PackagePublished package

@pasar6987/sf-tooling-ai

Name: Semantic model package @pasar6987/sf-tooling-ai
Creator: pasar6987

Machine learning models, prediction engines, generative AI planners, Einstein insights, and intelligent scoring definitions that bring predictive and generative intelligence to the platform.

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 · 51 datasets / 3 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

51 datasets3 measures · 132 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-tooling-ai

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-tooling-ai")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-tooling-ai",
    datasets=["AIApplication", "AIApplicationConfig"],
    max_tokens=2000,
)

print(model.datasets)        # ["AIApplication", "AIApplicationConfig", "AIDataDefinition"]
print(model.measures)        # [Measure(name="AIApplicationConfig.Rank", ...), Measure(name="MLFilter.SortOrder", ...), Measure(name="MLPredictionDefinition.Priority", ...)]
print(model.dimensions)      # [Dimension(name="DeveloperName", ...), Dimension(name="FullName", ...), Dimension(name="Language", ...)]
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-tooling-ai

Machine learning models, prediction engines, generative AI planners, Einstein insights, and intelligent scoring definitions that bring predictive and generative intelligence to the platform.

Overview

	Count
Objects (Datasets)	51
Dimensions	132
Measures	3
Relationships	0

Objects

AIApplication — Represents an instance of a machine learning (ML) application.
AIApplicationConfig — Represents additional prediction information related to a machine learning (ML) application.
AIDataDefinition — Data Definition (Tooling API)
AIFactorComponent — AI Factor Component (Tooling API)
AIFilter — AI Filter (Tooling API)
AIFilterGroup — AI Filter Group (Tooling API)
AIFilterValue — AI Filter Value (Tooling API)
AIManagedField — Managed Field (Tooling API)
AIModel — AI Model (Tooling API)
AIModelDefinition — AI Model Definition (Tooling API)
AIModelFactor — AI Model Factor (Tooling API)
AIModelGraph — AI Model Graph (Tooling API)
AIModelMetric — AI Model Metric (Tooling API)
AIPredictionDefinition — Prediction Definition (Tooling API)
AIPredictionExpression — Prediction Expression (Tooling API)
AIPredictionField — Entity (Tooling API)
AIPredictionTarget — Prediction Target (Tooling API)
AIReplyRecommendationsSettings — Entity (Tooling API)
AIScoringModelDefVersion — AI Scoring Model Definition Version (Tooling API)
AIScoringModelDefinition — AI Scoring Model Definition (Tooling API)
AIScoringStep — AI Scoring Step (Tooling API)
Ai4mSettings — Entity (Tooling API)
AiPluginUtteranceDef — Generative AI Plugin Utterance Definition (Tooling API)
AiResponseFormatDef — AI Response Format Definition (Tooling API)
CustomizablePropensityScoringSettings — Entity (Tooling API)
EinsteinAgentSettings — Entity (Tooling API)
EinsteinAssistantSettings — Entity (Tooling API)
EinsteinDealInsightsSettings — Entity (Tooling API)
EinsteinDocumentCaptureSettings — Entity (Tooling API)
EinsteinGptSettings — Entity (Tooling API)
GenAiConvDefPlanner — Generative AI Conversation Definition Planner (Tooling API)
GenAiFunctionDefinition — Represents an agent action.
GenAiPlannerAttrDefinition — Generative AI Planner Attribute Definition (Tooling API)
GenAiPlannerDefinition — Represents an agent planner service that uses a large language model (LLM) and a reasoning strategy to decompose a given task into smaller subtasks, identify the most suitable actions for each subtask, and invoke them.
GenAiPlannerRuleExpr — Generative AI Planner Rule Expression (Tooling API)
GenAiPlannerRuleExprObject — Generative AI Planner Rule Expression Object (Tooling API)
GenAiPluginFunctionDef — Generative AI Plugin Function Definition (Tooling API)
GenAiPluginInstructionDef — Generative AI Plugin Instruction Definition (Tooling API)
IndustriesEinsteinFeatureSettings — Entity (Tooling API)
KnowledgeGenerationSettings — Entity (Tooling API)
MLDataDefinition — Represents a modeling data definition, which specifies the data used to create a model for a machine learning (ML) application. Examples of such data can include filters, fields to include, and fields to exclude.
MLField — Represents a field in a modeling data definition. A modeling data definition specifies the data used to create a model for a machine learning (ML) application.
MLFilter — Represents a data filter based on a data comparison in a machine learning (ML) application. For each comparison, there’s a left-hand element, an operator, and a right-hand element.
MLPredictionDefinition — Represents the details about a prediction within a prediction definition used in a machine learning (ML) application.
PredictionBuilderSettings — Entity (Tooling API)
PredictionDefinition — Prediction Definition (Tooling API)
PredictionDefinitionField — Prediction Definition Field (Tooling API)
RecommendationBuilderSettings — Entity (Tooling API)
RecommendationDefReference — RecommendationDefReference (Tooling API)
ServiceAISetupDefinition — Setup Definition (Tooling API)
ServiceAISetupField — Entity (Tooling API)

Install

rawctx snapshot-download @pasar6987/sf-tooling-ai

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

datasets51

measures3

dimensions132

relationships0

AI context1

models/sf-tooling-ai.osi.yamlAI context included

51 datasets3 measures132 dimensions0 relationships