forecast-narrative

Generate a one-page sales forecast narrative from Salesforce and Gong data.

Updated May 2, 2026
One-click install
npx skills add https://github.com/marius-bughiu/ooligo --skill forecast-narrative
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: forecast-narrative
Source: https://github.com/marius-bughiu/ooligo/tree/main/apps/web/public/artifacts/forecast-narrative-skill
Command: npx skills add https://github.com/marius-bughiu/ooligo --skill forecast-narrative

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

RevOps teams waste hours each week manually compiling disjointed Salesforce pipeline data and Gong customer call records into consistent, exec-ready forecast briefs, leading to slow, error-prone narratives that fail to give sales leaders the clear, traceable context they need before forecast calls.

Core Features & Use Cases

  • Automated Data Synthesis: Combines Salesforce opportunity snapshots with Gong 14-day customer call activity to surface deal movement, supporting evidence, and hidden risks automatically.
  • Standardized Exec Output: Generates a one-page Markdown narrative with a headline commit number, confidence band, top 3 moving deals, single biggest risk, and specific ask for the VP/CRO, formatted for 2-minute exec review.
  • Built-in Quality Guardrails: Includes a mandatory hedge-word removal pass, snapshot audit trails, and source citation requirements to eliminate hallucinations and ensure every claim is traceable to Salesforce or Gong data. Use case: A RevOps analyst preparing a weekly enterprise AMER forecast brief can use this skill to cut prep time from 60 minutes to under 5, while ensuring the output meets the sales leadership team's strict formatting and traceability standards.

Quick Start

Use the forecast-narrative skill to generate a one-page forecast brief for the enterprise-amer segment for the week ending 2026-05-01, using Salesforce report ID 0050000000ABCDE, Gong workspace ID enterprise-amer, and April 2026 closed-won actuals of $9.8M against a $10.5M prior commit.

Frequently Asked Questions about forecast-narrative

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate an executive sales forecast brief from Salesforce and Gong data?

Generating an executive forecast brief requires combining Salesforce pipeline snapshots with Gong customer call activity records. This automated synthesis produces a standardized one-page narrative containing commit numbers, top deal movers, key risks, and executive asks for weekly RevOps forecast calls.

What is the best way to automate RevOps pipeline analysis for weekly forecast calls?

Automating RevOps pipeline analysis requires standardizing Salesforce and Gong data synthesis into a structured narrative. This approach eliminates manual compilation errors, automatically surfaces deal movers and risks, and enforces hedge-word removal with source citations for traceable executive review.

Do I need specific Salesforce report IDs and Gong workspace IDs to create a forecast narrative?

Yes, generating a forecast narrative requires Salesforce report IDs, Gong workspace IDs, prior-period actuals, and reference template files. These mandatory inputs ensure the output remains fully traceable to source data and aligned with executive forecast call standards.

Can I use Gong call activity data to identify pipeline risks in my B2B sales forecast?

Yes, Gong 14-day customer call activity data combined with Salesforce opportunity snapshots identifies pipeline risks. Synthesizing these sources automatically surfaces hidden risks, deal movement, and supporting evidence for B2B sales segments within your forecast narrative.

What limitations exist when automating a sales forecast narrative from CRM and call intelligence data?

Limitations include strict requirements for reference template files, prior-period actuals, and specific Salesforce and Gong identifiers. The process enforces mandatory hedge-word removal and source citation to prevent hallucinations, ensuring output cannot deviate from provided data traces.