Load DeepSign data to DuckDB
Build a DeepSign to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the DeepSign API base URL, auth, endpoints, and incremental loading.
DeepSign provides a REST API for managing digital signatures, document workflows, and signature requests within the DeepBox ecosystem. Everything needed to build a working DeepSign → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.
Build your DeepSign to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from DeepSign to DuckDB and run it on dltHub
That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the DeepSign API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →
Prefer to write it yourself? Every fact the agent uses is below.
DeepSign API at a glance
| Base URL | https://api.sign.deepbox.swiss/api/v1 |
| Example endpoint | GET documents |
| Records found at | documents |
| Authentication | All requests require an OAuth2 access token sent as a Bearer token in the Authorization header — sent in the Authorization header, prefixed Bearer |
| Pagination | Not paginated |
| Incremental field | not specified |
| Record id | id |
| API reference | https://apidocs.deepcloud.swiss/deepsign-api-docs/index.html |
These values come from the DeepSign API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the DeepSign API?
All API requests require an 'Authorization' HTTP header with the value 'Bearer <access_token>'. The access token is obtained via the OAuth2 token endpoint using service user credentials.
1. Get your credentials
- Contact DeepCloud development (development@deepcloud.swiss) to apply for API access and receive onboarding instructions. 2. Once authorized, create a Service-User in the DeepCloud account. 3. Obtain your Partner-Service-Client-ID and Partner-Service-Client-Secret from the setup process provided by DeepCloud. 4. Use these credentials to authenticate against the Keycloak identity provider at https://deepcloud.swiss/auth/realms/sso/protocol/openid-connect/token to retrieve an access_token.
2. Add them to .dlt/secrets.toml
[sources.deepsign_source] client_id = "your_partner_service_client_id" client_secret = "your_partner_service_client_secret" username = "your_service_account_username" password = "your_service_account_password"
dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.
What DeepSign data can I load into DuckDB?
These are the DeepSign endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| documents | documents | GET | documents | List documents overview |
| overview | overview | GET | documents | Overview/list of documents with pagination and size |
| available_modes | available-modes | GET | Available signature modes | |
| document_details | documents/{documentId} | GET | Get specific document details | |
| users_me_seals | users/me/seals | GET | List user seals |
How do I load only new DeepSign records?
DeepSign exposes not specified on documents, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.
{"name": "documents", "endpoint": { "path": "documents", "data_selector": "documents", "incremental": {"cursor_path": "not specified", "initial_value": "2024-01-01T00:00:00Z"}, }}
On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.
What does the generated DeepSign pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading token and documents from the DeepSign API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def deepsign_source(token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.sign.deepbox.swiss/api/v1", "auth": {"type": "bearer", "token": token}, }, "resources": [ {"name": "documents", "endpoint": {"path": "documents", "data_selector": "documents"}}, {"name": "overview", "endpoint": {"path": "overview", "data_selector": "documents"}} ], } yield from rest_api_resources(config) def load_deepsign_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="deepsign_pipeline", destination="duckdb", dataset_name="deepsign_data", ) load_info = pipeline.run(deepsign_source()) print(load_info) if __name__ == "__main__": load_deepsign_to_duckdb()
Run it with python deepsign_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.
How do I query DeepSign data in DuckDB?
dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.
Python (pandas DataFrame):
import dlt data = dlt.pipeline("deepsign_pipeline").dataset() df = data.documents.df() print(df.head())
SQL:
SELECT * FROM deepsign_data.documents LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the DeepSign to DuckDB pipeline in production?
The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.
- Deploy & schedule — run the pipeline as a managed job with automatic retries.
- Monitor — observable job queues, alerting, and load metrics for every run.
- Transform — promote raw DeepSign loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load DeepSign data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example value |
|---|---|
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.
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