Begin by familiarizing yourself with the data structures used in Twilio TaskRouter. TaskRouter is a flexible tool for managing tasks within contact centers. Review the types of data you will be working with, such as tasks, workers, and workflows, to understand how they are structured and how you will extract them.

To access the data in TaskRouter, you'll need to use the Twilio REST API. Log into your Twilio account and obtain your Account SID and Auth Token. These credentials will allow you to authenticate your API requests and retrieve data from your TaskRouter workspace.

Write a script in a language of your choice (such as Python) to interact with the Twilio REST API. Use the appropriate endpoints to fetch data from TaskRouter, such as listing tasks or workers. Here is a simple example in Python:

```python
from twilio.rest import Client

# Your Account SID and Auth Token from twilio.com/console
account_sid = 'your_account_sid'
auth_token = 'your_auth_token'
client = Client(account_sid, auth_token)

# Fetch data from TaskRouter
tasks = client.taskrouter.workspaces('your_workspace_sid').tasks.list()

# Process and store the data in a suitable format, like JSON or CSV
task_data = [{'sid': task.sid, 'attributes': task.attributes} for task in tasks]
```

Install DuckDB on your system. DuckDB is an in-process SQL OLAP database management system that is simple to set up and use. You can install it via pip if you are using Python:

```bash
pip install duckdb
```

Convert the data extracted from Twilio TaskRouter into a format suitable for loading into DuckDB. If you saved the data in JSON or CSV format, ensure it is structured correctly. For instance, if you have task data, make sure each task's attributes are represented as columns.

Use DuckDB's SQL interface to load the prepared data. You can do this directly from your script. Here is an example using Python:

```python
import duckdb
import pandas as pd

# Convert your task data to a DataFrame
df = pd.DataFrame(task_data)

# Connect to DuckDB and create a new table
con = duckdb.connect(database=':memory:') # Use ':memory:' for an in-memory database or specify a file path
con.execute('CREATE TABLE tasks (sid VARCHAR, attributes JSON)')

# Load the DataFrame into DuckDB
con.execute('INSERT INTO tasks SELECT * FROM df')
```

After loading the data, perform queries in DuckDB to verify that the data has been imported correctly. You can run simple SELECT queries to check the count of records or inspect specific entries to ensure that all fields are populated as expected.

```sql
SELECT * FROM tasks LIMIT 10;
```

By following these steps, you can effectively move data from Twilio TaskRouter to DuckDB without relying on third-party connectors or integrations.