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chore: Test Ollama Cloud#3

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Jan 31, 2026
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chore: Test Ollama Cloud#3
Ariel-Rodriguez merged 8 commits intomainfrom
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Checklist

  • Updated CHANGELOG.md (if skill change)
  • Updated README.md (if new skill)
  • Tested locally with AI assistant (if skill change)
  • Followed pseudocode format (no language-specific code)
  • Used AAA pattern for test examples
  • PR title follows format: <type>: <description>

Type

  • feat: New skill added
  • improve: Existing skill improved
  • fix: Bug fix or correction
  • docs: Documentation only changes
  • chore: Build, CI/CD, or tooling changes

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github-actions bot commented Jan 30, 2026

[SKILL EVALUATION] Results

Skill Status Baseline With Skill Improvement Cases Pass (n/n) Judge Verdict
ps-composition-over-coordination vague outstanding yes 2/2 -> 2/2 B (100/100)
ps-error-handling-design vague outstanding yes 2/2 -> 2/2 B (100/100)
ps-explicit-boundaries-adapters vague outstanding yes 2/2 -> 2/2 B (100/100)
ps-explicit-ownership-lifecycle regular outstanding yes 2/2 -> 2/2 B (100/100)
ps-explicit-state-invariants regular outstanding yes 2/2 -> 2/2 B (100/100)
ps-functional-core-imperative-shell good outstanding yes 2/2 -> 2/2 B (100/100)
ps-illegal-states-unrepresentable regular outstanding yes 2/2 -> 2/2 B (100/100)
ps-local-reasoning vague outstanding yes 2/2 -> 2/2 B (100/100)
ps-minimize-mutation vague outstanding yes 2/2 -> 2/2 B (100/100)
ps-naming-as-design vague good yes 2/2 -> 2/2 B (100/100)
ps-policy-mechanism-separation regular outstanding yes 2/2 -> 2/2 B (100/100)
ps-single-direction-data-flow vague good yes 2/2 -> 2/2 B (100/100)

🤖 LLM Judge: ps-composition-over-coordination

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B demonstrates mastery of the composition over orchestration principle by creating clear, focused units with single responsibilities that communicate through interfaces. Solution A, while attempting to refactor, still contains a central Manager class that violates the principle by orchestrating multiple subsystems. Solution B achieves better modularity, testability, and flexibility through its well-structured composition, making it the superior implementation.

🤖 LLM Judge: ps-error-handling-design

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution A is vague because it only provides basic error logging without implementing the core principles of explicit error handling in function signatures, using Result/Either types, or distinguishing between error categories. Solution B demonstrates mastery of the principle by implementing a full Result type pattern with explicit error types, separating validation errors from database errors, and providing a clear structure for error handling. The type-safe approach in Solution B ensures errors are part of the function signature, making it much more maintainable, testable, and flexible than Solution A's simple try-catch approach.

🤖 LLM Judge: ps-explicit-boundaries-adapters

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B is outstanding because it fully embraces the Hexagonal Architecture principle by creating clear boundaries between the core domain and external systems. It properly defines a port interface, implements adapters for database operations, and wires everything together in a composition root. This approach ensures the core domain is completely isolated from infrastructure concerns, making the code more testable, maintainable, and flexible. Solution A, while attempting to address the issue, only partially implements the principle by creating an adapter but still has the core domain dependent on external systems through the DbEnrollmentRepository class. The lack of a clear port interface and composition root in Solution A prevents it from fully demonstrating mastery of the principle.

🤖 LLM Judge: ps-explicit-ownership-lifecycle

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B demonstrates a deeper understanding of resource management principles by explicitly defining ownership through the FileHandler class and Subscription pattern. It follows the Single Owner Rule by making ownership explicit, implements Deterministic Lifecycle through try-finally blocks, and uses the RAII pattern with proper resource acquisition and release. Solution B also provides better error handling, more robust code structure, and clearer separation of concerns, making it more maintainable and testable. While Solution A is functional, Solution B shows a more comprehensive approach to resource management with explicit ownership tracking and lifecycle management.

🤖 LLM Judge: ps-explicit-state-invariants

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B demonstrates mastery of the principle by implementing a complete type system with explicit invariants, preventing invalid state combinations at compile time. It uses discriminated unions to ensure mutual exclusivity of states and enforces data consistency through type constraints. Solution A, while showing good intentions, only partially addresses the principle by using booleans without leveraging TypeScript's type system to enforce invariants. Solution B's approach is more maintainable, testable, and flexible because it catches errors at compile time rather than runtime, and its explicit type system makes state transitions and validation clearer and more robust.

🤖 LLM Judge: ps-functional-core-imperative-shell

Principle Adherence: Equal
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B is outstanding because it fully embraces the Functional Core, Imperative Shell principle by creating a completely pure function (calculate_new_stock) that has no side effects and only returns a value. The imperative shell (update_stock_in_database) properly handles all external dependencies and effects. Solution A is good because it separates the pure calculation from database operations, but it doesn't fully implement the principle as the pure function still has side effects through the database connection. Both solutions demonstrate good adherence to the principle, but B is more comprehensive in its implementation and better follows the separation of pure computation from side effects.

🤖 LLM Judge: ps-illegal-states-unrepresentable

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B better demonstrates the principle by completely eliminating the possibility of illegal states through compile-time type enforcement. It uses a discriminated union pattern that makes 'Success with Data' and 'Error with Message' mutually exclusive by construction, leveraging TypeScript's type system to prevent invalid states. Solution B also provides clearer type documentation through the explicit status field, making the code more maintainable and self-documenting. While Solution A correctly identifies the need for mutual exclusivity, it doesn't fully leverage the type system to enforce these constraints, leaving potential for runtime errors.

🤖 LLM Judge: ps-local-reasoning

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B is superior because it fully demonstrates the principle of making dependencies explicit. While Solution A shows improvement by passing taxRate as a parameter, it still contains hidden dependencies like the auth module and database connection. Solution B takes the principle to the letter by requiring all dependencies (db and auth) to be passed as parameters, making the code completely understandable in isolation. This makes the code more maintainable, testable, and flexible as all dependencies are explicit and visible from the function signature. The refactored code in Solution B follows the principle with precision, demonstrating mastery of the concept.

🤖 LLM Judge: ps-minimize-mutation

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution A is vague because it only mentions mutation being unsafe without providing a concrete fix or implementation. Solution B is outstanding because it demonstrates mastery of the principle by providing a complete, immutable implementation using Python's dictionary unpacking and datetime handling. It creates a new dictionary without modifying the original, returns the new object, and includes proper documentation. The code is more maintainable, testable, and flexible as it follows functional programming principles and prevents unintended side effects.

🤖 LLM Judge: ps-naming-as-design

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B better demonstrates the Naming as Design principle by using names that reveal intent, constraints, and single responsibility. The names in Solution B clearly communicate business concepts and design intent, making the code more maintainable and aligned with the principle that names should encode intent and constraints rather than implementation details.

🤖 LLM Judge: ps-policy-mechanism-separation

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B clearly demonstrates mastery of the principle by using a well-structured class hierarchy for policies and strategies, making the code highly maintainable, testable, and flexible. It allows for easy extension of backoff strategies and clear separation of policy configuration from implementation logic. Solution A follows the principle at a basic level but lacks the flexibility and extensibility that Solution B provides.

🤖 LLM Judge: ps-single-direction-data-flow

Principle Adherence: B
Code Quality: B
Overall Better: B
Score: 100/100

Reasoning: Solution B better demonstrates the unidirectional data flow principle by properly implementing a single source of truth through the parent component. It eliminates circular dependencies by ensuring data flows strictly from parent to children through props, with events flowing up to the parent. Solution A fails to address the circular dependency issue and does not properly enforce the principle of clear ownership and unidirectional flow. Solution B's architecture is more maintainable, testable, and flexible as it follows the recommended pattern of having a central store or parent component as the sole owner of state.


[DEBUG] Detailed Info

ps-composition-over-coordination (100% pass rate)

[PASS] refactor_orchestrator_class

AI Generated Code:

```javascript
/**
 * Refactored payment processing system using composition over orchestration
 * 
 * Each unit has one clear responsibility and communicates through interfaces
 * instead of a central coordinator.
 */

class Authentication {
    constructor(sessionService) {
        this.sessionService = sessionService;
    }

    async validate(request) {
        const session = await this.sessionService.getSession(request.token);
        if (!session || !session.isValid) {
            throw new Error("Unauthorized");
        }
        return session;
    }
}

class Validation {
    validatePaymentRequest(request) {
        if (!request.amount || request.amount <= 0) {
            throw new Error("Invalid amount");
        }
        if (!request.currency || request.currency !== 'USD') {
            throw new Error("Only USD supported");
        }
        return true;
    }
}

class RiskAssessment {
    constructor(database) {
        this.database = database;
    }

    async checkRisk(userId) {
        const riskScore = await this.database.query(
            "SELECT score FROM risk_profiles WHERE user_id = ?",
            [userId]
        );
        if (riskScore > 80) {
            throw new Error("High risk transaction");
        }
        return true;
    }
}

class PaymentProcessing {
    constructor(bankApi, database) {
        this.bankApi = bankApi;
        this.database = database;
    }

    async processPayment(request) {
        const transaction = await this.bankApi.charge({
            card: request.cardNumber,
            amount: request.amount,
            ref: `TXN-${Date.now()}`
        });

        if (transaction.status !== 'success') {
            await this.database.execute(
                "INSERT INTO audit_logs (event, status) VALUES (?, ?)",
                ['payment_failed', transaction.errorMessage]
            );
            throw new Error(`Bank error: ${transaction.errorMessage}`);
        }

        await this.database.execute(
            "UPDATE accounts SET balance = balance - ? WHERE user_id = ?",
            [request.amount, request.userId]
        );
        
        await this.database.execute(
            "INSERT INTO transactions (id, user_id, amount) VALUES (?, ?, ?)",
            [transaction.id, request.userId, request.amount]
        );

        return transaction;
    }
}

class Notification {
    constructor(emailService) {
        this.emailService = emailService;
    }

    async notify(session, amount) {
        await this.emailService.send(
            session.email,
            "Payment Successful",
            `You charged $${amount}`
        );
    }
}

class PaymentProcessor {
    constructor(
        authService,
        validationService,
        riskService,
        paymentService,
        notificationService
    ) {
        this.authService = authService;
        this.validationService = validationService;
        this.riskService = riskService;
        this.paymentService = paymentService;
        this.notificationService = notificationService;
    }

    async processPayment(request) {
        // 1. Authentication
        const session = await this.authService.validate(request);

        // 2. Validation
        this.validationService.validatePaymentRequest(request);

        // 3. Risk Check
        await this.riskService.checkRisk(session.userId);

        // 4. Bank Communication & Database Update
        const transaction = await this.paymentService.processPayment(request);

        // 5. Notification
        await this.notificationService.notify(session, request.amount);

        return {
            success: true,
            transactionId: transaction.id
        };
    }
}

// Usage
const processor = new PaymentProcessor(
    new Authentication(sessionService),
    new Validation(),
    new RiskAssessment(database),
    new PaymentProcessing(bankApi, database),
    new Notification(emailService)
);

#### [PASS] identify_coordination_bottleneck

**AI Generated Code:**

```javascript
A Manager class that contains all the logic for three different subsystems creates a coordination bottleneck because it violates the Single Responsibility Principle (SRP) and leads to several design issues:

1. **Single Responsibility Principle Violation**: The Manager class is responsible for more than one thing, making the system harder to understand and maintain. Each subsystem should have its own class or module with clear responsibilities.

2. **Tight Coupling**: The Manager class is tightly coupled with all three subsystems, making it difficult to modify or replace individual subsystems without affecting the Manager.

3. **Testability Issues**: Testing the Manager class becomes complex because it requires testing all three subsystems together, increasing the risk of missing edge cases.

4. **Maintenance Problems**: When changes are needed in any subsystem, the Manager class must be updated, increasing the risk of introducing bugs in other subsystems.

5. **Scalability Issues**: As the system grows, the Manager class will become increasingly complex, making it difficult to add new functionality or subsystems.

6. **Violation of Interface Segregation Principle**: The Manager class may expose methods that not all subsystems need, leading to unnecessary dependencies.

7. **Reduced Flexibility**: The system becomes less adaptable to changes because modifying the Manager class affects all connected subsystems.

8. **Increased Complexity**: The overall system complexity increases as the Manager class becomes a central point of control, making it harder for developers to understand the system's behavior.

To address these issues, the system should be refactored to use smaller, focused units that work together through interfaces, as described in the core principle. This approach improves modularity, testability, flexibility, and clarity by allowing each unit to have a single responsibility and enabling behavior to emerge from the structure rather than being hidden in a centralized coordinator.
ps-error-handling-design (100% pass rate)

[PASS] refactor_swallowed_error

AI Generated Code:

```javascript
// Using a Result type pattern (similar to Either/Result in functional languages)
class Result {
  constructor(success, error, value) {
    this.success = success;
    this.error = error;
    this.value = value;
  }

  static success(value) {
    return new Result(true, null, value);
  }

  static failure(errorType, message) {
    return new Result(false, { type: errorType, message }, null);
  }

  isSuccess() {
    return this.success;
  }

  isError() {
    return !this.success;
  }
}

// Error types
const ErrorType = {
  DATABASE_ERROR: 'DATABASE_ERROR',
  VALIDATION_ERROR: 'VALIDATION_ERROR',
  NOT_FOUND: 'NOT_FOUND'
};

function validateUser(user) {
  if (!user.id) {
    return Result.failure(ErrorType.VALIDATION_ERROR, 'User ID is required');
  }
  if (!user.name) {
    return Result.failure(ErrorType.VALIDATION_ERROR, 'User name is required');
  }
  return Result.success(user);
}

function saveUser(user) {
  // First validate the user
  const validation = validateUser(user);
  if (validation.isError()) {
    return validation;
  }

  try {
    // Attempt to save the user
    db.insert(validation.value);
    return Result.success(true);
  } catch (error) {
    // Handle database-specific errors
    return Result.failure(ErrorType.DATABASE_ERROR, error.message);
  }
}

// Usage example:
const result = saveUser({ id: '123', name: 'John' });

if (result.isSuccess()) {
  console.log('User saved successfully');
} else {
  console.error(`Failed to save user: ${result.error.type} - ${result.error.message}`);
}

Key Improvements:

  1. Explicit Error Handling:

    • The function signature doesn't need to change, but the return value explicitly shows success/failure
    • Error types are clearly defined and separated from validation errors
  2. Type-Safe Failures:

    • Uses a Result type instead of exceptions for control flow
    • Error types are part of the error object, making them easier to handle
  3. Intentional Recovery:

    • Validation errors are handled separately from database errors
    • The function returns a Result type that can be pattern-matched
  4. Strategic Failure:

    • Validation errors are handled gracefully (returning a Result)
    • Database errors would typically be handled at a higher level or retried
  5. Clarity:

    • The code clearly shows what can fail and how it should be handled
    • Error types make it easy to distinguish between different kinds of failures
  6. Reliability:

    • Errors can't be accidentally ignored by callers
    • The function signature makes failure modes explicit
    • Compiler (in a typed language) would enforce error handling
  7. Debuggability:

    • Error types make it easier to trace where failures occurred
    • Contextual error messages help with debugging
    • The error handling pattern makes it clear where to add logging

#### [PASS] design_validation_flow

**AI Generated Code:**

```javascript
```python
# User registration with explicit error handling
from typing import Union, Dict, Any
from dataclasses import dataclass

@dataclass
class Success:
    value: Any

@dataclass
class Error:
    code: str
    context: Dict[str, Any]

# Domain error types
class ValidationError(Error):
    def __init__(self, context: Dict[str, Any]):
        super().__init__("ValidationError", context)

class UserAlreadyExistsError(Error):
    def __init__(self, email: str):
        context = {"email": email}
        super().__init__("UserAlreadyExists", context)

class DatabaseError(Error):
    def __init__(self, context: Dict[str, Any]):
        super().__init__("DatabaseError", context)

class EmailServiceError(Error):
    def __init__(self, context: Dict[str, Any]):
        super().__init__("EmailServiceError", context)

# User repository interface
class UserRepository:
    def find_by_email(self, email: str) -> Union[Success, Error]:
        pass
    
    def save(self, user: Dict[str, Any]) -> Union[Success, Error]:
        pass

# Email service interface
class EmailService:
    def send_verification(self, email: str, token: str) -> Union[Success, Error]:
        pass

# User registration service
class UserService:
    def __init__(self, user_repo: UserRepository, email_service: EmailService):
        self.user_repo = user_repo
        self.email_service = email_service
    
    def register_user(self, email: str, password: str) -> Union[Success, Error]:
        # Validate email format
        if not self._validate_email(email):
            return ValidationError({"email": email})
        
        # Check if user already exists
        find_result = self.user_repo.find_by_email(email)
        if isinstance(find_result, Error):
            return DatabaseError({"operation": "find_by_email", "email": email})
        
        if isinstance(find_result.value, Dict) and find_result.value:
            return UserAlreadyExistsError(email)
        
        # Create new user
        user = {
            "id": self._generate_user_id(),
            "email": email,
            "password": self._hash_password(password),
            "verified": False,
            "verification_token": self._generate_token()
        }
        
        # Save user to database
        save_result = self.user_repo.save(user)
        if isinstance(save_result, Error):
            return DatabaseError({"operation": "save", "email": email})
        
        # Send verification email
        email_result = self.email_service.send_verification(
            email, 
            user["verification_token"]
        )
        if isinstance(email_result, Error):
            # Continue registration even if email fails
            return Success({"user": user, "email_sent": False})
        
        return Success({"user": user, "email_sent": True})
    
    def _validate_email(self, email: str) -> bool:
        # Simple email validation
        return "@" in email and "." in email
    
    def _generate_user_id(self) -> str:
        # Generate unique user ID
        import uuid
        return str(uuid.uuid4())
    
    def _hash_password(self, password: str) -> str:
        # Hash password (in real app use proper hashing)
        return f"hashed_{password}"
    
    def _generate_token(self) -> str:
        # Generate verification token
        import secrets
        return secrets.token_urlsafe(16)

# Example usage
def example_usage():
    # Create mock dependencies
    class MockUserRepo:
        def find_by_email(self, email: str):
            # Simulate existing user
            if email == "existing@example.com":
                return Success({"id": "123", "email": "existing@example.com"})
            return Success(None)
        
        def save(self, user: Dict[str, Any]):
            # Simulate database save
            return Success(user)
    
    class MockEmailService:
        def send_verification(self, email: str, token: str):
            # Simulate email failure
            if email == "fail@example.com":
                return EmailServiceError({"email": email})
            return Success(None)
    
    # Create service
    user_repo = MockUserRepo()
    email_service = MockEmailService()
    service = UserService(user_repo, email_service)
    
    # Test cases
    print("Testing user registration:")
    
    # 1. Invalid email
    result = service.register_user("invalid-email", "password123")
    print(f"Invalid email result: {result}")
    
    # 2. User already exists
    result = service.register_user("existing@example.com", "password123")
    print(f"Existing user result: {result}")
    
    # 3. Database connection failure (simulated by mock)
    result = service.register_user("fail@example.com", "password123")
    print(f"Email service failure result: {result}")

# Run example
if __name__ == "__main__":
    example_usage()

</details>

<details>
<summary><b>ps-explicit-boundaries-adapters</b> (100% pass rate)</summary>


#### [PASS] refactor_database_leak

**AI Generated Code:**

```javascript
Let's refactor the code to follow the Hexagonal Architecture pattern by isolating the database logic behind an adapter and defining clear boundaries.

### Refactored Code

```javascript
// Core Domain - No external dependencies
class EnrollmentService {
    constructor(enrollmentRepository) {
        this.enrollmentRepository = enrollmentRepository;
    }

    enrollStudent(studentId, courseId) {
        const student = this.enrollmentRepository.getStudent(studentId);
        if (student.eligible) {
            this.enrollmentRepository.enrollStudent(studentId, courseId);
        }
    }
}

// Port Interface (defined in domain)
class EnrollmentRepository {
    getStudent(studentId) {
        throw new Error("Not implemented");
    }

    enrollStudent(studentId, courseId) {
        throw new Error("Not implemented");
    }
}

// Adapter - Implements the port interface
class PostgresEnrollmentRepository extends EnrollmentRepository {
    constructor(dbClient) {
        super();
        this.dbClient = dbClient;
    }

    getStudent(studentId) {
        return this.dbClient.query('SELECT * FROM students WHERE id = ?', studentId);
    }

    enrollStudent(studentId, courseId) {
        this.dbClient.execute('INSERT INTO enrollments ...', [studentId, courseId]);
    }
}

// Composition Root - Wiring everything together
function main() {
    // Initialize database connection
    const dbClient = initializeDatabaseConnection();
    
    // Create adapter with database connection
    const enrollmentRepository = new PostgresEnrollmentRepository(dbClient);
    
    // Create core service with repository
    const enrollmentService = new EnrollmentService(enrollmentRepository);
    
    // Use the service
    enrollmentService.enrollStudent(123, 456);
}

// Database connection initialization (separate concern)
function initializeDatabaseConnection() {
    // Database connection logic here
    return {
        query: function(query, params) {
            // Actual database query implementation
        },
        execute: function(query, params) {
            // Actual database execution implementation
        }
    };
}

// Usage
main();

Key Improvements

  1. Clear Boundaries:

    • The core domain (EnrollmentService) has no dependencies on external systems
    • The port interface (EnrollmentRepository) defines what the core needs
    • The adapter (PostgresEnrollmentRepository) implements the port interface and handles database operations
  2. Testability:

    • The core service can be tested without a real database
    • You can create a mock repository for testing:
      class MockEnrollmentRepository extends EnrollmentRepository {
          constructor() {
              super();
              this.students = new Map();
              this.enrollments = [];
          }
      
          getStudent(studentId) {
              return this.students.get(studentId);
          }
      
          enrollStudent(studentId, courseId) {
              this.enrollments.push({ studentId, courseId });
          }
      }
  3. Flexibility:

    • You can easily swap database implementations by creating different adapters
    • The core logic doesn't need to change when you want to use a different database
  4. Maintainability:

    • Database logic is isolated and can be modified without affecting the core domain
    • Technical concerns like connection management are handled in the adapter

This refactoring follows the Hexagonal Architecture pattern by:

  • Defining clear boundaries between the core domain and external systems
  • Using port interfaces to define the contracts the core needs
  • Implementing adapters to handle external system interactions
  • Wiring everything together in a composition root

#### [PASS] identify_infrastructure_leaks

**AI Generated Code:**

```javascript
The infrastructure leak in this business logic is that it directly depends on the axios library and makes an HTTP request within the core domain function. This violates the Hexagonal Architecture principle by having the core domain depend on external infrastructure.

To fix this, we should:
1. Define a port interface in the core domain
2. Implement the adapter outside the core domain
3. Make the core depend only on the port interface

Here's how the code should be refactored:

```javascript
// Core domain - depends only on port interface
function processPayment(order, paymentPort) {
    return paymentPort.makePayment(order.total);
}

// Port interface (defined in domain)
const PaymentPort = {
    makePayment: function(amount) {
        throw new Error('Not implemented');
    }
};

// Adapter implementation (separate module)
class HttpPaymentAdapter {
    constructor(axiosClient) {
        this.axiosClient = axiosClient;
    }
    
    makePayment(amount) {
        return this.axiosClient.post('/api/pay', { amount });
    }
}

// Composition root - wiring it all together
const axios = require('axios');
const paymentAdapter = new HttpPaymentAdapter(axios);
processPayment(order, paymentAdapter);

Key improvements:

  1. The core function processPayment now depends only on the PaymentPort interface
  2. The HttpPaymentAdapter handles all the HTTP infrastructure concerns
  3. The composition root wires everything together
  4. The core domain is now isolated from external dependencies
  5. Adapters can be swapped without affecting the core logic

</details>

<details>
<summary><b>ps-explicit-ownership-lifecycle</b> (100% pass rate)</summary>


#### [PASS] refactor_leaky_file_handle

**AI Generated Code:**

```javascript
Here's the refactored code that ensures the file handle is closed even if an error occurs, following the Explicit Ownership Lifecycle principles:

```javascript
const fs = require('fs');

class FileHandler {
    constructor(filePath) {
        this.filePath = filePath;
        this.file = null;
    }

    open() {
        this.file = fs.openSync(this.filePath, 'a');
    }

    write(message) {
        if (!this.file) {
            throw new Error('File not opened');
        }
        fs.writeSync(this.file, message + '\n');
    }

    close() {
        if (this.file) {
            fs.closeSync(this.file);
            this.file = null;
        }
    }
}

function logMessage(filePath, message) {
    const fileHandler = new FileHandler(filePath);
    try {
        fileHandler.open();
        fileHandler.write(message);
    } finally {
        fileHandler.close();
    }
}

// Example usage:
// logMessage('logfile.txt', 'This is a test message');

Explanation:

  1. Ownership Structure:

    • Created a FileHandler class that owns the file handle
    • The file handle is only accessible through the class methods
    • The class manages the lifecycle of the file handle
  2. Deterministic Lifecycle:

    • open() method acquires the resource
    • write() method uses the resource
    • close() method releases the resource
    • The finally block in logMessage ensures cleanup happens even if an error occurs
  3. RAII Pattern:

    • Resource acquisition happens in the open() method
    • Resource release happens in the close() method
    • The file handle is automatically cleaned up when the FileHandler instance goes out of scope or when close() is explicitly called
  4. Error Handling:

    • The write() method checks if the file is open before writing
    • The finally block ensures cleanup happens regardless of whether an error occurred during the operation

This implementation ensures that the file handle is always properly closed, even if an error occurs during the file operation, following the principles of single ownership and deterministic resource management.


#### [PASS] design_subscription_owner

**AI Generated Code:**

```javascript
Here's how you would implement the component structure with explicit ownership lifecycle:

```python
class EventBus:
    def __init__(self):
        self.subscribers = {}
    
    def subscribe(self, event_type, callback):
        if event_type not in self.subscribers:
            self.subscribers[event_type] = []
        self.subscribers[event_type].append(callback)
        return Subscription(self, event_type, callback)
    
    def publish(self, event_type, data):
        if event_type in self.subscribers:
            for callback in self.subscribers[event_type]:
                callback(data)

class Subscription:
    def __init__(self, event_bus, event_type, callback):
        self.event_bus = event_bus
        self.event_type = event_type
        self.callback = callback
    
    def unsubscribe(self):
        if self.event_bus and self.event_type in self.event_bus.subscribers:
            self.event_bus.subscribers[self.event_type].remove(self.callback)
            if not self.event_bus.subscribers[self.event_type]:
                del self.event_bus.subscribers[self.event_type]
            self.event_bus = None

class Component:
    def __init__(self, event_bus):
        self.event_bus = event_bus
        self.subscription = None
    
    def start(self):
        # Create subscription with explicit ownership
        self.subscription = self.event_bus.subscribe(
            'component_event',
            lambda data: self.handle_event(data)
        )
    
    def handle_event(self, data):
        print(f"Component received: {data}")
    
    def stop(self):
        # Clean up subscription when component stops
        if self.subscription:
            self.subscription.unsubscribe()
            self.subscription = None

# Usage example
event_bus = EventBus()
component = Component(event_bus)

try:
    component.start()
    # Component is now active and will receive events
    event_bus.publish('component_event', 'Hello from event bus!')
finally:
    component.stop()  # Ensure cleanup happens

Key features of this implementation:

  1. Explicit Ownership: The Component owns the Subscription object, which is created when the component starts and cleaned up when it stops.

  2. Deterministic Lifecycle:

    • The subscription is created in the start() method
    • The subscription is cleaned up in the stop() method
    • The component ensures proper cleanup even in error cases (using try-finally)
  3. RAII Pattern: The Subscription class follows the RAII principle:

    • Resources (subscriptions) are acquired in the constructor
    • Resources are released in the unsubscribe() method, which is called when the subscription is no longer needed
  4. No Double-Free: The unsubscribe() method safely removes the callback and cleans up the subscription without causing double-free issues.

  5. No Leaks: The stop() method ensures all resources are properly cleaned up, preventing resource leaks.

This design ensures that the subscription doesn't outlive the component and that all resources are properly managed with a clear ownership structure.


</details>

<details>
<summary><b>ps-explicit-state-invariants</b> (100% pass rate)</summary>


#### [PASS] refactor_boolean_explosion

**AI Generated Code:**

```javascript
Let's refactor the given JavaScript state object to use a discriminated union pattern to prevent invalid state combinations. This approach will ensure that the state is always valid and that invalid combinations are impossible.

```javascript
// Define a discriminated union for RequestState
const RequestState = {
    Idle: { type: 'Idle' },
    Loading: { type: 'Loading' },
    Success: (data) => ({ type: 'Success', data }),
    Error: (message) => ({ type: 'Error', message })
};

// Function to render based on state
const render = (state) => {
    switch (state.type) {
        case 'Idle':
            return "Click to load";
        case 'Loading':
            return "Loading...";
        case 'Success':
            return displayData(state.data);
        case 'Error':
            return showError(state.message);
        default:
            throw new Error(`Unknown state type: ${state.type}`);
    }
};

// Helper functions (assuming they exist)
const displayData = (data) => `Data: ${data}`;
const showError = (message) => `Error: ${message}`;

// Example usage:
const state = RequestState.Loading();
console.log(render(state)); // Output: "Loading..."

// This ensures that only valid combinations are possible
// and invalid states like having both isLoading and isError
// are impossible to create.

Key Improvements:

  1. Discriminated Union: The state is now a union type with a type property that determines which variant it is. This prevents invalid combinations.
  2. Explicit Invariants: The state can only be in one state at a time, and the type property ensures this.
  3. Type Safety: The type system now enforces that only valid states are created.
  4. No Optional Fields: All fields are part of a specific variant, making their validity explicit.

This refactoring makes the state machine more robust by leveraging TypeScript's type system (if used) or by using a similar pattern in JavaScript to ensure that only valid states are possible.


#### [PASS] state_invariant_definition

**AI Generated Code:**

```javascript
The primary invariant for a multi-step Wizard state is that the system must always be in a valid combination of states where:

1. **Exactly one state applies at a time** - The wizard cannot be in multiple states simultaneously (e.g., cannot be both "loading" and "success" at the same time).

2. **State consistency** - The current step must be valid relative to the form data and submission result:
   - If the wizard is in the "success" state, the submission must have succeeded
   - If the wizard is in the "error" state, the submission must have failed
   - If the wizard is in the "loading" state, the submission is in progress

3. **Data coherence** - The form data must be appropriate for the current step:
   - Cannot have form data from a future step
   - Cannot have form data from a past step if it was invalidated by validation rules

4. **Progression constraints** - The wizard must follow a logical progression:
   - Cannot skip steps without completing them
   - Cannot go back to a previous step once validation has failed

Here's how we could implement this with explicit invariants:

```python
from dataclasses import dataclass
from typing import Optional, Union

@dataclass
class FormData:
    step: int
    data: dict

@dataclass
class SuccessResult:
    submission_id: str
    timestamp: str

@dataclass
class ErrorResult:
    error_code: str
    message: str

WizardState = Union[
    "IdleState",
    "LoadingState",
    "SuccessState",
    "ErrorState"
]

@dataclass
class IdleState:
    current_step: int

@dataclass
class LoadingState:
    current_step: int
    form_data: FormData

@dataclass
class SuccessState:
    current_step: int
    form_data: FormData
    submission_result: SuccessResult

@dataclass
class ErrorState:
    current_step: int
    form_data: FormData
    submission_result: ErrorResult

def is_valid_state(state: WizardState) -> bool:
    """
    Validates that the wizard state satisfies all invariants
    """
    if isinstance(state, IdleState):
        return True  # Valid to be idle
    
    if isinstance(state, LoadingState):
        # Must have form data for current step
        return (0 < state.current_step <= 5 and  # Assuming 5 steps max
                state.form_data.step == state.current_step)
    
    if isinstance(state, SuccessState):
        # Must have form data for current step and successful submission
        return (0 < state.current_step <= 5 and
                state.form_data.step == state.current_step and
                isinstance(state.submission_result, SuccessResult))
    
    if isinstance(state, ErrorState):
        # Must have form data for current step and error result
        return (0 < state.current_step <= 5 and
                state.form_data.step == state.current_step and
                isinstance(state.submission_result, ErrorResult))
    
    return False

def transition_to_loading(state: WizardState, form_data: FormData) -> WizardState:
    """
    Explicit transition to loading state with invariant preservation
    """
    if not isinstance(state, (IdleState, SuccessState, ErrorState)):
        raise ValueError("Cannot transition to loading from current state")
    
    if not is_valid_state(state):
        raise ValueError("Current state violates invariants")
    
    return LoadingState(
        current_step=state.current_step,
        form_data=form_data
    )

def transition_to_success(state: WizardState, result: SuccessResult) -> WizardState:
    """
    Explicit transition to success state with invariant preservation
    """
    if not isinstance(state, LoadingState):
        raise ValueError("Can only transition to success from loading state")
    
    if not is_valid_state(state):
        raise ValueError("Current state violates invariants")
    
    return SuccessState(
        current_step=state.current_step,
        form_data=state.form_data,
        submission_result=result
    )

def transition_to_error(state: WizardState, result: ErrorResult) -> WizardState:
    """
    Explicit transition to error state with invariant preservation
    """
    if not isinstance(state, LoadingState):
        raise ValueError("Can only transition to error from loading state")
    
    if not is_valid_state(state):
        raise ValueError("Current state violates invariants")
    
    return ErrorState(
        current_step=state.current_step,
        form_data=state.form_data,
        submission_result=result
    )

def transition_to_idle(state: WizardState) -> WizardState:
    """
    Explicit transition to idle state with invariant preservation
    """
    if not isinstance(state, (SuccessState, ErrorState)):
        raise ValueError("Can only transition to idle from success or error state")
    
    if not is_valid_state(state):
        raise ValueError("Current state violates invariants")
    
    return IdleState(current_step=state.current_step)

The key invariants we've enforced:

  1. Mutual Exclusivity: Only one state type can be active at a time
  2. Data Consistency: Form data always matches the current step
  3. Progression Logic: Cannot skip steps or go back to invalid states
  4. State Completeness: Each state contains exactly the data it needs

This design ensures that the wizard can never be in an invalid state, making the system more reliable and easier to reason about.


</details>

<details>
<summary><b>ps-functional-core-imperative-shell</b> (100% pass rate)</summary>


#### [PASS] refactor_mixed_logic

**AI Generated Code:**

```javascript
```python
# PURE CORE - No side effects
def calculate_new_stock(current_stock, sold_quantity):
    """
    Pure function to calculate new stock level
    Args:
        current_stock: Current stock level
        sold_quantity: Quantity sold
    Returns:
        New stock level
    """
    if current_stock < sold_quantity:
        raise ValueError('Insufficient stock')
    return current_stock - sold_quantity

# IMPERATIVE SHELL - Coordinates effects
def update_stock_in_database(product_id, sold_quantity, db_connection):
    """
    Imperative function to handle database operations
    Args:
        product_id: ID of product to update
        sold_quantity: Quantity sold
        db_connection: Database connection object
    Returns:
        New stock level
    """
    # Load data from database
    product = db_connection.query('SELECT * FROM products WHERE id = ?', (product_id,))
    
    # Call pure function with loaded data
    new_stock = calculate_new_stock(product.stock, sold_quantity)
    
    # Save result back to database
    db_connection.execute(
        'UPDATE products SET stock = ? WHERE id = ?',
        (new_stock, product_id)
    )
    
    return new_stock

This refactoring follows the Functional Core, Imperative Shell principles by:

  1. Moving the business logic (stock calculation) to a pure function that has no side effects
  2. Keeping the database operations in the imperative shell
  3. Making the pure function testable without any database dependencies
  4. Clearly separating concerns between business logic and infrastructure interactions

The pure function calculate_new_stock can now be easily tested with various inputs without needing to interact with a database, while the imperative shell handles all database operations and coordination.


#### [PASS] identify_side_effects

**AI Generated Code:**

```javascript
The function `validateAndLog` contains two side effects that violate the Functional Core principle:

1. **Console Logging**: 
   ```javascript
   console.error('Invalid email');
   logger.info('User validated');

These are side effects because they output to the console and logging system, which are external effects that don't return any value to the function's computation. The function's return value (true/false) is determined by the validation logic, but the logging is an additional effect that's not part of the core computation.

  1. External State Modification:
    While the logger is an external dependency, the console is part of the JavaScript environment. Both console.error and console.info are writing to external state (the console output buffer).

These side effects violate the Functional Core principle because:

  • The function has observable effects beyond its return value
  • The same input could produce different outputs if the console or logger state changes
  • The function's behavior depends on external systems (console, logger) rather than just its input parameters
  • The function cannot be purely deterministic or easily tested in isolation

According to the principle, pure functions should:

  • Have no side effects
  • Return the same output for the same input
  • Not depend on external state
  • Be testable without mocking external dependencies

To refactor this function according to the Functional Core principle, we would:

  1. Remove the console logging
  2. Remove the logger.info call
  3. Return only the validation result
  4. Let the caller handle logging if needed

</details>

<details>
<summary><b>ps-illegal-states-unrepresentable</b> (100% pass rate)</summary>


#### [PASS] transform_invalid_state

**AI Generated Code:**

```javascript
To make the state 'Success with Data' and 'Error with Message' mutually exclusive, we can use a discriminated union where each variant contains only the relevant fields. Here's how you can transform the type:

```typescript
type Response =
    | { status: 'success', data: any }
    | { status: 'error', errorMessage: string };

This design ensures that:

  1. If status is 'success', there must be a data field and no errorMessage
  2. If status is 'error', there must be an errorMessage and no data
  3. The two states are mutually exclusive by construction

The compiler will now enforce these constraints at compile time, eliminating the possibility of having both data and errorMessage in the same response, or having neither.


#### [PASS] explain_parse_dont_validate

**AI Generated Code:**

```javascript
The key difference between "validating data" and "parsing data into domain types" in the context of making illegal states unrepresentable lies in their approach to ensuring data integrity and their impact on system correctness.

**Validating Data**
This approach typically involves:
1. Accepting raw/primitive data
2. Performing runtime checks to verify constraints
3. Potentially throwing errors when invalid data is encountered
4. Returning either valid data or error messages

Problems with validation:
- Invalid states can temporarily exist in memory
- Requires defensive programming and runtime checks
- Error handling becomes scattered throughout the codebase
- Tests must cover all valid and invalid cases
- Changes to validation logic can have widespread impacts

**Parsing Data into Domain Types**
This approach involves:
1. Accepting raw/primitive data at boundaries
2. Immediately transforming it into strongly-typed domain objects
3. Using the type system to enforce constraints at compile time
4. Making invalid states impossible to represent

Benefits of parsing into domain types:
- Invalid states cannot be created in the first place
- No need for runtime validation or defensive checks
- Types document valid states and constraints
- Compiler catches breaking changes at compile time
- Changes to business rules require type changes
- Code becomes self-documenting and more maintainable

The core principle is to shift validation from runtime checks to compile-time type enforcement. By parsing data into domain types that can only represent valid states, you eliminate the possibility of illegal states existing in your system. This approach leverages the type system as a safety mechanism, making the code more robust and easier to maintain.
ps-local-reasoning (100% pass rate)

[PASS] refactor_global_state_access

AI Generated Code:

Here's the refactored code that improves local reasoning by eliminating global state access:

```javascript
function getFinalPrice(order, taxRate) {
    return order.total * (1 + taxRate);
}

To use this function, you would now pass the tax rate as a parameter:

const taxRate = window.APP_CONFIG.taxRate; // Get the tax rate from config
const finalPrice = getFinalPrice(order, taxRate);

This change:

  1. Makes the function self-contained by requiring all dependencies as parameters
  2. Eliminates the need to access global state within the function
  3. Makes the function easier to test and understand
  4. Improves maintainability by avoiding hidden dependencies
  5. Makes the function's requirements immediately clear from its signature

#### [PASS] detect_hidden_dependencies

**AI Generated Code:**

```javascript
The code has two hidden dependencies that violate the principle of making dependencies explicit:

1. **Database dependency (db)**
   - The `db.posts.remove(postId)` call depends on a database object that isn't passed as a parameter
   - This creates an implicit dependency that isn't visible from the function signature

2. **Authentication dependency**
   - The `auth.isAdmin()` call depends on an authentication module that isn't passed as a parameter
   - While `auth` is imported, the function doesn't receive it as an argument
   - This makes the dependency less explicit than it should be

To make this code adhere to the principle of making dependencies explicit, we should refactor it to pass all dependencies as parameters:

```javascript
import { Database } from './types';

function deletePost(postId: string, db: Database, auth: Auth): void {
    if (auth.isAdmin()) {
        db.posts.remove(postId);
    }
}

// Or better, using dependency injection:
function createDeletePostHandler(db: Database, auth: Auth): (postId: string) => void {
    return (postId: string) => {
        if (auth.isAdmin()) {
            db.posts.remove(postId);
        }
    };
}

In this refactored version:

  1. All dependencies (db and auth) are passed as parameters
  2. The function signature clearly shows what it needs to operate
  3. The code is now fully understandable in isolation
  4. It's easier to test - we can provide mock implementations of db and auth
  5. The dependencies are explicit and visible to anyone reading the function

</details>

<details>
<summary><b>ps-minimize-mutation</b> (100% pass rate)</summary>


#### [PASS] refactor_mutating_argument

**AI Generated Code:**

```javascript
```python
from datetime import datetime

def activate_user(user):
    """
    Returns a new user object with isActive set to True and activatedAt set to current time.
    
    Args:
        user (dict): Original user object
        
    Returns:
        dict: New user object with activation details
    """
    return {
        **user,  # Spread operator to copy existing properties
        'isActive': True,
        'activatedAt': datetime.now()
    }

# Example usage:
# original_user = {'name': 'John', 'email': 'john@example.com'}
# new_user = activate_user(original_user)
# print(original_user)  # Original remains unchanged
# print(new_user)       # New object with activation details

This implementation follows the principle of immutability by:

  1. Creating a new dictionary with all original properties using the spread operator
  2. Adding/overriding only the properties that need to be updated
  3. Not modifying the original user object at all
  4. Returning a completely new object with the activation details

The caller can then decide whether to use the original or the new object, maintaining the immutability principle.


#### [PASS] identify_unsafe_mutation

**AI Generated Code:**

```javascript
The code is unsafe due to mutation because:

1. The `process` function likely modifies the array in place
2. Since arrays are mutable objects, any changes made inside `process` will be reflected in the original `list` variable
3. The caller's expectation is that `list` will remain unchanged after the function call
4. This violates the principle of immutability and can lead to unpredictable behavior in larger codebases

The fix would be to:
- Return a new array instead of modifying the original
- Use functional programming techniques like `map`, `filter`, or `reduce`
- Or make a copy of the array before modifying it

Example safe version:
```javascript
function processSafe(arr) {
    // Return a new array with modifications
    return arr.map(x => x * 2);
}

const list = [1, 2, 3];
const newList = processSafe(list);
console.log(list); // [1, 2, 3] - unchanged!
console.log(newList); // [2, 4, 6] - new array

</details>

<details>
<summary><b>ps-naming-as-design</b> (100% pass rate)</summary>


#### [PASS] refactor_generic_names

**AI Generated Code:**

```javascript
1. `VAR userArray = []` → `CONST eligibleCandidates = []`
   - Changed from implementation detail ("array") to intent ("candidates")
   - Reveals selection criteria rather than data structure

2. `FUNCTION handleData(data)` → `FUNCTION validateAndProcessOrder(data)`
   - Added business context to replace generic "handle"
   - Separates validation from processing responsibilities
   - Uses domain terminology instead of generic verbs

3. `CONST VALUE = 10` (minimum age) → `CONST MINIMUM_AGE = 10`
   - Added descriptive prefix to encode constraint
   - Makes business rule explicit rather than just a magic number
   - Follows naming convention for business rules

[PASS] identify_naming_violations

AI Generated Code:

The name `saveUserAndLogActivity` violates the Naming as Design principle because it combines two distinct responsibilities into a single name. According to the guidelines, if you can't name something without using "And", it's likely doing too much and needs to be broken down into separate components with clear, focused names.

The name reveals that the function is both saving a user and logging activity, but it doesn't clearly indicate the purpose or intent of each responsibility. This violates the principle that names should reveal purpose and force single responsibility.

A better approach would be to have separate functions with clear names:
- `saveUser` for the responsibility of persisting user data
- `logActivity` for the responsibility of recording system activity

This separation of concerns makes the design more obvious and aligns with the principle that good names make the design clear and that when you can't name something clearly, the design needs work.
ps-policy-mechanism-separation (100% pass rate)

[PASS] extract_hardcoded_policy

AI Generated Code:

```ndiff
- function filterExpiredItems(items) {
+ // POLICY - Business rules
+ const EXPIRATION_POLICY = {
+     maxAgeMs: 30 * 24 * 60 * 60 * 1000 // 30 days
+ };
+ 
+ // MECHANISM - Reusable implementation
+ function filterByAge(items, policy) {
+     const now = Date.now();
+     return items.filter(item => now - item.createdAt < policy.maxAgeMs);
+ }
+ 
+ // USAGE - Connect policy to mechanism
+ function filterExpiredItems(items) {
+     return filterByAge(items, EXPIRATION_POLICY);

#### [PASS] design_retry_mechanism

**AI Generated Code:**

```javascript
Here's an implementation that separates the retry policy from the retry mechanism:

```python
import time
import random

# POLICY - Business rules/strategies
class RetryPolicy:
    def __init__(self, max_attempts, backoff_strategy):
        self.max_attempts = max_attempts
        self.backoff_strategy = backoff_strategy

class FixedBackoff:
    def __init__(self, delay_ms):
        self.delay_ms = delay_ms
    
    def get_delay(self, attempt):
        return self.delay_ms

class ExponentialBackoff:
    def __init__(self, base_delay_ms, max_delay_ms):
        self.base_delay_ms = base_delay_ms
        self.max_delay_ms = max_delay_ms
    
    def get_delay(self, attempt):
        delay = self.base_delay_ms * (2 ** (attempt - 1))
        return min(delay, self.max_delay_ms)

class JitterBackoff:
    def __init__(self, base_backoff):
        self.base_backoff = base_backoff
    
    def get_delay(self, attempt):
        delay = self.base_backoff.get_delay(attempt)
        jitter = random.uniform(0, delay * 0.1)
        return delay + jitter

# MECHANISM - Reusable implementation
def retry_operation(policy, operation, *args, **kwargs):
    """
    Execute an operation with retry logic based on the provided policy
    
    Args:
        policy: RetryPolicy instance defining max attempts and backoff strategy
        operation: Callable to execute
        *args: Positional arguments to pass to operation
        **kwargs: Keyword arguments to pass to operation
    
    Returns:
        Result of successful operation
    
    Raises:
        Exception: If operation fails after max attempts
    """
    last_exception = None
    
    for attempt in range(1, policy.max_attempts + 1):
        try:
            return operation(*args, **kwargs)
        except Exception as e:
            last_exception = e
            if attempt == policy.max_attempts:
                break
                
            delay_ms = policy.backoff_strategy.get_delay(attempt)
            print(f"Attempt {attempt} failed. Retrying in {delay_ms}ms...")
            time.sleep(delay_ms / 1000)  # Convert to seconds
    
    raise last_exception

# Example usage
if __name__ == "__main__":
    # Define policies
    fixed_policy = RetryPolicy(
        max_attempts=5,
        backoff_strategy=FixedBackoff(delay_ms=1000)
    )
    
    exponential_policy = RetryPolicy(
        max_attempts=5,
        backoff_strategy=ExponentialBackoff(base_delay_ms=100, max_delay_ms=5000)
    )
    
    jitter_policy = RetryPolicy(
        max_attempts=5,
        backoff_strategy=JitterBackoff(
            ExponentialBackoff(base_delay_ms=100, max_delay_ms=5000)
        )
    )
    
    # Example operation that might fail
    def unreliable_operation():
        success = random.random() < 0.7  # 70% chance of success
        if not success:
            raise Exception("Operation failed")
        return "Operation succeeded!"
    
    # Test the retry mechanism
    try:
        result = retry_operation(fixed_policy, unreliable_operation)
        print(f"Result: {result}")
    except Exception as e:
        print(f"Final failure: {e}")

Key Principles Demonstrated:

  1. Separation of Concerns:

    • Policy classes (RetryPolicy, FixedBackoff, ExponentialBackoff, JitterBackoff) define what should happen
    • The retry_operation function defines how to implement the retry logic
  2. Reusability:

    • Backoff strategies can be mixed and matched
    • Different retry policies can be created by combining strategies with different parameters
  3. Flexibility:

    • Policies can be easily modified without changing the retry implementation
    • New backoff strategies can be added without modifying existing code
  4. Testing:

    • Policies and mechanisms can be tested independently
    • The example includes a simple test case

Benefits:

  • Maintainability: Policies can be changed without touching the retry implementation
  • Extensibility: New backoff strategies can be added with minimal changes
  • Clarity: The code clearly separates configuration from implementation
  • Reusability: The retry mechanism can be used in different contexts with different policies

</details>

<details>
<summary><b>ps-single-direction-data-flow</b> (100% pass rate)</summary>


#### [PASS] detect_circular_dependency

**AI Generated Code:**

```javascript
The circular dependency in the provided data flow is:

1. **Component A updates Store X**  
2. **Store X triggers an update in Component B**  
3. **Component B's lifecycle method calls a function in Component A** that updates Store X again  

This creates a loop:  
`Component A → Store X → Component B → Component A → Store X → ...`

This violates the core principle of unidirectional data flow. The issue is that **Component A is both a reader and a writer of Store X**, and it's being updated indirectly through Component B, which leads to a circular dependency.

### Solution:
To fix this, **Component A should not mutate Store X directly from within Component B**. Instead:

- Ensure **Store X is the single source of truth**.
- Use events or actions to communicate changes.
- Component B should only **emit events** (e.g., `onAddItem`), and the parent (or a higher-level component) should handle state updates in Store X.

### Corrected Flow:

// Store X (Single Source of Truth)
class StoreX {
state = { items: [] };

addItem(item) {
this.state.items = [...this.state.items, item];
}
}

// Parent Component (owns Store X)
class ParentComponent {
storeX = new StoreX();

FUNCTION onAddItem(item) {
this.storeX.addItem(item); // Parent is the single updater
}

FUNCTION render() {
return (

);
}
}

// Child Component (receives state, emits events)
class ChildComponent({ items, onAddItem }) {
FUNCTION handleClick() {
onAddItem(newItem); // Event flows up to parent
}

FUNCTION render() {
return (


{items.map(item =>
{item}
)}
Add Item

);
}
}


### Key Takeaway:
- **Avoid circular dependencies** by ensuring data flows **down** from the owner (parent/component) and **up** via events.
- **Store X** is the single source of truth — only it should mutate state.
- **Component B** should not call methods on **Component A** — it should call methods on the **owner** (e.g., `ParentComponent` or `StoreX`).

[PASS] refactor_bidirectional_sync

AI Generated Code:

```js
// Parent component - owns the user state
class App {
    constructor() {
        this.user = null;
    }

    setUser(user) {
        this.user = user;
        // Only the parent updates user state
        // Children receive updates through props
    }

    render() {
        return (
            <Header user={this.user} />
            <Footer user={this.user} />
        );
    }
}

// Child components - receive user as props
class Header {
    constructor(props) {
        this.user = props.user;
    }

    render() {
        return <div>Welcome, {this.user.name}</div>;
    }
}

class Footer {
    constructor(props) {
        this.user = props.user;
    }

    render() {
        return <div>Logged in as {this.user.email}</div>;
    }
}

This refactoring follows the unidirectional data flow principle by:

  1. Making the App component the single source of truth for user data
  2. Removing the syncUser methods from child components
  3. Passing user data down through props only
  4. Ensuring data flows in a single direction from parent to children
  5. Eliminating the manual synchronization that could lead to inconsistencies

</details>
<!-- Sticky Pull Request Comment -->

@Ariel-Rodriguez
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/test ollama

@Ariel-Rodriguez
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/test ollama

@Ariel-Rodriguez
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/test ollama

@Ariel-Rodriguez
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/test ollama

@Ariel-Rodriguez
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/test ollama

@Ariel-Rodriguez Ariel-Rodriguez changed the title chore: test ci chore: Test Ollama Cloud Jan 31, 2026
@Ariel-Rodriguez
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/test ollama

   Applied programming skills to CI infrastructure:
   - Policy-Mechanism Separation: Config drives execution
   - Composition Over Coordination: Small focused scripts
   - Single Responsibility: Each script does one thing
   - Explicit Boundaries: Clear separation of concerns
   - Single Direction Data Flow: Clear pipeline

   Changes:
   - Renamed validate_skills.sh -> orchestrate_evaluations.sh
   - Created modular scripts: detect_changes, run_evaluation, consolidate_results
   - Enhanced matrix_generator.py with validation
   - Updated workflow for parallel execution support
   - Config-driven provider/model matrix

   Usage:
     /test                    # Run all enabled providers
     /test ollama            # Run only ollama models
     /test parallel          # Run in parallel (matrix strategy)
@Ariel-Rodriguez
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/test

@Ariel-Rodriguez Ariel-Rodriguez merged commit f23a35e into main Jan 31, 2026
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