First, determine the data you want from xkcd. xkcd provides JSON data for each comic through a simple URL: `https://xkcd.com/[comic_number]/info.0.json`. You can access the data using a script that retrieves JSON data via HTTP requests.

Write a Python script to loop through the xkcd comic numbers and extract the JSON data. Use libraries like `requests` to fetch data from each URL. Ensure your script handles exceptions and retries in case of network errors.

Parse the extracted JSON data and transform it into a structured format suitable for Redshift. Convert JSON fields into a tabular format (e.g., CSV) using Python libraries like `pandas`. This step involves standardizing data types and cleaning any inconsistencies.

Set up an Amazon Redshift cluster if you haven't already. Ensure you have the necessary IAM roles and permissions to access and manage Redshift. Create a new database and table schema that matches the structure of your transformed xkcd data.

Before loading data into Redshift, upload your transformed data files to an Amazon S3 bucket. Use AWS CLI or `boto3` library in Python to automate this step. Ensure the S3 bucket is in the same region as your Redshift cluster to minimize latency and transfer costs.

Use Redshift's `COPY` command to load data from S3 into your Redshift table. Connect to your Redshift cluster using a client like `psql` or a Python library such as `psycopg2`. Execute the `COPY` command with the necessary parameters, including the S3 file path, IAM role, and data format specifications.

After loading, verify the data in Redshift to ensure it matches your expectations. Perform data validation checks by querying the Redshift tables and comparing results against the original xkcd data. Address any discrepancies by revisiting the transformation and loading steps.

By following these steps, you can efficiently transfer data from xkcd to Amazon Redshift manually, without relying on any third-party connectors or integrations.