finance/skills/saas-metrics-coach/SKILL.md
SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing.
npx skillsauth add alirezarezvani/claude-skills saas-metrics-coachInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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.
If not already provided, ask for these in a single grouped request:
Work with partial data. Be explicit about what is missing and what assumptions are being made.
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:
scripts/quick_ratio_calculator.py when expansion/churn MRR data is availablescripts/unit_economics_simulator.py for forward-looking projectionsLoad references/benchmarks.md. For each metric show:
Match the benchmark tier to the user's market segment (Enterprise / Mid-Market / SMB / PLG) and company stage (Early / Growth / Scale). Ask if unclear.
Identify the top 2-3 metrics at WATCH or CRITICAL status. For each one state:
Order by impact — address the most damaging problem first.
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]
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.
references/formulas.md — All metric formulas with worked examplesreferences/benchmarks.md — Industry benchmark ranges by stage and segmentassets/input-template.md — Blank input form to share with usersscripts/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 projectionscripts/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
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:
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:
data-ai
Use when you want to understand what Claude contributed vs what you drove in a session. Triggers on: /collab-proof, session retrospective, ai contribution analysis, collaboration evidence, what did claude do.
data-ai
Personal coach that teaches users to become Claude power users. Use this skill the FIRST time a user asks to "learn Claude", "be a power user", "coach me", "teach me Claude tricks", "what can Claude do", "make me better at prompting", or any variation. After activation, also use it on EVERY subsequent turn to detect missed optimization opportunities (vague prompts, ignored capabilities, manual work Claude could automate) and surface a single power-user tip. Trigger generously — most users do not know what they do not know, so err on the side of coaching.
development
Use when designing or revisiting product pricing — selecting a pricing model (subscription seat-based, usage-based, value-based, freemium, or hybrid), running Van Westendorp Price Sensitivity Meter analysis on WTP survey data, or designing Good/Better/Best packaging tiers. Recommends a model and a price range with trade-offs, never a single number. For Commercial leads, Product Marketing, and CMOs at the pricing-design moment — not deal-by-deal discounting, not brand positioning.
testing
Use when a startup is approached by a prospective partner and someone has to decide should we sign this partner, at what partner tier (referral / reseller / OEM / SI-consulting / strategic alliance), with what joint GTM commitment, and at what revshare. Classifies partner tier from independent-demand evidence vs. preferential-terms hunting, designs a 90-day joint GTM plan, models revshare against direct-sale margin, and surfaces kill criteria for unwinding under-performing partnerships. For Head of Partnerships, Head of BD, and Founder-CEOs doing reseller agreement, OEM deal, or strategic alliance review — not technical sale enablement, not channel cost economics, not M&A.