| name | saas-metrics-health-check |
| description | Performs a diagnostic on a SaaS product's revenue and retention metrics from a CSV or spreadsheet of customers/subscriptions/events. Computes MRR, ARR, churn (gross/net), LTV, CAC, payback period, quick ratio, and cohort retention; flags anomalies; identifies the single highest-leverage fix. Invoked when the user asks "are my SaaS metrics healthy", "compute LTV for my data", "is my churn high", "review my MRR breakdown", or shares a billing/customer CSV. |
| short_desc | compute MRR/ARR/churn/LTV from customer CSV diagnostic |
| keywords | ["SaaS metrics","compute MRR","compute churn","payback period","cohort retention","unit economics","revenue metrics","SaaS health check","are my metrics healthy","quick ratio"] |
| model | opus |
| effort | high |
SaaS Metrics Health-Check (Opus)
Purpose: Turn a messy customer/subscription CSV into a defensible SaaS health report with a single recommended action.
Model: Opus 4.7.
When to invoke
Use this skill when the user:
- Hands over a billing export (Stripe, Lemon Squeezy, Paddle) and asks for a diagnostic
- Asks "is my churn high" or "what should LTV/CAC be at my stage"
- Wants a monthly metrics dashboard for the first time
- Is preparing a fundraise update or YC application and needs defensible numbers
- Shares MRR figures and asks "is this healthy"
Don't use this skill for:
- "What is MRR" — point to a tutorial; this skill assumes founder-literacy
- Real-time dashboards — this is a one-shot diagnostic; for ongoing tracking recommend Baremetrics / ChartMogul / a self-built dashboard
- Pre-revenue products (<5 paying customers) — sample size too small; do customer interviews instead
What this skill does
1. Data validation
Given a CSV, first verify:
- What's in the file: customers, subscriptions, charges, invoices, events?
- Date range covered and granularity (daily/monthly)
- Currency (assume one; if multi-currency flag for normalisation)
- Missing fields that block analysis (no churn timestamps, no plan info, no MRR field — request them)
- Deduplication issues (test customers, refunded charges, $0 transactions)
If the data is unusable, stop and ask for the right export. Don't fabricate fields.
Common providers and their exports:
- Stripe: Dashboard → Reports → Billing Overview, or
/v1/subscriptions API with expand[]=customer
- Lemon Squeezy: Dashboard → Reports → CSV export
- Paddle: Subscriptions list export with status + MRR
2. Core metric calculation
Compute and report with the exact definitions used:
- MRR (Monthly Recurring Revenue): sum of monthly-normalized active subscription value. Annual plans divided by 12.
- ARR: MRR × 12 (display only; not an operationally distinct number)
- New MRR: MRR from subscriptions created this month
- Expansion MRR: MRR added via upgrades from existing customers
- Contraction MRR: MRR lost via downgrades (still subscribed, lower plan)
- Churned MRR: MRR lost via cancellations
- Net New MRR = New + Expansion − Contraction − Churned
- Gross MRR Churn % = Churned MRR / MRR at start of month
- Net MRR Churn % = (Churned + Contraction − Expansion) / MRR at start of month
- Negative is great (expansion > churn)
- Logo (customer) churn % = Customers churned / Customers at start of month
- Quick Ratio = (New + Expansion) / (Churned + Contraction)
- ARPU = MRR / Active customers
- LTV (simple) = ARPU / Gross Monthly Churn
- Caveat: assumes churn is constant; over-estimates LTV when churn declines with tenure (it usually does — earliest customers churn fastest). Report alongside cohort-LTV (sum of revenue across a closed cohort) when data permits.
- CAC = Acquisition spend / New paying customers (ask user for the spend figure)
- LTV : CAC ratio = LTV / CAC (target >3 for SaaS health)
- CAC Payback (months) = CAC / (ARPU × Gross Margin)
- Solo founders: assume Gross Margin = 0.85 unless told otherwise (most SaaS run 70–90%)
3. Cohort retention table
Build a cohort table:
- Rows: signup month
- Columns: months since signup (M0, M1, M2, ..., M12)
- Cells: % of cohort still paying
This is the single most important table in SaaS metrics — it surfaces:
- Whether retention is improving over time (compare row-by-row at fixed column)
- Whether onboarding has a high M1 drop-off (M0→M1 cliff)
- Whether there's a delayed-activation curve (low M1 but high M6)
4. Diagnosis
Compare measured values against stage-appropriate benchmarks:
| Stage (MRR) | Monthly churn (B2C) | Monthly churn (B2B SMB) | LTV:CAC | Quick Ratio |
|---|
| <$1K | "Don't worry yet" | "Don't worry yet" | n/a | n/a |
| $1K–10K | <6% | <4% | >2 | >2 |
| $10K–50K | <5% | <3% | >3 | >3 |
| $50K+ | <4% | <2% | >3 | >4 |
(Benchmarks are rough industry medians; individual product economics vary.)
Flag:
- Any metric >2x worse than benchmark as a "blocker"
- Anomalies in cohort table (one bad month, one cliff)
- Definitional risks (e.g. if there's no annual subscription handling, churn % will be wrong)
5. The single recommended action
End the report with one prioritised action — not a list of 10 things. The point of a diagnostic is forcing prioritisation.
Examples of well-formed recommendations:
- "Your gross churn is 8% (benchmark 4%) and clusters at month 2. The onboarding sequence is your highest-leverage fix. Audit the signup → activation → first-value flow this week; everything else is downstream."
- "Quick ratio of 1.3 means acquisition barely outruns churn. Stop building features. Spend next 30 days on retention — turn on Stripe Smart Retries (involuntary churn), add a cancel survey (voluntary), add a pause-instead-of-cancel option."
- "LTV:CAC is 5.8 — you're under-investing in acquisition. Triple your ad spend on the channel with lowest CAC; you have 4 months of runway in the math even at 50% efficiency degradation."
Inputs needed from the user
If not provided, ask for:
- The data export (CSV preferred; can also work from numbers pasted as a table)
- Currency and whether the numbers are normalised
- Approx monthly acquisition spend (ads + content + tools attributable) — needed for CAC
- Approximate gross margin if unusual — needed for CAC payback
- Customer segment (B2C / prosumer / B2B SMB / B2B mid) — sets benchmark band
- Time horizon of the data — anything <3 months is too short for meaningful LTV
If they don't have export data, the skill should not invent numbers — instead pivot to "let's set up the tracking first" and recommend a 1-line dashboard.
Output format
# SaaS Health-Check: <product name>
## Headline
- MRR: $X
- Net MRR growth (last 3 mo avg): $Y/mo (Z%)
- Gross monthly churn: A%
- Quick ratio: B
- LTV:CAC: C
- Overall verdict: <Healthy / Acceptable / Concerning / Crisis>
## Metrics (with definitions)
<table: metric, value, benchmark, status>
## Cohort retention
<table or text-rendered grid>
## Anomalies
- <flagged issue 1, with month + size>
- <flagged issue 2>
## What changed vs last period
<if multi-period data was given>
## Single recommended action
<one paragraph; the highest-leverage next move>
## Open questions / data gaps
<things that would sharpen the diagnosis if available>
Data hygiene rules
- Never fabricate: if a metric requires data you don't have, mark it
n/a — need X
- Show your work: state the definition used for each metric (LTV is especially ambiguous)
- Flag double-counting: if the CSV mixes successful and refunded charges, separate them
- Currency: don't sum across currencies without normalisation; flag if mixed
Required reading before using this skill
knowledge/concepts/churn-taxonomy-and-tactics.md — for interpreting churn breakdownsknowledge/concepts/saas-pricing-psychology.md — pricing as a churn causeknowledge/concepts/north-star-metric-selection.md — the NSM is upstream of revenue metrics
Anti-patterns to push back on
- "Just give me the LTV" with a 4-week-old product — refuse; LTV is meaningless before 6+ months of churn data
- "Compare me to <Notion / Stripe / Slack>" — those are post-product-market-fit benchmarks, irrelevant for an indie
- "I want to look good for investors" — the diagnostic isn't a marketing exercise; if the metrics are weak, the right move is to fix them before the deck, not to spin them
- LTV:CAC > 10 — usually means under-investing in acquisition, not "great economics"
Knowledge Systems
Full reference: ~/.claude/shared/KNOWLEDGE_SYSTEMS.md
Decision tree:
- SaaS-metric definitions and patterns →
hybrid_search("SaaS metrics LTV churn definition") (Weaviate MCP)
- Stage-appropriate benchmarks → consult the table above + KG nodes
- Data parsing → standard Python via Bash; pandas if available, csv module if not
Success criteria
This skill is working well if:
- Every metric has the definition used printed next to the value
- The recommendation is one action, not a list
- Anomalies are flagged with month + magnitude, not "high"
- Output is short enough to read in 3 minutes
- The founder can take the recommendation and start work the same day
