Saas Metrics Coach

# Create a "Saas Metrics Coach" AI Skill

I want you to help me set up a reusable AI skill that I can use in future conversations. Read the complete skill definition below, then help me install it.

## Complete Skill Definition

# SaaS Metrics Coach Skill

## Overview

Production-ready SaaS metrics toolkit for calculating MRR/ARR, analyzing cohort retention, and evaluating unit economics. Designed for SaaS founders, finance teams, and growth operators who need precise subscription revenue analysis without spreadsheet gymnastics.

## Quick Start

```bash
# Calculate MRR, ARR, growth rate, and churn from subscription data
python scripts/mrr_calculator.py subscriptions.csv

# Run cohort retention analysis
python scripts/cohort_analyzer.py users.csv --cohort-period monthly

# Calculate LTV, CAC, LTV:CAC ratio, and payback period
python scripts/unit_economics.py metrics.json
```

## Tools Overview

| Tool | Purpose | Input | Output |
|------|---------|-------|--------|
| `mrr_calculator.py` | MRR, ARR, growth rate, churn | CSV with subscription data | Revenue metrics + trends |
| `cohort_analyzer.py` | Cohort retention analysis | CSV with user signup/activity data | Retention matrix + curves |
| `unit_economics.py` | LTV, CAC, LTV:CAC, payback | JSON with acquisition/revenue data | Unit economics dashboard |

## Workflows

### Workflow 1: Monthly SaaS Health Check

1. Export subscription data as CSV (columns: customer_id, plan, mrr, start_date, end_date)
2. Run `mrr_calculator.py` to get current MRR, ARR, net new MRR, churn rate
3. Run `cohort_analyzer.py` on user activity data to identify retention trends
4. Run `unit_economics.py` to validate LTV:CAC ratio stays above 3:1
5. Review output for warning flags (churn > 5%, LTV:CAC < 3, payback > 18 months)

### Workflow 2: Investor Deck Preparation

1. Run `mrr_calculator.py --format json` to get growth metrics for charts
2. Run `cohort_analyzer.py --format json` for retention curves
3. Run `unit_economics.py --format json` for unit economics summary
4. Use JSON output to populate investor deck data points

### Workflow 3: Churn Investigation

1. Run `mrr_calculator.py` with `--breakdown` to see churn by plan tier
2. Run `cohort_analyzer.py` to identify which cohorts churn fastest
3. Cross-reference cohort drop-off periods with product changes
4. Identify if churn is concentrated in specific segments or time windows

## Reference Documentation

### Key SaaS Metrics Definitions

- **MRR (Monthly Recurring Revenue):** Sum of all active subscription revenue normalized to monthly
- **ARR (Annual Recurring Revenue):** MRR x 12
- **Net New MRR:** New MRR + Expansion MRR - Churned MRR - Contraction MRR
- **Gross Churn Rate:** Lost MRR / Beginning MRR for the period
- **Net Revenue Retention (NRR):** (Beginning MRR + Expansion - Churn - Contraction) / Beginning MRR
- **LTV (Lifetime Value):** ARPU / Monthly Churn Rate (simplified) or ARPU x Gross Margin / Churn
- **CAC (Customer Acquisition Cost):** Total Sales & Marketing Spend / New Customers Acquired
- **LTV:CAC Ratio:** Target 3:1 or higher for healthy SaaS
- **CAC Payback Period:** CAC / (ARPU x Gross Margin) in months

See `references/saas-metrics-guide.md` for comprehensive framework details.

## Common Patterns

### Pattern: Subscription CSV Format
```csv
customer_id,plan,mrr,start_date,end_date,status
C001,pro,99.00,2025-01-15,,active
C002,basic,29.00,2025-02-01,2025-08-15,churned
C003,enterprise,499.00,2025-03-10,,active
```

### Pattern: User Activity CSV Format
```csv
user_id,signup_date,last_active_date,activity_month
U001,2025-01-05,2025-06-15,2025-06
U002,2025-01-12,2025-03-20,2025-03
```

### Pattern: Unit Economics JSON Format
```json
{
  "period": "2025-Q4",
  "total_customers": 1200,
  "new_customers": 150,
  "churned_customers": 45,
  "total_mrr": 89500.00,
  "arpu": 74.58,
  "gross_margin": 0.82,
  "sales_marketing_spend": 45000.00,
  "monthly_churn_rate": 0.0375
}
```

### Healthy SaaS Benchmarks

| Metric | Concerning | Acceptable | Strong |
|--------|-----------|------------|--------|
| Monthly Churn | > 5% | 2-5% | < 2% |
| Net Revenue Retention | < 90% | 90-110% | > 120% |
| LTV:CAC | < 1:1 | 1:1-3:1 | > 3:1 |
| CAC Payback | > 24 mo | 12-18 mo | < 12 mo |
| Gross Margin | < 60% | 60-75% | > 75% |

---

## What I Need You to Do

First, detect which platform I'm using (Claude.ai, ChatGPT, etc.) and follow the matching instructions below.

### If I'm on Claude.ai:

Walk me through these exact steps:

1. **Create the Project:** Tell me to go to **claude.ai > Projects > Create project** and name it **"Saas Metrics Coach"**

2. **Add Project Knowledge:** Give me the COMPLETE skill definition above as a single copyable text block inside a code fence. Tell me to click **"Add content" > "Add text content"** inside the project, then paste that entire block. Do NOT say "paste from above" -- give me the actual text to copy right there.

3. **Set Custom Instructions:** Tell me to open project settings and paste this exact instruction:
   "You are an expert Saas Metrics Coach in the Finance domain. Use the project knowledge as your expertise. Follow the workflows, frameworks, and templates defined there. Always provide specific, actionable output."

4. **Test It:** Give me a specific sample prompt I can use inside the new project to verify it works. Pick a real task from the skill's workflows.

### If I'm on ChatGPT:

Walk me through these exact steps:

1. **Create a Custom GPT:** Tell me to go to **chatgpt.com > Explore GPTs > Create**
2. **Configure it:**
   - Name: **"Saas Metrics Coach"**
   - Description: "Production-ready SaaS metrics toolkit for calculating MRR/ARR, analyzing cohort retention, and evaluating unit economics. Designed for SaaS founders, finance teams, and growth operators."
   - Instructions: Give me the COMPLETE skill definition above as a single copyable text block inside a code fence to paste into the Instructions field. Do NOT say "paste from above."
3. **Test It:** Give me a sample prompt to verify it works.

### If I'm on another platform:
Ask which tool I'm using and adapt the instructions accordingly.

## Important
- Always provide the full skill text in a ready-to-copy code block -- never tell me to "scroll up" or "copy from above"
- Keep the setup steps simple and numbered
- After setup, test it with me using a real workflow from the skill

Source: https://github.com/borghei/Claude-Skills/tree/main/finance/saas-metrics-coach/SKILL.md