Load StockX data to DuckDB

StockX is a marketplace platform providing a REST API for catalog searching, market data, and seller account order and listing management. Everything needed to build a working StockX → 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 StockX 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 StockX 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 StockX 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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StockX API at a glance

These values come from the StockX API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the StockX API?

The StockX API requires two headers: 'Authorization' with the value 'Bearer {ACCESS_TOKEN}' and 'x-api-key' with your assigned API key. An access token must be obtained first via the OAuth 2.0 authorization code flow.

1. Get your credentials

To obtain API credentials, first sign up or log in to your StockX account, then navigate to the StockX Developer Portal to complete the developer registration form. After your application is reviewed and approved (typically 5-7 business days), log in to the Developer Portal to access the 'Keys' and 'Applications' pages. There, you can create an application to generate your client_id and client_secret, and retrieve your automatically generated x-api-key.

2. Add them to .dlt/secrets.toml

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[sources.stockx_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
x_api_key = "your_x_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.

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What StockX data can I load into DuckDB?

These are the StockX endpoints dlt can load into DuckDB:

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How do I load only new StockX records?

The StockX 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": "catalog_search", "endpoint": {
    "path": "catalog/search",
    # 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 StockX pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading authorize and token from the StockX API into DuckDB:

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

@dlt.source
def stockx_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.stockx.com/v2",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "catalog_search", "endpoint": {"path": "catalog/search", "data_selector": "products"}},
            {"name": "order_single", "endpoint": {"path": "selling/orders/{orderNumber}"}}
        ],
    }
    yield from rest_api_resources(config)


def load_stockx_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="stockx_pipeline",
        destination="duckdb",
        dataset_name="stockx_data",
    )
    load_info = pipeline.run(stockx_source())
    print(load_info)


if __name__ == "__main__":
    load_stockx_to_duckdb()

Run it with python stockx_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 StockX 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("stockx_pipeline").dataset()
df = data.catalog_search.df()
print(df.head())

SQL:

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SELECT * FROM stockx_data.catalog_search LIMIT 10;

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

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How do I deploy the StockX 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 StockX 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 StockX 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