This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user asks to "calculate MRR", "analyze churn", "compute SaaS metrics", "do cohort retention analysis", "calculate LTV or CAC", "evaluate unit economics", or "track subscription revenue growth".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
Before calculating, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
# 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
| 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 |
mrr_calculator.py to get current MRR, ARR, net new MRR, churn ratecohort_analyzer.py on user activity data to identify retention trendsunit_economics.py to validate LTV:CAC ratio stays above 3:1mrr_calculator.py --format json to get growth metrics for chartscohort_analyzer.py --format json for retention curvesunit_economics.py --format json for unit economics summarymrr_calculator.py with --breakdown to see churn by plan tiercohort_analyzer.py to identify which cohorts churn fastestSee references/saas-metrics-guide.md for comprehensive framework details.
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
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
{
"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
}
| 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% |
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This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.