Connecting Datacloud

Name: Connecting Datacloud
Author: forcedotcom

forcedotcom/afv-library

519 installs
787 repo stars
Updated August 5, 2026
forcedotcom/afv-library

This is a copy of connecting-datacloud by forcedotcom - installs and ranking accrue to the original listing.

connecting-datacloud is a Salesforce Agentforce skill that manages Data Cloud source connections, tests connectors, browses schemas, and prepares ingestion payloads using the sf data360 CLI for developers who onboard new

About

connecting-datacloud is the Connect phase skill in forcedotcom/afv-library for Salesforce Data Cloud integration. It guides agents through sf data360 CLI workflows to create and test source connections, inspect connector metadata, browse source objects or databases, and stage ingestion payloads before harmonization or streaming steps. The skill requires the sf data360 CLI plugin and a Data Cloud-enabled Salesforce org, and it explicitly defers data streams and DLO work to preparing-datacloud, DMO and identity resolution to harmonizing-datacloud, retrieval to retrieving-datacloud, and STDM telemetry to observing-agentforce. Developers reach for connecting-datacloud when wiring CRM, warehouse, or SaaS sources into Data Cloud and validating connector health before downstream mapping, identity resolution, or agent retrieval pipelines run.

Handles all sf data360 connection * commands
Performs connector discovery, testing, and schema inspection
Supports Snowflake, SharePoint Unstructured, and Ingestion API sources
Manages connection metadata and source-object browsing
Delegates data streams, DLOs, DMOs and retrieval tasks to sibling skills

Connecting Datacloud by the numbers

519 all-time installs (skills.sh)
Data as of Aug 5, 2026 (Skillselion catalog sync)

npx skills add https://github.com/forcedotcom/afv-library --skill connecting-datacloud

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Installs	519
repo stars	★ 787
Last updated	August 5, 2026
Repository	forcedotcom/afv-library ↗

How do you configure Salesforce Data Cloud source connectors?

Manage Salesforce Data Cloud source connections, test connectors, browse schemas, and prepare ingestion payloads using the sf data360 CLI.

Who is it for?

Salesforce developers and integration engineers setting up or troubleshooting Data Cloud source connections with the sf data360 CLI plugin.

Skip if: Developers working on DMO harmonization, data streams, identity resolution, or Agentforce STDM telemetry without new source connector setup.

When should I use this skill?

The user manages Data Cloud connections, tests connectors, browses source objects, or sets up a new source system with sf data360.

What you get

Tested Data Cloud connector configs, browsed source schemas, and prepared ingestion payload definitions via sf data360 CLI.

validated connector configurations
source schema inventories
ingestion payload definitions

By the numbers

Part of a multi-skill afv-library Data Cloud workflow spanning at least four related phases

Files

SKILL.mdMarkdownGitHub ↗

connecting-datacloud: Data Cloud Connect Phase

Use this skill when the user needs source connection work: connector discovery, connection metadata, connection testing, source-object browsing, connector schema inspection, or connector-specific setup payloads for external sources.

When This Skill Owns the Task

Use connecting-datacloud when the work involves:

sf data360 connection *
connector catalog inspection
connection creation, update, test, or delete
browsing source objects, fields, databases, or schemas
identifying connector types already in use
preparing connector definitions for Snowflake, SharePoint Unstructured, or Ingestion API sources

Delegate elsewhere when the user is:

creating data streams or DLOs → preparing-datacloud
creating DMOs, mappings, IR rulesets, or data graphs → harmonizing-datacloud
writing Data Cloud SQL or search-index workflows → retrieving-datacloud

---

Required Context to Gather First

Ask for or infer:

target org alias
connector type or source system
whether the user wants inspection only or live mutation
connection name or ID if one already exists
whether credentials are already configured outside the CLI
whether the user also expects stream creation right after connection setup
whether the source is a database, an unstructured document source, or an Ingestion API feed

---

Core Operating Rules

Verify the plugin runtime first; see ../orchestrating-datacloud/references/plugin-setup.md.
Run the shared readiness classifier before mutating connections: node ../orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase connect --json.
Prefer read-only discovery before connection creation.
Suppress linked-plugin warning noise with 2>/dev/null for standard usage.
Remember that connection list requires --connector-type.
For connection test, pass --connector-type when resolving a non-Salesforce connection by name.
Discover existing connector types from streams first when the org is unfamiliar.
Use curated example payloads before inventing connector-specific credentials or parameters.
For connector types outside the curated examples, inspect a known-good UI-created connection via REST before building JSON.
Do not promise API-based stream creation for every connector type just because connection creation succeeds.

---

Recommended Workflow

1. Classify readiness for connect work

node ../orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase connect --json

2. Discover connector types

sf data360 connection connector-list -o <org> 2>/dev/null
sf data360 data-stream list -o <org> 2>/dev/null

3. Inspect connections by type

sf data360 connection list -o <org> --connector-type SalesforceDotCom 2>/dev/null
sf data360 connection list -o <org> --connector-type REDSHIFT 2>/dev/null
sf data360 connection list -o <org> --connector-type SNOWFLAKE 2>/dev/null

4. Inspect a specific connection or uploaded schema

sf data360 connection get -o <org> --name <connection> 2>/dev/null
sf data360 connection objects -o <org> --name <connection> 2>/dev/null
sf data360 connection fields -o <org> --name <connection> 2>/dev/null
sf data360 connection schema-get -o <org> --name <connection-id> 2>/dev/null

5. Test or create only after discovery

sf data360 connection test -o <org> --name <connection> --connector-type <type> 2>/dev/null
sf data360 connection create -o <org> -f connection.json 2>/dev/null

6. Start from curated example payloads for external connectors

Use the phase-owned examples before inventing a payload from scratch:

examples/connections/heroku-postgres.json
examples/connections/redshift.json
examples/connections/sharepoint-unstructured.json
examples/connections/snowflake-connection.json
examples/connections/ingest-api-connection.json
examples/connections/ingest-api-schema.json

Typical Ingestion API setup flow:

sf data360 connection create -o <org> -f examples/connections/ingest-api-connection.json 2>/dev/null
sf data360 connection schema-upsert -o <org> --name <connector-id> -f examples/connections/ingest-api-schema.json 2>/dev/null
sf data360 connection schema-get -o <org> --name <connector-id> 2>/dev/null

7. Discover payload fields for unknown connector types

Create one in the UI, then inspect it directly:

sf api request rest "/services/data/v66.0/ssot/connections/<id>" -o <org>

---

High-Signal Gotchas

connection list has no true global "list all" mode; query by connector type.
The connector catalog name and connection connector type are not always the same label.
connection test may need --connector-type for name resolution when the source is not a default Salesforce connector.
An empty connection list usually means "enabled but not configured yet", not "feature disabled".
Heroku Postgres, Redshift, Snowflake, SharePoint Unstructured, and Ingestion API all use different credential and parameter shapes; reuse the curated examples instead of guessing.
SharePoint Unstructured uses clientId, clientSecret, and tokenEndpoint in the credentials array and does not require a parameters array.
Snowflake uses key-pair auth and can often be created through the API, but downstream stream creation can still remain UI-only.
Ingestion API connector setup is incomplete until connection schema-upsert has uploaded the object schema.
Some external connector credential setup still depends on UI-side configuration or external-system permissions.

---

Output Format

Connect task: <inspect / create / test / update>
Connector type: <SalesforceDotCom / REDSHIFT / SNOWFLAKE / SPUnstructuredDocument / IngestApi / ...>
Target org: <alias>
Commands: <key commands run>
Verification: <passed / partial / blocked>
Next step: <prepare phase or connector follow-up>

---

References

README.md
examples/connections/heroku-postgres.json
examples/connections/redshift.json
examples/connections/sharepoint-unstructured.json
examples/connections/snowflake-connection.json
examples/connections/ingest-api-connection.json
examples/connections/ingest-api-schema.json
../orchestrating-datacloud/references/plugin-setup.md
../orchestrating-datacloud/references/feature-readiness.md
../orchestrating-datacloud/UPSTREAM.md

{
  "connectorType": "HerokuPostgres",
  "label": "My Heroku DB",
  "name": "My_Heroku_DB",
  "method": "Ingress",
  "credentials": [
    { "paramName": "credentialType", "value": "UsernamePasswordAuthentication" },
    { "paramName": "user", "value": "<HEROKU_DB_USER>" },
    { "paramName": "password", "value": "<HEROKU_DB_PASSWORD>" }
  ],
  "parameters": [
    { "paramName": "jdbc_connection_url", "value": "<HOST>:<PORT>" },
    { "paramName": "DATABASE", "value": "<DATABASE_NAME>" }
  ]
}

{
  "schemas": [
    {
      "label": "<OBJECT_NAME>",
      "name": "<OBJECT_NAME>",
      "schemaType": "IngestApi",
      "fields": [
        {
          "name": "Id",
          "label": "Id",
          "dataType": "Text"
        },
        {
          "name": "Name",
          "label": "Name",
          "dataType": "Text"
        },
        {
          "name": "Timestamp",
          "label": "Timestamp",
          "dataType": "DateTime"
        },
        {
          "name": "Value",
          "label": "Value",
          "dataType": "Number"
        }
      ]
    }
  ]
}

{
  "connectorType": "REDSHIFT",
  "label": "My Redshift",
  "name": "My_Redshift",
  "method": "Ingress",
  "credentials": [
    { "paramName": "authenticationOption", "value": "usernameAndPassword" },
    { "paramName": "username", "value": "<REDSHIFT_USER>" },
    { "paramName": "password", "value": "<REDSHIFT_PASSWORD>" }
  ],
  "parameters": [
    { "paramName": "url", "value": "<HOST>:<PORT>" },
    { "paramName": "database", "value": "<DATABASE_NAME>" },
    { "paramName": "hasPrivateNetworkRoute", "value": "false" }
  ]
}

{
  "connectorType": "SPUnstructuredDocument",
  "label": "My SharePoint Docs",
  "name": "My_SharePoint_Docs",
  "method": "Ingress",
  "credentials": [
    {
      "paramName": "clientId",
      "value": "<AZURE_APP_CLIENT_ID>"
    },
    {
      "paramName": "clientSecret",
      "value": "<AZURE_APP_CLIENT_SECRET>"
    },
    {
      "paramName": "tokenEndpoint",
      "value": "https://login.microsoftonline.com/<AZURE_TENANT_ID>/oauth2/v2.0/token"
    }
  ]
}

{
  "connectorType": "SNOWFLAKE",
  "label": "Snowflake Demo",
  "name": "Snowflake_Demo",
  "method": "Ingress",
  "credentials": [
    {
      "paramName": "credentialType",
      "value": "KeyPairAuthentication"
    },
    {
      "paramName": "user",
      "value": "<SNOWFLAKE_USERNAME>"
    },
    {
      "paramName": "privateKey",
      "value": "<YOUR_PRIVATE_KEY_WITHOUT_BEGIN_END_HEADERS>"
    }
  ],
  "parameters": [
    {
      "paramName": "accountUrl",
      "value": "https://<ORG_ID>-<ACCOUNT_ID>.snowflakecomputing.com"
    },
    {
      "paramName": "database",
      "value": "<DATABASE_NAME>"
    },
    {
      "paramName": "warehouse",
      "value": "<WAREHOUSE_NAME>"
    },
    {
      "paramName": "region",
      "value": "<AWS_REGION>"
    },
    {
      "paramName": "hasPrivateNetworkRoute",
      "value": "false"
    }
  ]
}

connecting-datacloud

Connection and connector workflows for Salesforce Data Cloud.

Use this skill for

listing available connector types
inspecting configured connections
testing a connection
browsing source objects, databases, fields, and uploaded schemas
preparing connector JSON for Snowflake, SharePoint Unstructured, and Ingestion API sources
preparing for stream creation

Key reminders

connection list requires --connector-type
connection test may need --connector-type for non-Salesforce name resolution
use connection schema-upsert after creating an Ingestion API connector
start with inspection before mutation
use 2>/dev/null to suppress linked-plugin warning noise
some connector types can be created by API while downstream stream creation still requires UI flow

Example requests

"Show me which Data Cloud connections already exist in this org"
"Test my Snowflake Data Cloud connection"
"What source objects are available on this Salesforce connector?"
"Help me create an Ingestion API connector and upload its schema"
"Set up a SharePoint Unstructured connection for document ingestion"

Common commands

sf data360 connection connector-list -o myorg 2>/dev/null
sf data360 connection list -o myorg --connector-type SalesforceDotCom 2>/dev/null
sf data360 connection get -o myorg --name SalesforceDotCom_Home 2>/dev/null
sf data360 connection test -o myorg --name Snowflake_Demo --connector-type SNOWFLAKE 2>/dev/null
sf data360 connection schema-get -o myorg --name <connector-id> 2>/dev/null
sf data360 connection create -o myorg -f examples/connections/heroku-postgres.json 2>/dev/null
sf data360 connection schema-upsert -o myorg --name <connector-id> -f examples/connections/ingest-api-schema.json 2>/dev/null

Example payloads

examples/connections/heroku-postgres.json
examples/connections/redshift.json
examples/connections/sharepoint-unstructured.json
examples/connections/snowflake-connection.json
examples/connections/ingest-api-connection.json
examples/connections/ingest-api-schema.json

References

SKILL.md
../orchestrating-datacloud/references/plugin-setup.md
../orchestrating-datacloud/UPSTREAM.md
CREDITS.md

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How it compares

Pick connecting-datacloud over harmonizing-datacloud when the task is wiring and validating source connectors, not mapping DMOs or resolving identities.

FAQ

What CLI does connecting-datacloud require?

connecting-datacloud requires the sf data360 CLI plugin installed locally and a Salesforce org with Data Cloud enabled. Agents use that CLI to manage source connections, test connectors, browse schemas, and prepare ingestion payloads during the Data Cloud Connect phase.

When should connecting-datacloud not be used?

connecting-datacloud should not be used for data streams or DLO setup, DMO harmonization, identity resolution, retrieval search, or STDM telemetry. Those tasks belong to preparing-datacloud, harmonizing-datacloud, retrieving-datacloud, and observing-agentforce respectively in the

Backend & APIsagentsautomation

About

Connecting Datacloud by the numbers

Add your badge

How do you configure Salesforce Data Cloud source connectors?

Who is it for?

When should I use this skill?

What you get

By the numbers

Files

connecting-datacloud: Data Cloud Connect Phase

When This Skill Owns the Task

Required Context to Gather First

Core Operating Rules

Recommended Workflow

1. Classify readiness for connect work

2. Discover connector types

3. Inspect connections by type

4. Inspect a specific connection or uploaded schema

5. Test or create only after discovery

6. Start from curated example payloads for external connectors

7. Discover payload fields for unknown connector types

High-Signal Gotchas

Output Format

References

Credits & Acknowledgments

connecting-datacloud

Use this skill for

Key reminders

Example requests

Common commands

Example payloads

References

Related skills

How it compares

FAQ

What CLI does connecting-datacloud require?

When should connecting-datacloud not be used?

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