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-# Creating Challenges for Auto-GPT
-
-🏹 We're on the hunt for talented Challenge Creators! 🎯
-
-Join us in shaping the future of Auto-GPT by designing challenges that test its limits. Your input will be invaluable in guiding our progress and ensuring that we're on the right track. We're seeking individuals with a diverse skill set, including:
-
-🎨 UX Design: Your expertise will enhance the user experience for those attempting to conquer our challenges. With your help, we'll develop a dedicated section in our wiki, and potentially even launch a standalone website.
-
-💻 Coding Skills: Proficiency in Python, pytest, and VCR (a library that records OpenAI calls and stores them) will be essential for creating engaging and robust challenges.
-
-⚙️ DevOps Skills: Experience with CI pipelines in GitHub and possibly Google Cloud Platform will be instrumental in streamlining our operations.
-
-Are you ready to play a pivotal role in Auto-GPT's journey? Apply now to become a Challenge Creator by opening a PR! 🚀
-
-
-# Getting Started
-Clone the original Auto-GPT repo and checkout to master branch
-
-
-The challenges are not written using a specific framework. They try to be very agnostic
-The challenges are acting like a user that wants something done: 
-INPUT:
-- User desire
-- Files, other inputs
-
-Output => Artifact (files, image, code, etc, etc...)
-
-## Defining your Agent
-
-Go to https://github.com/Significant-Gravitas/Auto-GPT/blob/master/tests/integration/agent_factory.py
-
-Create your agent fixture.
-
-```python
-def kubernetes_agent(
-    agent_test_config, memory_json_file, workspace: Workspace
-):
-    # Please choose the commands your agent will need to beat the challenges, the full list is available in the main.py
-    # (we 're working on a better way to design this, for now you have to look at main.py)
-    command_registry = CommandRegistry()
-    command_registry.import_commands("autogpt.commands.file_operations")
-    command_registry.import_commands("autogpt.app")
-
-    # Define all the settings of our challenged agent
-    ai_config = AIConfig(
-        ai_name="Kubernetes",
-        ai_role="an autonomous agent that specializes in creating Kubernetes deployment templates.",
-        ai_goals=[
-            "Write a simple kubernetes deployment file and save it as a kube.yaml.",
-        ],
-    )
-    ai_config.command_registry = command_registry
-
-    system_prompt = ai_config.construct_full_prompt()
-    agent_test_config.set_continuous_mode(False)
-    agent = Agent(
-        # We also give the AI a name 
-        ai_name="Kubernetes-Demo",
-        memory=memory_json_file,
-        full_message_history=[],
-        command_registry=command_registry,
-        config=ai_config,
-        next_action_count=0,
-        system_prompt=system_prompt,
-        triggering_prompt=DEFAULT_TRIGGERING_PROMPT,
-        workspace_directory=workspace.root,
-    )
-
-    return agent
-```
-
-## Creating your challenge
-Go to `tests/challenges`and create a file that is called `test_your_test_description.py` and add it to the appropriate folder. If no category exists you can create a new one.
-
-Your test could look something like this 
-
-```python
-import contextlib
-from functools import wraps
-from typing import Generator
-
-import pytest
-import yaml
-
-from autogpt.commands.file_operations import read_file, write_to_file
-from tests.integration.agent_utils import run_interaction_loop
-from tests.challenges.utils import run_multiple_times
-
-def input_generator(input_sequence: list) -> Generator[str, None, None]:
-    """
-    Creates a generator that yields input strings from the given sequence.
-
-    :param input_sequence: A list of input strings.
-    :return: A generator that yields input strings.
-    """
-    yield from input_sequence
-
-
-@pytest.mark.skip("This challenge hasn't been beaten yet.")
-@pytest.mark.vcr
-@pytest.mark.requires_openai_api_key
-def test_information_retrieval_challenge_a(kubernetes_agent, monkeypatch) -> None:
-    """
-    Test the challenge_a function in a given agent by mocking user inputs
-    and checking the output file content.
-
-    :param get_company_revenue_agent: The agent to test.
-    :param monkeypatch: pytest's monkeypatch utility for modifying builtins.
-    """
-    input_sequence = ["s", "s", "s", "s", "s", "EXIT"]
-    gen = input_generator(input_sequence)
-    monkeypatch.setattr("autogpt.utils.session.prompt", lambda _: next(gen))
-
-    with contextlib.suppress(SystemExit):
-        run_interaction_loop(kubernetes_agent, None)
-
-    # here we load the output file
-    file_path = str(kubernetes_agent.workspace.get_path("kube.yaml"))
-    content = read_file(file_path)
-
-    # then we check if it's including keywords from the kubernetes deployment config
-    for word in ["apiVersion", "kind", "metadata", "spec"]:
-        assert word in content, f"Expected the file to contain {word}"
-
-    content = yaml.safe_load(content)
-    for word in ["Service", "Deployment", "Pod"]:
-        assert word in content["kind"], f"Expected the file to contain {word}"
-
-
-```