HubPublic
PackagePublished package

@pasar6987/sf-sales-forecasting

Revenue and pipeline forecasting with adjustable quotas, custom categories, submission workflows, and judgment-based overrides.

Published package · Latest published 1.0.1 Mar 4, 2026 · 23 datasets / 32 measures in the latest review · Updated Mar 11, 2026

Release path

1.0.1Published Mar 4, 2026

Publisher

@pasar6987Published Mar 4, 2026

Structure snapshot

23 datasets32 measures · 263 dimensions

Semantic Graph

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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

salesforcecrmsalesforecasting

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-sales-forecasting

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-sales-forecasting")
prompt = rawctx.to_prompt(
    "@pasar6987/sf-sales-forecasting",
    datasets=["ForecastingAdjustment", "ForecastingColumnDefinitionLocalization"],
    max_tokens=2000,
)

print(model.datasets)        # ["ForecastingAdjustment", "ForecastingColumnDefinitionLocalization", "ForecastingCustomCategory"]
print(model.measures)        # [Measure(name="ForecastingAdjustment.AdjustedAmount", ...), Measure(name="ForecastingAdjustment.AdjustedQuantity", ...), Measure(name="ForecastingCustomCategory.DisplayPosition", ...)]
print(model.dimensions)      # [Dimension(name="AdjustmentNote", ...), Dimension(name="CurrencyIsoCode", ...), Dimension(name="ForecastCategoryName", ...)]
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-sales-forecasting

Revenue and pipeline forecasting with adjustable quotas, custom categories, submission workflows, and judgment-based overrides.

Overview

Count
Objects (Datasets)23
Dimensions263
Measures32
Relationships11

Objects

  • ForecastingAdjustment — This object represents an individual forecast manager’s adjustment for a subordinate’s or child territory’s forecast via a ForecastingItem.
  • ForecastingColumnDefinitionLocalization — Represents the translated value of a custom calculated column or custom reference data column label when the Translation Workbench is enabled for your organization.
  • ForecastingCustomCategory — Represents a custom forecasting category used for forecast rollups.
  • ForecastingCustomData — Represents forecast data from external sources to display in the forecasts page. For example, risk or last year’s revenue.
  • ForecastingDisplayedFamily — Represents the table in Forecasts Settings where an admin selects the product families that users can forecast on in Lightning Experience.
  • ForecastingFact — This object is read-only and links a ForecastingItem with its opportunities, such as opportunities that share the same owner or forecast category and have a closing date within the period of the forecasting item.
  • ForecastingFilter — Represents the custom filter for including or excluding data from opportunity forecasts.
  • ForecastingFilterCondition — Represents the custom filter condition logic for including or excluding data from opportunity forecasts.
  • ForecastingGroup — Represents groups used to roll up forecast totals on the forecasts page.
  • ForecastingGroupItem — Represents the value within the picklist that is specified as the forecasting group for a forecast type. For example, if you have a forecasting group that identifies the industry an opportunity is part of, this object represents the value in the the industry picklist that’s chosen to be part of the group.
  • ForecastingItem — This object is read-only used for individual forecast amounts. Users see amounts based on their perspectives and forecast roles.
  • ForecastingOwnerAdjustment — This object represents an individual forecast user’s adjustment of their own forecast, including territory forecasts they own, via a ForecastingItem.
  • ForecastingQuota — This object represents an individual user’s or territory’s quota for a specified time period.
  • ForecastingSourceDefinition — Represents the object, measure, date type, and hierarchy that a forecast uses to project sales.
  • ForecastingSrcRecJudgment — Represents forecast managers’ judgment of whether they consider an opportunity-related deal to be certain to close.
  • ForecastingSubmission — Represents a submitted forecast.
  • ForecastingSubmissionItem — Represents the values for each forecast category in a submitted forecast.
  • ForecastingType — Represents a forecast type.
  • ForecastingTypeSource — Represents the mapping of a forecasting source definition to a forecast type.
  • ForecastingUserPreference — Represents the forecasting selections that a user has made, such as display options, date range, forecasting type, and currency.
  • FrcstCustmCatgRampRateSrc — Represents the total contract value used for custom bulk adjustments.
  • FrcstCustmzAdjustment — Represents an individual forecast manager’s adjustment of a subordinate’s consumption forecast.
  • FrcstCustmzOwnerAdjustment — Represents an individual forecast user’s adjustment of their own consumption forecast.

Install

rawctx snapshot-download @pasar6987/sf-sales-forecasting

Structure review

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

Models1
datasets23
measures32
dimensions263
relationships11
AI context1
models/sf-sales-forecasting.osi.yamlAI context included
23 datasets32 measures263 dimensions11 relationships