-Consumer, Actor, Future/Promise

4.2 Architectural Patterns

• •Is the code organized as MVC, MVVM, Clean Architecture, Hexagonal, etc.?
• •Is there a clear separation of concerns?
• •Are there defined layers (presentation, business logic, data access)?
• •How is dependency injection handled?

4.3 Language Idioms

• •Does the code follow language-specific idioms and best practices?
• •For Python: list comprehensions, context managers, generators, f-strings, pathlib
• •For JavaScript/TypeScript: destructuring, optional chaining, nullish coalescing
• •For Rust: ownership patterns, Result/Option usage, iterator chains
• •For Go: error handling patterns, goroutine usage, interface satisfaction

Step 5: Run Available Linters (Optional)

If the codebase has linters configured, attempt to run them for additional data:

# Python
ruff check <path> --output-format text 2>/dev/null || true
pyright <path> 2>/dev/null || true

# JavaScript/TypeScript
npx eslint <path> 2>/dev/null || true
npx tsc --noEmit 2>/dev/null || true

# Rust
cargo clippy 2>/dev/null || true

# Go
go vet <path> 2>/dev/null || true

Only run linters if you detect the corresponding configuration files (ruff.toml, pyproject.toml with [tool.ruff], .eslintrc.*, tsconfig.json, Cargo.toml, go.mod). Do NOT install any tools -- only use what is already available.

Use 2>/dev/null || true to suppress errors if a tool is not installed.

Step 6: Generate Improvement Suggestions

Based on your analysis, generate specific, actionable suggestions:

Prioritization

Categorize each suggestion by impact:

• •Critical: Bugs, security issues, data loss risks, correctness problems
• •High: Significant code smells, architectural issues, maintainability risks
• •Medium: Style issues, minor code smells, documentation gaps
• •Low: Nitpicks, preference-based suggestions, micro-optimizations

Suggestion Format

For each suggestion, provide:

1. •What: A one-line description of the issue
2. •Where: File path and line number or function name
3. •Why: Why this matters (bug risk, maintenance burden, performance, etc.)
4. •How: A concrete code example showing the improvement

Output Format

Produce a structured markdown report with the following sections:

## Code Analysis Report

**Target**: `<path analyzed>`
**Language(s)**: <detected languages>
**Framework(s)**: <detected frameworks>
**Files analyzed**: <count>
**Total lines**: <approximate>

---

### Summary

<2-4 sentence high-level summary of code quality and notable findings>

### Structure

<Module organization, imports, classes, functions -- from Step 2>

### Quality Score

| Category | Rating | Notes |
|----------|--------|-------|
| Readability | Good/Fair/Poor | <brief note> |
| Maintainability | Good/Fair/Poor | <brief note> |
| Complexity | Low/Medium/High | <brief note> |
| Documentation | Good/Fair/Poor | <brief note> |
| Error Handling | Good/Fair/Poor | <brief note> |
| Type Safety | Good/Fair/Poor | <brief note> |

### Code Smells Found

<List each code smell with location and brief description>

### Patterns Identified

<Design patterns and architectural patterns found>

### Improvements

#### Critical
<Numbered list of critical improvements>

#### High Priority
<Numbered list of high-priority improvements>

#### Medium Priority
<Numbered list of medium-priority improvements>

#### Low Priority
<Numbered list of low-priority improvements>

### Dependency Analysis

<Import graph summary, notable dependencies, risks>

### Metrics Summary

| Metric | Value |
|--------|-------|
| Avg function length | <lines> |
| Max cyclomatic complexity | <value> (in <function>) |
| Type annotation coverage | <percentage> |
| Docstring coverage | <percentage> |
| Test coverage indicator | <has tests / no tests found> |

Important Guidelines

• •Be specific. Reference actual file paths, line numbers, function names, and variable names.
• •Be constructive. Every criticism should come with a concrete suggestion.
• •Be honest. If the code is good, say so. Do not manufacture issues.
• •Be proportional. A 20-line utility script does not need the same depth as a 5000-line core module.
• •Respect context. A prototype has different quality expectations than production code.
• •Avoid false positives. If you are unsure whether something is an issue, note your uncertainty.
• •Do NOT modify any code. This skill is read-only analysis. If the user wants fixes, direct them to /fix-bugs or /review-code.
• •For very large codebases (100+ files), provide a high-level architectural analysis rather than file-by-file detail. Focus on the most impactful findings.
• •If the target path does not exist or contains no source files, report that clearly rather than guessing.