PackagePublished package

@pasar6987/sf-education-learning

Name: Semantic model package @pasar6987/sf-education-learning
Creator: pasar6987

Learning content, programs, pathways, achievements, equivalencies, and rules powering a flexible LMS.

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 · 15 datasets / 14 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

15 datasets14 measures · 176 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-education-learning

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-education-learning")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-education-learning",
    datasets=["Learning", "LearningAchievement"],
    max_tokens=2000,
)

print(model.datasets)        # ["Learning", "LearningAchievement", "LearningAchievementConfig"]
print(model.measures)        # [Measure(name="Learning.Duration", ...), Measure(name="LearningCourse.Duration", ...), Measure(name="LearningFoundationItem.Duration", ...)]
print(model.dimensions)      # [Dimension(name="AcademicLevel", ...), Dimension(name="ActiveFromDate", ...), Dimension(name="ActiveToDate", ...)]
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-education-learning

Learning content, programs, pathways, achievements, equivalencies, and rules powering a flexible LMS.

Overview

	Count
Objects (Datasets)	15
Dimensions	176
Measures	14
Relationships	0

Objects

Learning — Represents information that defines a training that can be made available as a course, program, or on-site experience, for a contact.
LearningAchievement — Represents information about the outcome of a learning activity.
LearningAchievementConfig — Represents the mapping details between a Learning Achievement type and a Learning Achievement record type.
LearningCourse — Represents information about a course that’s required in a learning framework.
LearningEquivalency — Represents the details of the equivalency between external and internal learnings.
LearningEqvAchvMapping — Represents the mapping between a learning equivalency, internal achievement group, and external achievement group.
LearningFoundationItem — Represents information about the prerequisite, co-requisite, or the recommended learning that’s required for a learning outcome.
LearningOutcomeItem — Represents information about the mapping between the learnings and the related outcome.
LearningPathwayTemplate — Represents a template that a learner can apply to create a planned learning path.
LearningPathwayTemplateItem — Represents a requirement with completion details in the Learning Pathway Template.
LearningPathwayTmplPgmPlan — Represents a junction between Learning Program Plan and Learning Pathway Template objects.
LearningProgram — Represents information about one or more trainings in a program that’s required to obtain a credential.
LearningProgramPlan — Represents details of a plan that’s created to execute a Learning Program.
LearningProgramPlanRqmt — Represents information about the requirements of a learning outcome that’s included in the learning program plan.
LearningRule — Represents a junction between the Learning and Rule (Expression Set) objects for an extensible rule on a learning.

Install

rawctx snapshot-download @pasar6987/sf-education-learning

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

datasets15

measures14

dimensions176

relationships0

AI context1

models/sf-education-learning.osi.yamlAI context included

15 datasets14 measures176 dimensions0 relationships