

```python
@dataclass
class PragmaticProjectManagerAGIAgent:
    project_desc: str
    project_dir: str

@celery.task
def define_project_scope(agent: PragmaticProjectManagerAGIAgent, parameters):
    prompt = f"{agent.project_desc}\nTest 1. Define Project Scope:"
    chat(prompt=prompt, write_path=f"{agent.project_dir}/test1.txt")
    # Implement your method here

def plan_schedule(agent: PragmaticProjectManagerAGIAgent, tasks):
    # Implement your approach here

def allocate_resources(agent: PragmaticProjectManagerAGIAgent, resources):
    # Implement resource allocation logic here

def monitor_progress(agent: PragmaticProjectManagerAGIAgent, status_reports):
    # Implement progress monitoring here

def manage_risks(agent: PragmaticProjectManagerAGIAgent, risk_analysis):
    # Implement risk management strategy here

def communicate_with_stakeholders(agent: PragmaticProjectManagerAGIAgent, communication_plan):
    # Implement communication strategy here

def ensure_quality(agent: PragmaticProjectManagerAGIAgent, quality_metrics):
    # Implement quality assurance measures here

def control_changes(agent: PragmaticProjectManagerAGIAgent, change_requests):
    # Implement change management here

def close_project(agent: PragmaticProjectManagerAGIAgent, closure_report):
    # Implement project closure procedures here

def evaluate_project_success(agent: PragmaticProjectManagerAGIAgent, evaluation_criteria):
    # Implement project success evaluation here

# Hyper advanced code for end-to-end project scaffolding with pytest
# Tracer Bullet End to End Project Scaffolding with Pytest and PragmaticProjectManagerAGIAgent
import pytest 
from PragmaticProgrammerAGIAgent import PragmaticProjectManagerAGIAgent, define_project_scope

def test_define_project_scope():
    agent = PragmaticProjectManagerAGIAgent(
        project_desc="My Project Description",
        project_dir="/path/to/project/"
    )
    parameters = {}
    define_project_scope(agent, parameters)
    
# Continue with other methods tests...
```
```

"How to Automate Your Next.js Project and Manage it with PragmaticProjectManagerAGIAgent"

Creating a Next.js app can seem daunting, especially if you're new to the world of JavaScript and React. However, with the help of automation and a project manager AGI, you can not only simplify this task but also manage your project effectively. This article will walk you through the entire process, step by step:

**Step 1**: Import the Required Modules in Python

The first thing you'll need to do is import the necessary modules. In your Python environment, run these codes to import:

```python
from typetemp.template.typed_template import TypedTemplate
from dataclasses import dataclass
import subprocess
import os
```

These modules will help you in creating, starting, and testing your Next.js application as well as running your project management functions. 

**Step 2**: Create Templated Classes

Next, you'll create your templated classes. This step involves creating four classes: NextJSAppCreationTemplate, NextJSAppStartTemplate, NextJSJestTestTemplate, and NextJSCypressTestTemplate. 

**Step 3**: Automate the Creation and Testing of Next.js App

Create a class NextJSAppAutomation with methods for creating your app, starting it, creating Jest tests, running Cypress tests, and a function for full automation. 

Note: When running the full automation method, make sure that your app is named appropriately. For this guide, we're going to use the name "my-nextjs-app". 

**Step 4**: Use the Chat Function

Now, you'll need to implement the 'chat' function in your script. This function is a robust and customizable method that facilitates interaction with the OpenAI API.

**Step 5**: Implement the PragmaticProjectManagerAGIAgent 

Next, you'll create a class called PragmaticProjectManagerAGIAgent using the @dataclass decorator from the dataclasses module, initialized with project description and directory.

**Step 6**: Use the Project Management Functions

Finally, you can define and use the project management tasks for your AGIAgent. These include defining the project scope, planning the schedule, allocating resources, monitoring progress, managing risks, sharing communication, ensuring quality, controlling changes, and evaluating project success. You will use the 'chat' function to write these results to a destined file.

Tada, you've done it! You've fully automated a Next.js app and managed it through AGIAgent. Automation in critical areas of your application can help you to focus on what matters: productive development. Take advantage of these tools to streamline your development processes and improve project management. Happy coding!

from dataclasses import dataclass
from typing import List
import os
import subprocess

@dataclass
class PragmaticProjectManagerAGIAgent:
    """
    PragmaticProjectManagerAGIAgent is a class that encapsulates all of the methods necessary for managing a project.
    """
    project_desc: str
    project_dir: str

    def __post_init__(self):
        """
        The __post_init__ method is a special method that gets called after the object has been initialized.
        """
        if not os.path.exists(self.project_dir):
            os.makedirs(self.project_dir)

    def _write_to_file(self, filename: str, content: str):
        """
        Private helper function to write content to a file within the project directory.
        """
        with open(os.path.join(self.project_dir, filename), 'a') as file:
            file.write(content)

    def define_project_scope(self, parameters: dict):
        """
        This method defines the project scope based on the given parameters. 
        It writes the scope to a file named 'scope.txt' within the project directory.
        """
        scope = ""
        for key, value in parameters.items():
            scope += f"{key}: {value}\n"
        self._write_to_file('scope.txt', scope)

    def plan_schedule(self, tasks: List[str]):
        """
        This method plans the project schedule based on the given tasks. 
        It writes the schedule to a file named 'schedule.txt' within the project directory.
        """
        schedule = ""
        for task in tasks:
            schedule += f"- {task}\n"
        self._write_to_file('schedule.txt', schedule)

    # Similar methods would exist for allocating_resources, monitor_progress, etc.
    # but are not included here to keep the code concise.


# To use the class, create an instance and use the methods as follows:

# Define the project description and directory
project_desc = "Example Project"
project_dir = "/path/to/project/directory"

# Create a PragmaticProjectManagerAGIAgent instance
agent = PragmaticProjectManagerAGIAgent(project_desc, project_dir)

# Define the project scope
parameters = {
    "Objective": "Create a new software product",
    "Deliverables": "The finished software product, and its source code",
    "Constraints": "Must be completed within 6 months with a budget of $500,000"
}
agent.define_project_scope(parameters)

# Plan the schedule
tasks = [
    "Gather requirements",
    "Design the software architecture",
    "Develop the software",
    "Test the software",
    "Deploy the software",
    "Maintain the software"
]
agent.plan_schedule(tasks)


class PragmaticPhilosophy:
    def __init__(self):
        self.pragmatic_philosophy_points = [
            "Care About Your Craft",
            "Think! About Your Work",
            "Provide Options, Don’t Make Lame Excuses",
            "Don’t Live with Broken Windows",
            "Be a Catalyst for Change",
            "Remember the Big Picture",
            "Make Quality a Requirements Issue",
            "Invest Regularly in Your Knowledge Portfolio",
            "Critically Analyze What You Read and Hear",
            "It’s Both What You Say and the Way You Say It"
        ]

    def review_philosophy(self):
        for point in self.pragmatic_philosophy_points:
            print(point)

if __name__ == "__main__":
    pragmatic = PragmaticPhilosophy()
    pragmatic.review_philosophy()


## How to Implement Pragmatic Philosophy in Your Programming Practices

The concept of Pragmatic Programming is one that is rooted in constantly envisioning the practical implementation of your projects. It was born out of the idea that developers should not just write code but think critically about their work, fix problems as soon as they arrive, and always consider the bigger picture. This article will guide you on how to implement the principles from the Pragmatic Philosophy in your programming practices.

**Step 1: Understand the Pragmatic Philosophy**

Before you can implement the Pragmatic Philosophy in your programming practices, you need to truly understand it. Start by learning the ten philosophies that drive this concept:

1. Care About Your Craft
2. Think! About Your Work
3. Provide Options, Don’t Make Lame Excuses
4. Don’t Live with Broken Windows
5. Be a Catalyst for Change
6. Remember the Big Picture
7. Make Quality a Requirements Issue
8. Invest Regularly in Your Knowledge Portfolio
9. Critically Analyze What You Read and Hear
10. It’s Both What You Say and the Way You Say It

**Step 2: Integrate the Philosophy Into Your Routine**

Once you truly understand these principles, start applying them to your routine.

Firstly, always be proactive in enhancing your skills (Care About Your Craft).

Secondly, before you write code, have a clear understanding of its purpose and how it integrates with the overall project (Think! About Your Work).

**Step 3: Embrace Accountability**

Understand that if a challenge arises, you should find ways to solve it instead of giving excuses (Provide Options, Don’t Make Lame Excuses).

Also, ensure you fix bugs as soon as they occur to prevent future problems (Don’t Live with Broken Windows).

**Step 4: Drive Changes and Reflect on Your Results**

Do not be afraid to introduce new practices or technologies (Be a Catalyst for Change) and always keep your end goal in sight (Remember the Big Picture).

**Step 5: Ensure Quality and Continuous Learning**

Hold yourself and your team to high quality standards (Make Quality a Requirements Issue). Always strive to learn new things and share your knowledge with others (Invest Regularly in Your Knowledge Portfolio).

**Step 6: Exercise Critical Thinking and Effective Communication**

Always question the validity of information to avoid invalid assumptions (Critically Analyze What You Read and Hear). Lastly, when you communicate, ensure you're clear and respectful to avoid unnecessary confusion or tension (It’s Both What You Say and the Way You Say It).

**Step 7: Regularly Review These Philosophies**

Frequent reviews will help you stay on course. You can even print out these philosophies and pin them on your work area!

Here's how you can create an automated review system using Python:

```python
class PragmaticPhilosophy:
    def __init__(self):
        self.pragmatic_philosophy_points = [
            "Care About Your Craft",
            "Think! About Your Work",
            "Provide Options, Don’t Make Lame Excuses",
            "Don’t Live with Broken Windows",
            "Be a Catalyst for Change",
            "Remember the Big Picture",
            "Make Quality a Requirements Issue",
            "Invest Regularly in Your Knowledge Portfolio",
            "Critically Analyze What You Read and Hear",
            "It’s Both What You Say and the Way You Say It"
        ]

    def review_philosophy(self):
        for point in self.pragmatic_philosophy_points:
            print(point)

if __name__ == "__main__":
    pragmatic = PragmaticPhilosophy()
    pragmatic.review_philosophy()
```

By following these seven steps, you're well on your way toward embracing the Pragmatic Programmer's Philosophy. Over time, these practices will become second nature, making you a more efficient and effective programmer.

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"How to Automate Your Next.js Project and Manage it with PragmaticProjectManagerAGIAgent"

Creating a Next.js app can seem daunting, especially if you're new to the world of JavaScript and React. However, with the help of automation and a project manager AGI, you can not only simplify this task but also manage your project effectively. This article will walk you through the entire process, step by step:

Step 1: Import the Required Modules in Python

The first thing you'll need to do is import the necessary modules. In your Python environment, run these codes to import:

```python
from typetemp.template.typed_template import TypedTemplate
from dataclasses import dataclass
import subprocess
import os
```

These modules will help you in creating, starting, and testing your Next.js application as well as running your project management functions.

Step 2: Create Templated Classes

Next, you'll create your templated classes. This step involves creating four classes: NextJSAppCreationTemplate, NextJSAppStartTemplate, NextJSJestTestTemplate, and NextJSCypressTestTemplate.

Step 3: Automate the Creation and Testing of Next.js App

Create a class NextJSAppAutomation with methods for creating your app, starting it, creating Jest tests, running Cypress tests, and a function for full automation.

Note: When running the full automation method, make sure that your app is named appropriately. For this guide, we're going to use the name "my-nextjs-app".

Step 4: Use the Chat Function

Now, you'll need to implement the 'chat' function in your script. This function

GPT error: unhashable type: 'slice'

```python
from dataclasses import dataclass
from typing import List
import os

@dataclass
class PragmaticProjectManagerAGIAgent:
    """
    PragmaticProjectManagerAGIAgent is a class that encapsulates all of the methods necessary for managing a project.
    """
    project_desc: str
    project_dir: str

    def __post_init__(self):
        """
        The __post_init__ method is a special method that gets called after the object has been initialized.
        """
        if not os.path.exists(self.project_dir):
            os.makedirs(self.project_dir)

    def _write_to_file(self, filename: str, content: str):
        """
        Private helper function to write content to a file within the project directory.
        """
        with open(os.path.join(self.project_dir, filename), 'a') as file:
            file.write(content)

    def define_project_scope(self, parameters: dict):
        """
        This method defines the project scope based on the given parameters. 
        It writes the scope to a file named 'scope.txt' within the project directory.
        """
        scope = ""
        for key, value in parameters.items():
            scope += f"{key}: {value}\n"
        self._write_to_file('scope.txt', scope)

    def plan_schedule(self, tasks: List[str]):
        """
        This method plans the project schedule based on the given tasks. 
        It writes the schedule to a file named 'schedule.txt' within the project directory.
        """
        schedule = ""
        for task in tasks:
            schedule += f"- {task}\n"
        self._write_to_file('schedule.txt', schedule)

    def allocate_resources(self, resources: dict):
        """
        This method allocates resources for the project based on the given resource allocation plan. 
        It writes the resource allocation plan to a file named 'resources.txt' within the project directory.
        """
        resource_plan = ""
        for key, value in resources.items():
            resource_plan += f"{key}: {value}\n"
        self._write_to_file('resources.txt', resource_plan)

    def monitor_progress(self, status_reports: List[str]):
        """
        This method monitors the progress of the project based on the given status reports. 
        It writes the status reports to a file named 'progress.txt' within the project directory.
        """
        progress = ""
        for report in status_reports:
            progress += f"- {report}\n"
        self._write_to_file('progress.txt', progress)

    def manage_risks(self, risk_analysis: dict):
        """
        This method manages the risks associated with the project based on the given risk analysis. 
        It writes the risk analysis to a

```python
class PragmaticPhilosophy:
    def __init__(self):
        self.pragmatic_philosophy_points = [
            "Care About Your Craft",
            "Think! About Your Work",
            "Provide Options, Don’t Make Lame Excuses",
            "Don’t Live with Broken Windows",
            "Be a Catalyst for Change",
            "Remember the Big Picture",
            "Make Quality a Requirements Issue",
            "Invest Regularly in Your Knowledge Portfolio",
            "Critically Analyze What You Read and Hear",
            "It’s Both What You Say and the Way You Say It"
        ]

    def review_philosophy(self):
        for point in self.pragmatic_philosophy_points:
            print(point)

if __name__ == "__main__":
    pragmatic = PragmaticPhilosophy()
    pragmatic.review_philosophy()
```

By implementing the Pragmatic Philosophy in your programming practices, you can improve your code quality, productivity, and overall success as a developer. Embrace these principles, continually review them, and strive to apply them in your work. Happy coding!

```python
# First let's develop a basic Tracer Bullet structure that can be iteratively refactored towards the final solution.

# Tracer Bullet for Problem Decomposition and Solution Generation

def identify_problem(problem_statement: str):
    """
    Given a problem statement, this function identifies the key components of the problem.
    """
    # TODO: Implement problem decomposition logic
    return []

def generate_solutions(problem_components: list):
    """
    Given a list of problem components, this function generates potential solutions in the form of pseudocode.
    """
    # TODO: Implement solution generation logic
    return []

def refine_pseudocode(pseudocode: list):
    """
    Given a list of pseudocode instructions, this function refines them into more precise instructions suitable for conversion into code.
    """
    # TODO: Implement pseudocode refinement logic
    return []

def convert_to_code(refined_pseudocode: list):
    """
    Given a list of refined pseudocode instructions, this function converts them into executable Python code.
    """
    # TODO: Implement code generation logic
    return ""

def tracer_bullet_development(problem_statement: str):
    """
    The main function that encapsulates the entire Tracer Bullet Development process from problem decomposition to code generation.
    """
    problem_components = identify_problem(problem_statement)
    pseudocode = generate_solutions(problem_components)
    refined_pseudocode = refine_pseudocode(pseudocode)
    code = convert_to_code(refined_pseudocode)
    return code

# Now we have a basic Tracer Bullet that we can iteratively improve based on the feedback we get, both from our own observations and from the feedback provided by end users/testers.

# Let's move on to creating real-time error detection
# Imaging a function called detect_errors which gets added to tracer_bullet_development function

def detect_errors(code: str):
    """
    Given a block of code, this function identifies potential errors or areas of improvement.
    """
    # TODO: Implement error detection logic
    return []

def tracer_bullet_development(problem_statement: str):
    """
    The main function that encapsulates the entire Tracer Bullet Development process from problem decomposition to code generation.
    """
    problem_components = identify_problem(problem_statement)
    pseudocode = generate_solutions(problem_components)
    refined_pseudocode = refine_pseudocode(pseudocode)
    code = convert_to_code(refined_pseudocode)
    errors = detect_errors(code)
    return code, errors

# The last critical function we still need is performance_improvements

def performance_improvements(code: str):
    """
    Given a block of code, this function identifies potential areas for performance improvement.
    """
    # TODO: Implement performance improvement detection logic
    return []

def tracer_bullet_development(problem_statement: str):
    """
    The main function that encapsulates the entire Tracer Bullet Development process from problem decomposition to code generation.
    It also identifies potential errors and performance improvements in the generated code.
    """
    problem_components = identify_problem(problem_statement)
    pseudocode = generate_solutions(problem_components)
    refined_pseudocode = refine_pseudocode(pseudocode)
    code = convert_to_code(refined_pseudocode)
    errors = detect_errors(code)
    improvements = performance_improvements(code)
    return code, errors, improvements
```
Now that we have the skeleton of the tracer bullet ready, we need to focus on implementing the functionality of each part independently, making adjustments as we proceed. After each function has been implemented, we can also implement unit tests to ensure that everything is functioning as expected, and gradually refactor the code towards the final system. After the system is ready, we need to also benchmark it against the user's expectations, striving to make it 9.232343243x better than what the user expects. This involves not only producing correct solutions, but also optimizing runtime and memory usage, creating an intuitive user interface or API, and generally making the problem-solving process as smooth as possible for the user. The ultimate goal is to make the system's output appear almost magical to the user, achieving results far beyond their initial expectations.

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