Agent skill · Business & Finance

cro-advisor

Revenue leadership for B2B SaaS companies. Revenue forecasting, sales model design, pricing strategy, net revenue retention, and sales team scaling. Use when designing the revenue engine, setting quotas, modeling NRR, evaluating pricing, building board forecasts, or when user mentions CRO, chief revenue officer, revenue strategy, sales model, ARR growth, NRR, expansion revenue, churn, pricing strategy, or sales capacity.

Alireza Rezvani23,369★ · +428/wk · 1 repos on radarProfile →
claude-codecodexcursorships scriptsMIT
Install
npx skills add alirezarezvani/claude-skills --skill cro-advisor --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 9 KB
Bundled scripts: yes
Version: 1.0.0
Declared author: Alireza Rezvani
Path: c-level-advisor/skills/cro-advisor/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 23,791 · +422 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# CRO Advisor Revenue frameworks for building predictable, scalable revenue engines — from $1M ARR to $100M and beyond. ## Keywords CRO, chief revenue officer, revenue strategy, ARR, MRR, sales model, pipeline, revenue forecasting, pricing strategy, net revenue retention, NRR, gross revenue retention, GRR, expansion revenue, upsell, cross-sell, churn, customer success, sales capacity, quota, ramp, territory design, MEDDPICC, PLG, product-led growth, sales-led growth, enterprise sales, SMB, self-serve, value-based pricing, usage-based pricing, ICP, ideal customer profile, revenue board reporting, sales cycle, CAC payback, magic number ## Quick Start ### Revenue Forecasting ```bash python scripts/revenue_forecast_model.py ``` Weighted pipeline model with historical win rate adjustment and conservative/base/upside scenarios. ### Churn & Retention Analysis ```bash python scripts/churn_analyzer.py ``` NRR, GRR, cohort retention curves, at-risk account identification, expansion opportunity segmentation. ## Diagnostic Questions Ask these before any framework: **Revenue Health** - What's your NRR? If below 100%, everything else is a leaky bucket. - What percentage of ARR comes from expansi

What's inside
Steps it walks through
  1. Keywords
  2. Quick Start
  3. Revenue Forecasting
  4. Churn & Retention Analysis
  5. Diagnostic Questions
  6. Core Responsibilities (Overview)
  7. Revenue Metrics
  8. Board-Level (monthly/quarterly)
  9. Revenue Waterfall
  10. NRR Benchmarks
  11. Red Flags
  12. Integration with Other C-Suite Roles
  13. Resources
  14. Proactive Triggers
Ships with 5 files
  • references/nrr_playbook.md
  • references/pricing_strategy.md
  • references/sales_playbook.md
  • scripts/churn_analyzer.py
  • scripts/revenue_forecast_model.py
Commands it runs
python scripts/revenue_forecast_model.py
python scripts/churn_analyzer.py
More from claude-skills
All skills →
About this skill
What does the cro-advisor skill do?

Revenue leadership for B2B SaaS companies. Revenue forecasting, sales model design, pricing strategy, net revenue retention, and sales team scaling. Use when designing the revenue engine, setting quotas, modeling NRR, evaluating pricing, building board forecasts, or when user mentions CRO, chief revenue officer, revenue strategy, sales model, ARR growth, NRR, expansion revenue, churn, pricing strategy, or sales capacity.

How do I install it?

Run `npx skills add alirezarezvani/claude-skills --skill cro-advisor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going