# Revenue Operations

Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM...

- Repository: https://github.com/borghei/Claude-Skills (1026 stars)
- Category: Engineering
- License: MIT + Commons Clause
- Last updated: 2026-10-23
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add borghei/Claude-Skills --skill revenue-operations -g -a claude-code -y`
Codex: `npx skills add borghei/Claude-Skills --skill revenue-operations -g -a codex -y`
Cursor: `npx skills add borghei/Claude-Skills --skill revenue-operations -g -a cursor -y`
Gemini CLI: `npx skills add borghei/Claude-Skills --skill revenue-operations -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add borghei/Claude-Skills`

## Description

Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Revenue Operations

Pipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams.

## Table of Contents

- [Quick Start](#quick-start)
- [Tools Overview](#tools-overview)
  - [Pipeline Analyzer](#1-pipeline-analyzer)
  - [Forecast Accuracy Tracker](#2-forecast-accuracy-tracker)
  - [GTM Efficiency Calculator](#3-gtm-efficiency-calculator)
- [Revenue Operations Workflows](#revenue-operations-workflows)
  - [Weekly Pipeline Review](#weekly-pipeline-review)
  - [Forecast Accuracy Review](#forecast-accuracy-review)
  - [GTM Efficiency Audit](#gtm-efficiency-audit)
  - [Quarterly Business Review](#quarterly-business-review)
- [Reference Documentation](#reference-documentation)
- [Templates](#templates)

---

## Clarify First

Before running the analysis, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

- **Which analysis:** pipeline health, forecast accuracy, or GTM efficiency (selects the script and its input schema).
- **Quota / target:** the number pipeline coverage and Magic Number are measured against.
- **Data export readiness:** deals with stage/value/age/close-date, or forecast-vs-actual periods (the tools consume specific JSON; forecast trend needs 3+ periods).
- **Company stage + sales motion:** seed vs growth, PLG vs enterprise (benchmarks vary widely by stage and motion).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

## Quick Start

```bash
# Analyze pipeline health and coverage
python scripts/pipeline_analyzer.py --input assets/sample_pipeline_data.json --format text

# Track forecast accuracy over multiple periods
python scripts/forecast_accuracy_tracker.py assets/sample_forecast_data.json --format text

The rest: https://github.com/borghei/Claude-Skills/blob/HEAD/business-growth/revenue-operations/SKILL.md