Begin by familiarizing yourself with Asana's API documentation. Asana offers a RESTful API that allows you to programmatically access your data. You need to generate a Personal Access Token from Asana for authentication. Navigate to your Asana account settings and create a token, which will be used in your API requests to authenticate and access your data.

Determine which data from Asana you need to move to Postgres. This may include tasks, projects, comments, or other relevant entities. Use the Asana API documentation to identify the appropriate endpoints for fetching this data. For instance, you might use the `/tasks` endpoint to fetch task-related data.

Develop a script in a language of your choice (such as Python, Node.js, or Ruby) to make HTTP requests to the Asana API using the endpoints identified. Use the Personal Access Token for authentication. Parse the JSON responses to extract the relevant data fields. For example, in Python, you can use the `requests` library to handle API requests and JSON parsing.

Set up your Postgres database if you haven't already. Create tables that correspond to the data structures you are exporting from Asana. Define appropriate columns and data types based on the data you extracted. For instance, if you're exporting task data, you might create a `tasks` table with columns for `task_id`, `name`, `status`, `created_at`, etc.

Before inserting the data into Postgres, ensure it matches the schema. This might involve data transformation, such as converting date formats or handling nested JSON objects. Write functions in your script to transform the data accordingly. Pay special attention to data types and ensure they align with those defined in your Postgres schema.

Extend your script to connect to your Postgres database using a suitable driver (like `psycopg2` for Python). Insert the transformed data into the respective tables. Use SQL `INSERT INTO` statements within your script to add the data row by row or in batches. Ensure you handle any potential exceptions or errors during the insertion process to maintain data integrity.

Once your script is tested and working, consider automating the process to keep your Postgres database up-to-date with Asana data. Use a scheduling tool like `cron` on Unix-based systems to run your script at regular intervals. Make sure to implement logging and error handling in your script to troubleshoot any issues that arise during automated runs.

By following these steps, you can effectively move data from Asana to a Postgres destination without relying on third-party connectors or integrations.