Load Cloud bot data to DuckDB

Cloud BOT is a cloud-based robotic process automation (RPA) service that provides a RESTful API for managing bots, jobs, files, and contracts. Everything needed to build a working Cloud bot → 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.

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Build your Cloud bot to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Cloud bot 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 Cloud bot 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.

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Cloud bot API at a glance

These values come from the Cloud bot API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Cloud bot API?

Authentication requires an access token and a secret key, which are managed and issued via the Cloud BOT console; these are typically provided in headers or as request parameters depending on the specific endpoint implementation.

1. Get your credentials

1. Log into the Automation 360 Control Room as an administrator or a user with permissions to generate API keys. 2. Navigate to the API key management section within the Control Room interface to generate a new API key. 3. If required by your organization, ensure the user account is assigned the 'Generate API-key' role permission. 4. Use your username and the generated API key (or username and password) to POST a request to the Authentication API at {control_room_url}/v1/authentication to receive a JWT. 5. Securely store this JWT to use as the Bearer token in the 'X-Authorization' header for subsequent API requests.

2. Add them to .dlt/secrets.toml

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[sources.cloud_bot_source]
username = "your_username"
api_key = "your_api_key"

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.

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What Cloud bot data can I load into DuckDB?

These are the Cloud bot endpoints dlt can load into DuckDB:

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How do I load only new Cloud bot records?

The Cloud bot API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "automations", "endpoint": {
    "path": "v3/automations",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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.

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What does the generated Cloud bot pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading v3/automations and v3/automations/executions from the Cloud bot API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def cloud_bot_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.c-bot.pro/v5",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": access_token},
        },
        "resources": [
            {"name": "automations", "endpoint": {"path": "v3/automations", "data_selector": "automations"}},
            {"name": "executions", "endpoint": {"path": "v3/automations/executions", "data_selector": "executions"}}
        ],
    }
    yield from rest_api_resources(config)


def load_cloud_bot_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cloud_bot_pipeline",
        destination="duckdb",
        dataset_name="cloud_bot_data",
    )
    load_info = pipeline.run(cloud_bot_source())
    print(load_info)


if __name__ == "__main__":
    load_cloud_bot_to_duckdb()

Run it with python cloud_bot_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Cloud bot 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):

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import dlt

data = dlt.pipeline("cloud_bot_pipeline").dataset()
df = data.automations.df()
print(df.head())

SQL:

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SELECT * FROM cloud_bot_data.automations LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Cloud bot 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 Cloud bot loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Cloud bot data to?

dlt loads into any of these — only the destination argument changes:

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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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack