refactoring

---

name: refactoring description: | Linter-driven refactoring patterns to reduce complexity and improve code quality. Use when linter fails with complexity issues (cyclomatic, cognitive, maintainability) or when code feels hard to read/maintain. Applies storifying, type extraction, and function extraction patterns. allowed-tools:

• Skill(go-linter-driven-development:code-designing)
• Skill(go-linter-driven-development:testing)
• Skill(go-linter-driven-development:pre-commit-review)

---

Reference: See reference.md for complete decision tree and all patterns. Examples: See examples.md for real-world refactoring case studies.

<quick_start>

1. Receive linter failures from @linter-driven-development
2. Analyze root cause - Does it read like a story? Can it be broken down?
3. Apply patterns in priority order (early returns → extract function → storifying → extract type)
4. Verify - Re-run linter automatically
5. Iterate until linter passes

IMPORTANT: This skill operates autonomously - no user confirmation needed. </quick_start>

<when_to_use>

• Automatically invoked by @linter-driven-development when linter fails
• Automatically invoked by @pre-commit-review when design issues detected
• Complexity failures: cyclomatic, cognitive, maintainability index
• Architectural failures: noglobals, gochecknoinits, gochecknoglobals
• Design smell failures: dupl (duplication), goconst (magic strings), ineffassign
• Functions > 50 LOC or nesting > 2 levels
• Mixed abstraction levels in functions
• Manual invocation when code feels hard to read/maintain </when_to_use>

<learning_resources> Choose your learning path:

• Quick Start: Use the patterns below for common refactoring cases
• Complete Reference: See reference.md for full decision tree and all patterns
• Real-World Examples: See examples.md to learn the refactoring thought process
  • Example 1: Storifying and extracting a single leaf type
  • Example 2: Primitive obsession with multiple types and switch elimination </learning_resources>

<analysis_phase> Before applying any refactoring patterns, automatically analyze the context:

<system_context_analysis> AUTOMATICALLY ANALYZE:

1. Find all callers of the failing function
2. Identify which flows/features depend on it
3. Determine primary responsibility
4. Check for similar functions revealing patterns
5. Spot potential refactoring opportunities </system_context_analysis>

<type_discovery> Proactively identify hidden types in the code:

POTENTIAL TYPES TO DISCOVER:

1. Data being parsed from strings → Parse* types Example: ParseCommandResult(), ParseLogEntry()

2. Scattered validation logic → Validated types Example: Email, Port, IPAddress types

3. Data that always travels together → Aggregate types Example: UserCredentials, ServerConfig

4. Complex conditions → State/status types Example: DeploymentStatus with IsReady(), CanProceed()

5. Repeated string manipulation → Types with methods Example: FilePath with Dir(), Base(), Ext() </type_discovery>

<analysis_output> The analysis produces a refactoring plan identifying:

• Function's role in the system
• Potential domain types to extract
• Recommended refactoring approach
• Expected complexity reduction </analysis_output> </analysis_phase>

<refactoring_signals> <linter_failures> Complexity Issues:

• Cyclomatic Complexity: Too many decision points → Extract functions, simplify logic
• Cognitive Complexity: Hard to understand → Storifying, reduce nesting
• Maintainability Index: Hard to maintain → Break into smaller pieces

Architectural Issues:

• noglobals/gochecknoglobals: Global variable usage → Dependency rejection pattern
• gochecknoinits: Init function usage → Extract initialization logic
• Static/singleton patterns: Hidden dependencies → Inject dependencies

Design Smells:

• dupl: Code duplication → Extract common logic/types
• goconst: Magic strings/numbers → Extract constants or types
• ineffassign: Ineffective assignments → Simplify logic </linter_failures>

<code_smells>

• Functions > 50 LOC
• Nesting > 2 levels
• Mixed abstraction levels
• Unclear flow/purpose
• Primitive obsession
• Global variable access scattered throughout code </code_smells> </refactoring_signals>

<automation_flow> This skill operates completely autonomously once invoked:

<iteration_loop> AUTOMATED PROCESS:

1. Receive trigger:
   • From @linter-driven-development (linter failures)
   • From @pre-commit-review (design debt/readability debt)
2. Apply refactoring pattern (start with least invasive)
3. Run linter immediately (no user confirmation)
4. If linter still fails OR review finds more issues:
   • Try next pattern in priority order
   • Repeat until both linter and review pass
5. If patterns exhausted and still failing:
   • Report what was tried
   • Suggest file splitting or architectural changes </iteration_loop>

<pattern_priority> For Complexity Failures (cyclomatic, cognitive, maintainability):

1. Early Returns → Reduce nesting quickly
2. Extract Function → Break up long functions
3. Storifying → Improve abstraction levels
4. Extract Type → Create domain types (only if "juicy")
5. Switch Extraction → Categorize switch cases

For Architectural Failures (noglobals, singletons):

1. Dependency Rejection → Incremental bottom-up approach
2. Extract Type with dependency injection
3. Push global access up call chain one level
4. Iterate until globals only at entry points (main, handlers)

For Design Smells (dupl, goconst):

1. Extract Type → For repeated values or validation
2. Extract Function → For code duplication
3. Extract Constant → For magic strings/numbers </pattern_priority>

<no_manual_intervention>

• NO asking for confirmation between patterns
• NO waiting for user input
• NO manual linter runs
• AUTOMATIC progression through patterns
• ONLY report results at the end </no_manual_intervention> </automation_flow>

<refactoring_patterns>

// AFTER - Story-like func ProcessOrder(order Order) error { if err := validateOrder(order); err != nil { return err } if err := saveToDatabase(order); err != nil { return err } return notifyCustomer(order) }

func validateOrder(order Order) error { /* ... / } func saveToDatabase(order Order) error { / ... / } func notifyCustomer(order Order) error { / ... */ }

</pattern>

<pattern name="extract_type" signal="Primitive obsession or unstructured data">
<juiciness_test version="2">
**BEHAVIORAL JUICINESS** (rich behavior):
- Complex validation rules (regex, ranges, business rules)
- Multiple meaningful methods (≥2 methods)
- State transitions or transformations
- Format conversions (different representations)

**STRUCTURAL JUICINESS** (organizing complexity):
- Parsing unstructured data into fields
- Grouping related data that travels together
- Making implicit structure explicit
- Replacing map[string]interface{} with typed fields

**USAGE JUICINESS** (simplifies code):
- Used in multiple places
- Significantly simplifies calling code
- Makes tests cleaner and more focused

**Score**: Need "yes" in at least ONE category to create the type
</juiciness_test>

```go
// NOT JUICY - Don't create type
func ValidateUserID(id string) error {
    if id == "" {
        return errors.New("empty id")
    }
    return nil
}
// Just use: if userID == ""

// JUICY (Behavioral) - Complex validation
type Email string

func ParseEmail(s string) (Email, error) {
    if s == "" {
        return "", errors.New("empty email")
    }
    if !emailRegex.MatchString(s) {
        return "", errors.New("invalid format")
    }
    if len(s) > 255 {
        return "", errors.New("too long")
    }
    return Email(s), nil
}

func (e Email) Domain() string { /* extract domain */ }
func (e Email) LocalPart() string { /* extract local */ }

// JUICY (Structural) - Parsing complex data
type CommandResult struct {
    FailedFiles  []string
    SuccessFiles []string
    Message      string
    ExitCode     int
}

func ParseCommandResult(output string) (CommandResult, error) {
    // Parse unstructured output into structured fields
}

Warning Signs of Over-Engineering:

• Type with only one trivial method
• Simple validation (just empty check)
• Type that's just a wrapper without behavior
• Good variable naming would be clearer

See Example 2 for complete case study.

// AFTER - Extracted functions func CreateUser(data map[string]interface{}) error { user, err := validateAndParseUser(data) if err != nil { return err } if err := saveUser(user); err != nil { return err } if err := sendWelcomeEmail(user); err != nil { return err } logUserCreation(user) return nil }

</pattern>

<pattern name="early_returns" signal="Deep nesting > 2 levels">
```go
// BEFORE - Deeply nested
func ProcessItem(item Item) error {
    if item.IsValid() {
        if item.IsReady() {
            if item.HasPermission() {
                // Process
                return nil
            } else {
                return errors.New("no permission")
            }
        } else {
            return errors.New("not ready")
        }
    } else {
        return errors.New("invalid")
    }
}

// AFTER - Early returns
func ProcessItem(item Item) error {
    if !item.IsValid() {
        return errors.New("invalid")
    }
    if !item.IsReady() {
        return errors.New("not ready")
    }
    if !item.HasPermission() {
        return errors.New("no permission")
    }
    // Process
    return nil
}

// AFTER - Extracted handlers func HandleRequest(reqType string, data interface{}) error { switch reqType { case "create": return handleCreate(data) case "update": return handleUpdate(data) case "delete": return handleDelete(data) default: return errors.New("unknown type") } }

func handleCreate(data interface{}) error { /* ... / } func handleUpdate(data interface{}) error { / ... / } func handleDelete(data interface{}) error { / ... */ }

</pattern>

<pattern name="dependency_rejection" signal="noglobals linter fails or global/singleton usage">
**Goal**: Create "islands of clean code" by incrementally pushing dependencies up the call chain

**Strategy**: Work from bottom-up, rejecting dependencies one level at a time
- DON'T do massive refactoring all at once
- Start at deepest level (furthest from main)
- Extract clean type with dependency injected
- Push global access up one level
- Repeat until global only at entry points

```go
// BEFORE - Global accessed deep in code
func PublishEvent(event Event) error {
    conn, err := nats.Connect(env.Configs.NATsAddress)
    // ... complex logic
}

// AFTER - Dependency rejected up one level
type EventPublisher struct {
    natsAddress string  // injected, not global
}

func NewEventPublisher(natsAddress string) *EventPublisher {
    return &EventPublisher{natsAddress: natsAddress}
}

func (p *EventPublisher) Publish(event Event) error {
    conn, err := nats.Connect(p.natsAddress)
    // ... same logic, now testable
}

// Caller pushed up (closer to main)
func SetupMessaging() *EventPublisher {
    return NewEventPublisher(env.Configs.NATsAddress)  // Global only here
}

Result: EventPublisher is now 100% testable without globals

Key Principles:

• Incremental: One type at a time, one level at a time
• Bottom-up: Start at deepest code, work toward main
• Pragmatic: Accept globals at entry points (main, handlers)
• Testability: Each extracted type is an island (testable in isolation)

See Example 3 for complete case study.

</refactoring_patterns>

<decision_tree> When linter fails, ask these questions (see reference.md for details):

1. Does this read like a story?

   • No → Extract functions for different abstraction levels

2. Can this be broken into smaller pieces?

   • By responsibility? → Extract functions
   • By task? → Extract functions
   • By category? → Extract functions

3. Does logic run on primitives?

   • Yes → Is this primitive obsession? → Extract type

4. Is function long due to switch statement?

   • Yes → Extract case handlers

5. Are there deeply nested if/else?

   • Yes → Use early returns or extract functions </decision_tree>

<testing_integration> When creating new types or extracting functions during refactoring:

ALWAYS invoke @testing skill to write tests for:

• Isolated types: Types with injected dependencies (testable islands)
• Value object types: Types that may depend on other value objects but are still isolated
• Extracted functions: New functions created during refactoring
• Parse functions: Functions that transform unstructured data

<island_definition> A type is an "island of clean code" if:

• Dependencies are explicit (injected via constructor)
• No global or static dependencies
• Can be tested in isolation
• Has 100% testable public API

Examples of testable islands:

• NATSClient with injected natsAddress string (no other dependencies)
• Email type with validation logic (no dependencies)
• ServiceEndpoint that uses Port value object (both are testable islands)
• OrderService with injected Repository and EventPublisher (all testable)

Note: Islands can depend on other value objects and still be isolated! </island_definition>

<stopping_criteria> STOP when ALL of these are met:

• Linter passes
• All functions < 50 LOC
• Nesting ≤ 2 levels
• Code reads like a story
• No more "juicy" abstractions to extract

Warning Signs of Over-Engineering:

• Creating types with only one method
• Functions that just call another function
• More abstraction layers than necessary
• Code becomes harder to understand
• Diminishing returns on complexity reduction

Pragmatic Approach: IF linter passes AND code is readable: STOP - Don't extract more EVEN IF you could theoretically extract more: STOP - Avoid abstraction bloat </stopping_criteria>

<output_format>

CONTEXT ANALYSIS

Function: CreateUser (user/service.go:45)
Role: Core user creation orchestration
Called by:
- api/handler.go:89 (HTTP endpoint)
- cmd/user.go:34 (CLI command)

Potential types spotted:
- Email: Complex validation logic scattered
- UserID: Generation and validation logic

REFACTORING APPLIED

Patterns Successfully Applied:
1. Early Returns: Reduced nesting from 4 to 2 levels
2. Storifying: Extracted validate(), save(), notify()
3. Extract Type: Created Email and PhoneNumber types

Patterns Tried but Insufficient:
- Extract Function alone: Still too complex, needed types

Types Created (with Juiciness Score):

Email type (JUICY - Behavioral + Usage):
- Behavioral: ParseEmail(), Domain(), LocalPart() methods
- Usage: Used in 5+ places across codebase
- Island: Testable in isolation
- → Invoke @testing skill to write tests

Types Considered but Rejected (NOT JUICY):
- UserID: Only empty check, good naming sufficient
- Status: Just string constants, enum adequate

METRICS

Complexity Reduction:
- Cyclomatic: 18 → 7
- Cognitive: 25 → 8
- LOC: 120 → 45
- Nesting: 4 → 2

FILES MODIFIED

Modified:
- user/service.go (+15, -75 lines)

Created (Islands of Clean Code):
- user/email.go (new, +45 lines) → Ready for @testing skill

AUTOMATION COMPLETE

Stopping Criteria Met:
- Linter passes (0 issues)
- All functions < 50 LOC
- Max nesting = 2 levels
- Code reads like a story
- No more juicy abstractions

Ready for: @pre-commit-review phase

</output_format>

Iterative Loop:

1. Linter fails → invoke @refactoring
2. Refactoring complete → re-run linter
3. Linter passes → invoke @pre-commit-review
4. Review finds design debt → invoke @refactoring again
5. Repeat until both linter AND review pass

Invokes (When Needed):

• @code-designing: When refactoring creates new types, validate design
• @testing: Automatically invoked to write tests for new types/functions
• @pre-commit-review: Validates design quality after linting passes

See reference.md for complete refactoring patterns and decision tree.

<success_criteria> Refactoring is complete when ALL of the following are true:

• Linter passes (0 issues)
• All functions < 50 LOC
• Max nesting ≤ 2 levels
• Code reads like a story (single abstraction level per function)
• No more "juicy" abstractions to extract
• Tests written for new types/functions (via @testing skill)
• Ready for @pre-commit-review phase </success_criteria>