PackagePublished package

@pasar6987/sf-education-courses

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

Course catalog with offerings, schedules, participant tracking, grading, rubrics, and relationships between sections.

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 / 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

9 datasets14 measures · 123 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-courses

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-courses")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-education-courses",
    datasets=["CourseCreditTransferAppln", "CourseOffering"],
    max_tokens=2000,
)

print(model.datasets)        # ["CourseCreditTransferAppln", "CourseOffering", "CourseOfferingParticipant"]
print(model.measures)        # [Measure(name="CourseCreditTransferAppln.LearningDuration", ...), Measure(name="CourseCreditTransferAppln.LearningNumericGrade", ...), Measure(name="CourseOffering.EnrolleeCount", ...)]
print(model.dimensions)      # [Dimension(name="ApprovedLearningEquivalencyId", ...), Dimension(name="ApprovedLearningId", ...), Dimension(name="CaseId", ...)]
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-courses

Course catalog with offerings, schedules, participant tracking, grading, rubrics, and relationships between sections.

Overview

	Count
Objects (Datasets)	9
Dimensions	123
Measures	14
Relationships	0

Objects

CourseCreditTransferAppln — Represents the details of a course credit transfer application.
CourseOffering — Represents an instance of a training course with location and date details of training courses.
CourseOfferingParticipant — Represents information about a student's enrollment in a Course Offering.
CourseOfferingPtcpResult — Represents the outcome of a student's participation in a course.
CourseOfferingRelationship — Represents a junction between a course offering and a related course offering.
CourseOfferingSchedule — Represents information about the schedule defined for a Course Offering.
CourseOfferingScheduleTmpl — Represents a template that can be used to define a course schedule.
CourseOfrPtcpActvtyGrd — https://gus.lightning.force.com/lightning/r/MDS_Entity__c/a7BEE000000QYE92AO/view
CourseOfrgRubricCriterion — Represents an activity rubric criterion for a course offering.

Install

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

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

measures14

dimensions123

relationships0

AI context1

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

9 datasets14 measures123 dimensions0 relationships