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Load Saleshandy data to DuckDB

Build a Saleshandy to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Saleshandy API base URL, auth, endpoints, and incremental loading.

SourceSaleshandySaleshandy API | Saleshandy Help CenterDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Saleshandy is a sales engagement platform providing email outreach, prospect management, sequences, analytics and related automation via a REST API. Everything needed to build a working Saleshandy → 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 Saleshandy to DuckDB pipeline

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

Prompt
Run uvx dlthub-init@latest to build a pipeline from Saleshandy 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 Saleshandy 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.


Saleshandy API at a glance

Base URLhttps://open-api.saleshandy.com/v1
Example endpointGET v1/prospects
Records found atnotes
Authenticationall requests require an API key in the header — sent in the x-api-key header
PaginationPage-number page size via pageSize (for page-based endpoints) or take (for skip-based endpoints). The Saleshandy native REST API uses inconsistent pagination across endpoints. Some endpoints use 'page' and 'pageSize' (e.g., /prospects, /sequences), while others use 'skip' and 'take' (e.g., /prospects/{id}/notes). MindCloud documentation is a third-party wrapper and should be ignored in favor of the native developer.saleshandy.com documentation.
Incremental fieldcreatedAt
Record idid
API referencehttps://developer.saleshandy.com/api-reference/introduction

These values come from the Saleshandy API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Saleshandy API?

All requests require the API key to be passed in the x-api-key HTTP header.

1. Get your credentials

  1. Log in to your Saleshandy account at my.saleshandy.com. 2. Navigate to Settings in the sidebar. 3. Select API Key (or API). 4. Click 'Create API Key'. 5. Enter a descriptive label for your key (e.g., 'dlt-integration'). 6. Copy the generated key immediately, as it will only be displayed once.

2. Add them to .dlt/secrets.toml

[sources.saleshandy_source] api_key = "your_saleshandy_api_key_here"

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 Saleshandy data can I load into DuckDB?

These are the Saleshandy endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
prospects/v1/prospectsGETList all prospects
sequences/v1/sequencesGETList all sequences
prospect_notes/v1/prospects/{prospectId}/notesGETnotesList notes for a specific prospect
email_accounts/v1/email-accountsGETList connected email accounts
tasks/v1/tasksGETList all tasks

How do I load only new Saleshandy records?

Saleshandy exposes createdAt on v1/prospects, 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": "prospects", "endpoint": { "path": "v1/prospects", "data_selector": "notes", "incremental": {"cursor_path": "createdAt", "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 Saleshandy pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v1/sequences and /v1/prospects from the Saleshandy API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def saleshandy_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://open-api.saleshandy.com/v1", "auth": {"type": "api_key", "api_key": api_key, "name": "x-api-key", "location": "header"}, }, "resources": [ {"name": "prospects", "endpoint": {"path": "v1/prospects", "data_selector": "notes"}}, {"name": "sequences", "endpoint": {"path": "v1/sequences", "data_selector": "sequences"}} ], } yield from rest_api_resources(config) def load_saleshandy_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="saleshandy_pipeline", destination="duckdb", dataset_name="saleshandy_data", ) load_info = pipeline.run(saleshandy_source()) print(load_info) if __name__ == "__main__": load_saleshandy_to_duckdb()

Run it with python saleshandy_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 Saleshandy 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("saleshandy_pipeline").dataset() df = data.sequences.df() print(df.head())

SQL:

SELECT * FROM saleshandy_data.sequences LIMIT 10;

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


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

Book a demo →


What other destinations can I load Saleshandy data to?

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

DestinationExample 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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