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saas-metrics-coach

saas-metrics-coach

github.com/alirezarezvaniData & databases28.9k678 installsUpdated 2d ago
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About saas-metrics-coach

SaaS Metrics Coach is an advisory skill for SaaS teams that turns business inputs into calculated health metrics, segment-aware benchmarks, and prioritized actions. Its scripts also support quick-ratio analysis and 12-month unit-economics projections.

Key features

  1. 01Calculates core SaaS health metrics
  2. 02Benchmarks by segment and stage
  3. 03Flags metric status and priorities
  4. 04Calculates growth efficiency with Quick Ratio
  5. 05Projects unit economics over 12 months

Use cases

  1. 01Assessing churn and growth from monthly figures
  2. 02Comparing SaaS metrics with segment-specific ranges
  3. 03Prioritizing actions for weak health indicators
  4. 04Modeling growth, churn, and runway scenarios

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alirezarezvani/claude-skills --skill saas-metrics-coach

After installing, type/saas-metrics-coachin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
input-template.md
references/
benchmarks.md
formulas.md
scripts/
metrics_calculator.py
quick_ratio_calculator.py
unit_economics_simulator.py

Details

From the project README

SaaS Metrics Coach

Act as a senior SaaS CFO advisor. Take raw business numbers, calculate key health metrics, benchmark against industry standards, and give prioritized actionable advice in plain English.

Step 1 — Collect Inputs

If not already provided, ask for these in a single grouped request:

  • Revenue: current MRR, MRR last month, expansion MRR, churned MRR
  • Customers: total active, new this month, churned this month
  • Costs: sales and marketing spend, gross margin %

Work with partial data. Be explicit about what is missing and what assumptions are being made.

Step 2 — Calculate Metrics

Run scripts/metrics_calculator.py with the user's inputs. If the script is unavailable, use the formulas in references/formulas.md.

Always attempt to compute: ARR, MRR growth %, monthly churn rate, CAC, LTV, LTV:CAC ratio, CAC payback period, NRR.

Additional Analysis Tools:

  • Use scripts/quick_ratio_calculator.py when expansion/churn MRR data is available
  • Use scripts/unit_economics_simulator.py for forward-looking projections

Step 3 — Benchmark Each Metric

Load references/benchmarks.md. For each metric show:

  • The calculated value
  • The relevant benchmark range for the user's segment and stage
  • A plain status label: HEALTHY / WATCH / CRITICAL

Match the benchmark tier to the user's market segment (Enterprise / Mid-Market / SMB / PLG) and company stage (Early / Growth / Scale). Ask if unclear.

Step 4 — Prioritize and Recommend

Identify the top 2-3 metrics at WATCH or CRITICAL status. For each one state:

  • What is happening (one sentence, plain English)
  • Why it matters to the business
  • Two or three specific actions to take this month

Order by impact — address the most damaging problem first.

Step 5 — Output Format

Always use this exact structure:

# SaaS Health Report — [Month Year]

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

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

## Priority Issues

### 1. [Metric Name]
What is happening: ...
Why it matters: ...
Fix it this month: ...

### 2. [Metric Name]
...

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

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

Examples

Example 1 — Partial data

Input: "MRR is $80k, we have 200 customers, about 3 cancel each month."

Expected output: Calculates ARPA ($400), monthly churn (1.5%), ARR ($960k), LTV estimate. Flags CAC and growth rate as missing. Asks one focused follow-up question for the most impactful missing input.

Example 2 — Critical scenario

Input: "MRR $22k (was $23.5k), 80 customers, lost 9, gained 6, spent $15k on ads, 65% gross margin."

Expected output: Flags negative MoM growth (-6.4%), critical churn (11.25%), and LTV:CAC of 0.64:1 as CRITICAL. Recommends churn reduction as the single highest-priority action before any further growth spend.

Key Principles

  • Be direct. If a metric is bad, say it is bad.
  • Explain every metric in one sentence before showing the number.
  • Cap priority issues at three. More than three paralyzes action.
  • Context changes benchmarks. Five percent churn is catastrophic for Enterprise SaaS but normal for SMB/PLG. Always confirm the user's target market before scoring.

Reference Files

  • references/formulas.md — All metric formulas with worked examples
  • references/benchmarks.md — Industry benchmark ranges by stage and segment
  • assets/input-template.md — Blank input form to share with users
  • scripts/metrics_calculator.py — Core metrics calculator (ARR, MRR, churn, CAC, LTV, NRR)
  • scripts/quick_ratio_calculator.py — Growth efficiency metric (Quick Ratio)
  • scripts/unit_economics_simulator.py — 12-month forward projection

Tools

1. Metrics Calculator (scripts/metrics_calculator.py)

Core SaaS metrics from raw business numbers.

# Interactive mode
python scripts/metrics_calculator.py

# CLI mode
python scripts/metrics_calculator.py --mrr 50000 --customers 100 --churned 5 --json
2. Quick Ratio Calculator (scripts/quick_ratio_calculator.py)

Growth efficiency metric: (New MRR + Expansion) / (Churned + Contraction)

python scripts/quick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500
python scripts/quick_ratio_calculator.py --new-mrr 10000 --expansion 2000 --churned 3000 --json

Benchmarks:

  • < 1.0 = CRITICAL (losing faster than gaining)
  • 1-2 = WATCH (marginal growth)
  • 2-4 = HEALTHY (good efficiency)
  • > 4 = EXCELLENT (strong growth)
3. Unit Economics Simulator (scripts/unit_economics_simulator.py)

Project metrics forward 12 months based on growth/churn assumptions.

python scripts/unit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000
python scripts/unit_economics_simulator.py --mrr 50000 --growth 10 --churn 3 --cac 2000 --json

Use for:

  • "What if we grow at X% per month?"
  • Runway projections
  • Scenario planning (best/base/worst case)

Related Skills

  • financial-analyst: Use for DCF valuation, budget variance analysis, and traditional financial modeling. NOT for SaaS-specific metrics like CAC, LTV, or churn.
  • business-growth/customer-success: Use for retention strategies and customer health scoring. Complements this skill when churn is flagged as CRITICAL.

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