Begin by accessing your Todoist data. You can utilize Todoist's API to retrieve data. Use an HTTP client like `curl` or create a script in a language such as Python to send requests to the Todoist API. Ensure you authenticate using your API token and request data such as tasks, projects, and labels.

Once you've extracted the data, transform it into a JSON format compatible with Elasticsearch. Ensure that each Todoist task or item is structured as a JSON object. You can use Python's `json` module to handle this transformation or write a custom script to format the data accordingly.

If you haven't already, install and configure an Elasticsearch instance. You can set it up locally or on a server. Follow the official Elasticsearch documentation to configure settings such as cluster name and network host.

Before you can add data, you need to create an index in Elasticsearch. Use the Elasticsearch API or Kibana Dev Tools to create an index that matches the structure of your Todoist data. Define the mappings to ensure the data types (e.g., text, date) are correctly interpreted.

Develop a script to upload the JSON-formatted data to your Elasticsearch instance. Use an HTTP client or library (such as Python's `requests`) to send POST requests to the Elasticsearch `_bulk` API endpoint. This allows you to upload multiple documents efficiently.

Before uploading all data, test the upload process with a small subset. Verify that the data appears correctly in Elasticsearch by querying the index using Kibana or the Elasticsearch API. Check that all fields are mapped properly and the data is searchable.

To keep your Elasticsearch data updated, consider automating the extraction and upload process. You can schedule the script using a task scheduler like cron (Linux) or Task Scheduler (Windows). This ensures your Elasticsearch instance stays in sync with Todoist data over time.

By following these steps, you can manually move data from Todoist to Elasticsearch without relying on third-party connectors or integrations.