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data360-prepare

Creates and manages Salesforce Data Cloud data streams, DLOs, transforms, and Document AI ingestion workflows.

Updated Jul 2, 2026
Search Tags:#data ingestion#etl#salesforce#data streams#data cloud#dlo#ingestion api
One-click install
npx skills add https://github.com/padjei/SF_Build --skill data360-prepare-padjei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data360-prepare
Source: https://github.com/padjei/SF_Build/tree/main/.claude/skills/data360-prepare
Command: npx skills add https://github.com/padjei/SF_Build --skill data360-prepare-padjei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyJWT, cryptography, requests.

What problem does it solve? Setting up ingestion into Salesforce Data Cloud involves many moving parts—data streams, Data Lake Objects (DLOs), transforms, Document AI, and unstructured sources—and choosing the wrong category or refresh path leads to broken pipelines. This Skill guides the full Prepare phase so ingestion assets are created, classified, and refreshed correctly. ## Core Features & Use Cases - Data Stream & DLO Management: Create, inspect, run, and delete data streams and DLOs via the sf data360 CLI, with guidance on Profile vs Engagement vs Other classification. - Unstructured & Document AI Ingestion: Handle SharePoint-style document sources, re-scan files with stream-level refresh, and configure unstructured DLO payloads. - Ingestion API Send-Data Flow: Includes a working Python example that authenticates via JWT and pushes records to the Ingestion API endpoint. - Use Case: A user asks to create a Data Cloud stream from the Contact object, verify the resulting DLO shape, and re-run the stream after new files arrive in a connected SharePoint source. ## Quick Start Ask the assistant to create a Data Cloud data stream from the Contact object in your org and inspect the DLO it generates.

Frequently Asked Questions about data360-prepare

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a Data Cloud data stream from a Salesforce object?▼

Use sf data360 data-stream create-from-object with the org alias, source object, and connection name, for example creating a stream from Contact via the SalesforceDotCom_Home connection. Confirm the dataset category (Profile, Engagement, or Other) before creation.

How do I re-scan a SharePoint document stream in Data Cloud?▼

Use sf data360 data-stream run with the stream name to trigger a stream-level refresh that picks up newly added or changed files. The connection-level run-existing command is not a reliable substitute for unstructured source rescans.

What is the difference between Profile, Engagement, and Other data stream categories?▼

Profile is for person or entity records requiring a primary key, Engagement is for time-based events requiring a primary key plus an event time field, and Other is for reference or supporting datasets requiring a primary key.

Can I send records to Data Cloud through the Ingestion API with Python?▼

Yes, the included send-data.py example authenticates through a staged JWT to Salesforce token to Data Cloud token flow, then posts records to the tenant ingestion endpoint. A 202 response means the payload was accepted for processing, not that records are immediately queryable.

Why does the CdpDataStreams error appear when running data-stream commands?▼

CdpDataStreams means the stream module is gated for the current org or user. The correct response is to review Data Cloud provisioning and permissions rather than retrying the command blindly.

When should I use the UI instead of the CLI for Data Cloud stream setup?▼

Some external database connectors and Ingestion API streams still require UI-based stream creation, and initial unstructured SharePoint-style setup provisions richer metadata through the UI. The CLI alone cannot guarantee a full creation path for every connector type.

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