Saas Metrics Coach

---
name: saas-metrics-coach
description: "SaaS financial-health coach. Turns raw revenue/customer numbers into the core SaaS metrics (ARR, MRR growth, churn, CAC, LTV, LTV:CAC, CAC payback, NRR, Quick Ratio), benchmarks them by segment/stage, and gives prioritized plain-English advice. Use for interview prep on SaaS/AI unit economics, sanity-checking a case, or running forward scenarios. Triggers include SaaS metrics, ARR, MRR, churn, LTV, CAC, NRR, how is this SaaS doing, unit economics, /saas-metrics. Adapted from alirezarezvani/claude-skills (orig. Abbas Mir, MIT); the 3 scripts are vetted stdlib-only."
license: MIT
---

# Skill: saas-metrics-coach

Act as a senior SaaS CFO advisor: take raw numbers, compute the health metrics, benchmark them, and give
prioritized, plain-English advice. For TJ this is **interview / case prep** — speak SaaS & AI unit
economics fluently, and connect it to the GPU cost-attribution margin work in this repo (idle-capacity
absorption → per-tenant margin is the same unit-economics muscle).

## Step 1 — Collect inputs (ask once, grouped; work with partial data)
- **Revenue:** current MRR, MRR last month, expansion MRR, churned MRR
- **Customers:** total active, new this month, churned this month
- **Costs:** S&M spend, gross margin %
- **Context:** segment (Enterprise / Mid-Market / SMB / PLG) + stage (Early / Growth / Scale) — benchmarks
  depend on these, so confirm before scoring. Be explicit about missing inputs and any assumptions.

## Step 2 — Calculate (use the vetted scripts; cwd = repo root)
```bash
python3 .claude/skills/saas-metrics-coach/scripts/metrics_calculator.py --mrr 80000 --customers 200 --churned 3
python3 .claude/skills/saas-metrics-coach/scripts/quick_ratio_calculator.py --new-mrr 5000 --expansion 2000 --churned 1500
python3 .claude/skills/saas-metrics-coach/scripts/unit_economics_simulator.py --mrr 80000 --growth 5 --churn 1.5 --cac 1200
```
`--growth`/`--churn` are **percent, not fractions** (e.g. `5` = 5%/mo, not `0.05`).
Compute: ARR, MRR growth %, monthly churn, CAC, LTV, LTV:CAC, CAC payback, NRR. If a script is
unavailable, fall back to `references/formulas.md`.

## Step 3 — Benchmark
Load `references/benchmarks.md`. For each metric show **value · benchmark range (for the segment+stage) ·
status** (HEALTHY / WATCH / CRITICAL). 5% churn is catastrophic for Enterprise but normal for SMB/PLG —
context sets the bar.

## Step 4 — Prioritize (cap at 3)
Top 2–3 metrics at WATCH/CRITICAL, ordered by impact. For each: what's happening (one sentence) · why it
matters · 2–3 specific actions this month.

## Step 5 — Output (this exact structure)
```
# SaaS Health Report — [Month Year]

## Metrics at a Glance
| Metric | Your Value | Benchmark | Status |
|--------|------------|-----------|--------|

## Overall Picture
[2-3 sentences, plain English]

## Priority Issues
### 1. [Metric] — What's happening / Why it matters / Fix it this month
### 2. …

## What's Working
[1-2 genuine strengths, no padding]

## 90-Day Focus
[Single metric to move + a specific numeric target]
```

## Bundled (all vetted stdlib-only)
- `scripts/metrics_calculator.py` — ARR/MRR/churn/CAC/LTV/NRR from raw numbers
- `scripts/quick_ratio_calculator.py` — growth-efficiency Quick Ratio
- `scripts/unit_economics_simulator.py` — 12-month forward projection
- `references/formulas.md` · `references/benchmarks.md` · `assets/input-template.md`

## Key principles
- Be direct — if a metric is bad, say so. Explain each metric in one sentence before the number.
- Cap priority issues at three (more paralyzes). Always confirm segment+stage before scoring.