HubPublic
PackagePublished package

@pasar6987/sf-metadata-embedded-service

Configurable embedded service experiences with branding, custom components, flows, and chat integration.

Published package · Latest published 1.0.1 Mar 4, 2026 · 10 datasets / 2 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

10 datasets2 measures · 117 dimensions

Semantic Graph

Relationship counts appear after the graph loads.

Loading graph

Package graph is loading

Package relationships will appear when the summary is ready.

Reference context

Secondary package facts stay compact

Updated
Mar 11, 2026
Visibility
Public hub listing
License
MIT
Created
Mar 4, 2026

Reference facts

Secondary package facts after the usage path is clear

Licensing, categorization, ownership, and linked metadata stay below the runnable path so the page reads in the right order.

Format

OSI

Upstream

developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Repository

Not linked

Owner

@pasar6987

Organization

Independent

License

MIT

Visibility

Public hub listing

Publisher

@pasar6987Published Mar 4, 2026

Latest published version

1.0.1Published Mar 4, 2026

Tags

salesforcecrmmetadataembedded-service

Schema preview

Schema 0.1.1

SDK handoff

Use this package in code and AI

After structure review, move straight into typed reads with load() or compact LLM context with to_prompt().

Python SDK

Python example for @pasar6987/sf-metadata-embedded-service

This example uses the current package ref and, when preview data is available, fills in real dataset names from the published summary.

import rawctx

model = rawctx.load("@pasar6987/sf-metadata-embedded-service")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-metadata-embedded-service",
    datasets=["EmbeddedServiceBranding", "EmbeddedServiceConfig"],
    max_tokens=2000,
)

print(model.datasets)        # ["EmbeddedServiceBranding", "EmbeddedServiceConfig", "EmbeddedServiceCustomComponent"]
print(model.measures)        # [Measure(name="EmbeddedServiceMenuItem.DisplayOrder", ...), Measure(name="EmbeddedServiceQuickAction.Order", ...)]
print(model.dimensions)      # [Dimension(name="ContrastInvertedColor", ...), Dimension(name="ContrastPrimaryColor", ...), Dimension(name="DeveloperName", ...)]
print(model.relationships)   # [Relationship(name='...', ...)]
print(prompt)

README

Package narrative and examples

Use documentation after the package clears provenance, ownership, and runnable-path checks.

@pasar6987/sf-metadata-embedded-service

Configurable embedded service experiences with branding, custom components, flows, and chat integration.

Overview

Count
Objects (Datasets)10
Dimensions117
Measures2
Relationships0

Objects

  • EmbeddedServiceBranding — Salesforce standard object
  • EmbeddedServiceConfig — Represents a setup node for creating an Embedded Service deployment.
  • EmbeddedServiceCustomComponent — Represents a custom component created for an Embedded Service feature.
  • EmbeddedServiceCustomLabel — Represents a customized label that appears in the embedded component for a particular Embedded Service deployment. Labels can be customized for both Embedded Chat and embedded Appointment Management (beta).
  • EmbeddedServiceFlow — Represents a Flow Definition used by an Embedded Service deployment.
  • EmbeddedServiceFlowConfig — Represents whether an Embedded Service Flow feature is enabled or not.
  • EmbeddedServiceLiveAgent — Represents a setup node for creating an Embedded Chat deployment.
  • EmbeddedServiceMenuItem — Represents the information needed to configure a Channel Menu item.
  • EmbeddedServiceMenuSettings — Represents a setup node for creating a channel menu deployment. Channel menus list the ways in which customers can contact your business.
  • EmbeddedServiceQuickAction — Salesforce standard object

Install

rawctx snapshot-download @pasar6987/sf-metadata-embedded-service

Structure review

Inspect package structure after the usage path is clear

Use the structural review when you need the package footprint, field counts, and model paths before a deeper explorer pass.

Models1
datasets10
measures2
dimensions117
relationships0
AI context1
models/sf-metadata-embedded-service.osi.yamlAI context included
10 datasets2 measures117 dimensions0 relationships