Revenue Operations

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

.codex/skills/a11y-audit

.codex/skills/ab-test-setup

.codex/skills/account-executive

.codex/skills/ad-creative

.codex/skills/aeo

Audit 0.4.1 · source sha 6e3fcbdbb5cf · 19 Oct 2026Report an issue or request a re-scan

• Publisherborghei GitHub userOwner of the repository this skill is read from
• Source folderborghei/Claude-Skills/business-growth/revenue-operationsThe exact folder we audited
• LicenceNOASSERTIONAs the repository states it
• Commit pinned for this verdict6e3fcbdbb5cfA new audit runs when the source changes
• Repository statusNot archivedLast push 12 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

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

---
name: revenue-operations
description: >
  Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates
  GTM efficiency metrics for SaaS revenue optimization
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: business-growth
  domain: revenue-ops
  updated: 2026-03-31
  tags: [revops, pipeline, forecast, gtm-efficiency, saas-metrics]
---
# 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, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **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)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the output.

## 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
# Text report (human-readable)
python scripts/pipeline_analyzer.py --input pipeline.json --format text

# JSON output (for dashboards/integrations)
python scripts/pipeline_analyzer.py --input pipeline.json --format json
```

**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"
    }
  ]
}
```

### 2. Forecast Accuracy Tracker

Tracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.

**Input:** JSON file with forecast periods and optional category breakdowns
**Output:** MAPE score, bias analysis, trends, category breakdown, accuracy rating

**Usage:**

```bash
# Track forecast accuracy
python scripts/forecast_accuracy_tracker.py forecast_data.json --format text

# JSON output for trend analysis
python scripts/forecast_accuracy_tracker.py forecast_data.json --format json
```

**Key Metrics Calculated:**
- **MAPE** -- Mean Absolute Percentage Error: mean(|actual - forecast| / |actual|) x 100
- **Forecast Bias** -- Over-forecasting (positive) vs under-forecasting (negative) tendency
- **Weighted Accuracy** -- MAPE weighted by deal value for materiality
- **Period Trends** -- Improving, stable, or declining accuracy over time
- **Category Breakdown** -- Accuracy by rep, product, segment, or any custom dimension

**Accuracy Ratings:**
| Rating | MAPE Range | Interpretation |
|--------|-----------|----------------|
| Excellent | <10% | Highly predictable, data-driven process |
| Good | 10-15% | Reliable forecasting with minor variance |
| Fair | 15-25% | Needs process improvement |
| Poor | >25% | Significant forecasting methodology gaps |

**Input Schema:**

```json
{
  "forecast_periods": [
    {"period": "2025-Q1", "forecast": 480000, "actual": 520000},
    {"period": "2025-Q2", "forecast": 550000, "actual": 510000}
  ],
  "category_breakdowns": {
    "by_rep": [
      {"category": "Rep A", "forecast": 200000, "actual": 210000},
      {"category": "Rep B", "forecast": 280000, "actual": 310000}
    ]
  }
}
```

### 3. GTM Efficiency Calculator

Calculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.

**Input:** JSON file with revenue, cost, and customer metrics
**Output:** Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings

**Usage:**

```bash
# Calculate all GTM efficiency metrics
python scripts/gtm_efficiency_calculator.py gtm_data.json --format text

# JSON output for dashboards
python scr

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/borghei/Claude-Skills
git -C Claude-Skills checkout 6e3fcbdbb5cf

Works in

1. 2026-10-19
   !CAUTION B 89 / 100

   first audit · source 6e3fcbdbb5cf

Questions about Revenue Operations

• github.com/borghei/Claude-Skills/tree/main/business-growth/reven — the audited source at commit 6e3fcbdbb5cf and every finding aboveObserved · 19 Oct 2026

Spotted something wrong? Report a correction · Methodology