PackagePublished package

@pasar6987/sf-core-live-agent

Name: Semantic model package @pasar6987/sf-core-live-agent
Creator: pasar6987

Live agent chat sessions, transcript events, telephony dialer usage, call center agent favorites, and supervisor skill configuration for real-time customer engagement.

OSISchema 0.1.1Domain crm Source developer.salesforce.com/docs/atlas.en-us.object_reference.meta/object_reference

Published package · Latest published 1.0.1 Mar 4, 2026 · 9 datasets / 0 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

9 datasets0 measures · 94 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

Domain

crm

Source

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

Owner

@pasar6987

Organization

Independent

License

MIT

Visibility

Public hub listing

Publisher

@pasar6987Published Mar 4, 2026

Latest published version

1.0.1Published Mar 4, 2026

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-core-live-agent

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-core-live-agent")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-core-live-agent",
    datasets=["AdditionalNumber", "CallCtrAgentFavTrfrDestShare"],
    max_tokens=2000,
)

print(model.datasets)        # ["AdditionalNumber", "CallCtrAgentFavTrfrDestShare", "ChatRetirementRdyMetrics"]
print(model.measures)        # [Measure(name="measure_a", ...)]
print(model.dimensions)      # [Dimension(name="CallCenterId", ...), Dimension(name="Description", ...), Dimension(name="Name", ...)]
print(model.relationships)   # [Relationship(name='...', ...)]
print(prompt)

See Python docs

README

Package narrative and examples

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

@pasar6987/sf-core-live-agent

Live agent chat sessions, transcript events, telephony dialer usage, call center agent favorites, and supervisor skill configuration for real-time customer engagement.

Overview

	Count
Objects (Datasets)	9
Dimensions	94
Measures	0
Relationships	0

Objects

AdditionalNumber — Represents an optional additional number for a call center. This additional number is visible in the call center's phone directory.
CallCtrAgentFavTrfrDestShare — Represents a sharing entry on a favorite transfer destination in the Omni-Channel softphone for voice call transfers. This object is available in API version 55.0 and later.
ChatRetirementRdyMetrics — Chat Retirement Readiness Metrics - ChatRetirementRdyMetrics
DialerCallUsage — Dialer Call Usages - DialerCallUsage
LiveAgentSessionHistory — This object is automatically created for each Chat session and stores information about changes made to the session.
LiveAgentSessionShare — This object is automatically created for each Chat session and stores information about the session.
LiveChatTranscriptEvent — Captures specific events that occur over the lifetime of a chat.
LiveChatTranscriptShare — Represents a sharing entry on a LiveChatTranscript object.
SupervisorAgentConfigSkill — SupervisorAgentConfigSkills - SupervisorAgentConfigSkill

Install

rawctx snapshot-download @pasar6987/sf-core-live-agent

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

datasets9

measures0

dimensions94

relationships0

AI context1

models/sf-core-live-agent.osi.yamlAI context included

9 datasets0 measures94 dimensions0 relationships