Customer Service & Retention AI

Revenue Operations

Revenue Operations is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to you are deciding what to improve…

Provider
Alireza Rezvani
Platforms
Codex · Claude Code · OpenClaw
View original link · GitHub

What this skill helps you do

Provides three Python CLI tools for SaaS revenue teams: a Pipeline Analyzer that calculates coverage ratios, conversion rates, deal velocity, aging risks, and concentration risks; a Forecast Accuracy Tracker that measures MAPE, detects systematic bias, and provides category-level breakdowns; and a GTM Efficiency Calculator that computes Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, and Net Dollar Retention with industry benchmarking. Includes templates for weekly pipeline reviews, forecast accuracy reviews, GTM efficiency audits, and comprehensive Quarterly Business Reviews.

Install and get started

Copy the full instructions into your AI tool. Test one low-risk example before connecting real store data.

Original Skill instructions

You are a Revenue Operations analyst for SaaS. Three Python tools on JSON data: (1) pipeline_analyzer.py—coverage ratio (healthy: 3-4x quota), stage conversion rates, sales velocity, deal aging (>2x avg cycle), concentration risk (>40% in single deal), coverage gap analysis. (2) forecast_accuracy_tracker.py—MAPE (<10% excellent, 10-15% good, 15-25% fair, >25% poor), over/under-forecast bias, weighted accuracy, period trends, category breakdowns by rep/product/segment. (3) gtm_efficiency_calculator.py—Magic Number (>0.75), LTV:CAC (>3:1), CAC Payback (<18mo), Burn Multiple (<2x), Rule of 40 (>40%), NDR (>110%). Cross-check all outputs against CRM/finance. Use templates for pipeline review, forecast reports, GTM dashboards.

Useful tasks

  • Weekly pipeline inspection with coverage ratios and aging deal detection
  • Monthly forecast accuracy reviews with MAPE tracking and bias analysis
  • Quarterly GTM efficiency audits for board presentations
  • Comprehensive QBR analysis combining pipeline, forecast accuracy, and GTM benchmarks
  • Identifying coverage gaps and concentration risks before they impact quarterly targets

How to use it

  • Always cross-check computed totals against your CRM source data before drawing conclusions
  • Combine all three tools for QBRs—pipeline forward, forecast backward, GTM efficiency
  • Flag deals aging beyond 2x average cycle time per stage for immediate intervention
  • Monitor concentration risk: when >40% of pipeline sits in one deal, diversify proactively
  • Use category breakdowns to identify which reps or segments need coaching

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