To access Slack data, first create a Slack App through the Slack API portal. Go to the Slack API website, create a new app, and choose the workspace you want to connect. Note down the OAuth token provided; you'll need this to authenticate your requests.

Determine which data you need to export, such as messages from a specific channel or user data. Familiarize yourself with Slack's API documentation to understand the endpoints required to fetch this data, like `conversations.history` for channel messages.

Create a script using a programming language like Python. Use an HTTP client library like `requests` to send GET requests to the Slack API endpoints. Include the OAuth token in your request headers for authentication. For example, to fetch channel messages, call the `conversations.history` endpoint with the channel ID.

Once you receive a response from the Slack API, parse the JSON data. Extract the relevant information you need, such as message text, timestamps, and user IDs. Use a JSON library to handle the parsing and ensure you handle any pagination for large datasets.

Organize the parsed data into a tabular structure suitable for CSV. Create a list of dictionaries or a list of lists, where each item represents a row in the CSV file. Define the headers (e.g., Message, Timestamp, User) based on the data fields you extracted.

Use a library like Python's `csv` module to open a new CSV file in write mode. Utilize the `csv.writer` or `csv.DictWriter` to write headers and rows to the file. Ensure you handle file encoding and newline characters to avoid formatting issues.

To keep your CSV data updated, automate the script execution using a task scheduler like `cron` on Linux or Task Scheduler on Windows. Configure it to run at desired intervals, ensuring your CSV file always contains the latest data from Slack.

By following these steps, you can manually move data from Slack to a CSV file without relying on third-party tools.