# Revenue Operations

Use for pipeline coverage, forecast accuracy or GTM efficiency (magic number, CAC...

- Repository: https://github.com/alirezarezvani/claude-skills (29114 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: yes, review before use

## Install

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

## Description

Use for pipeline coverage, forecast accuracy or GTM efficiency (magic number, CAC payback). Runs pipeline_analyzer.py, forecast_accuracy_tracker.py and gtm_efficiency_calculator.py.

## 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.

> **Output formats:** All scripts support `--format text` (human-readable) and `--format json` (dashboards/integrations).

---

## 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

# Calculate GTM efficiency metrics
python scripts/gtm_efficiency_calculator.py assets/sample_gtm_data.json --format text
```

---

## Tools Overview

### 1. Pipeline Analyzer

Analyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks.

**Input:** JSON file with deals, quota, and stage configuration
**Output:** Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment

**Usage:**

```bash
python scripts/pipeline_analyzer.py --input pipeline.json --format text
```

**Key Metrics Calculated:**
- **Pipeline Coverage Ratio** -- Total pipeline value / quota target (healthy: 3-4x)
- **Stage Conversion Rates** -- Stage-to-stage progression rates
- **Sales Velocity** -- (Opportunities x Avg Deal Size x Win Rate) / Avg Sales Cycle
- **Deal Aging** -- Flags deals exceeding 2x average cycle time per stage
- **Concentration Risk** -- Warns when >40% of pipeline is in a single deal
- **Coverage Gap Analysis** -- Identifies quarters with insufficient pipeline

**Input Schema:**

```json
{
  "quota": 500000,
  "stages": ["Discovery", "Qualification", "Proposal", "Negotiation", "Closed Won"],
  "average_cycle_days": 45,
  "deals": [
    {
      "id": "D001",
      "name": "Acme Corp",
      "stage": "Proposal",
      "value": 85000,
      "age_days": 32,
      "close_date": "2025-03-15",
      "owner": "rep_1"
    }
  ]
}
```

The rest: https://github.com/alirezarezvani/claude-skills/blob/HEAD/business-growth/skills/revenue-operations/SKILL.md