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nimrodfisher/data-analytics-skills211 installs

business-metrics-calculator

Standard business metric calculation with industry benchmarks. Use when calculating SaaS metrics (MRR, churn, LTV, CAC), e-commerce KPIs, or product analytics metrics with proper definitions.

How do I install this agent skill?

npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill business-metrics-calculator
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The business-metrics-calculator skill is a safe utility for defining and computing SaaS and e-commerce KPIs. It includes a Python script that performs standard arithmetic calculations on user-provided data without any network access, file system writing, or code execution risks.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • ZeroLeakspass

    Score: 93/100 · 2 sections analyzed

What does this agent skill do?

Business Metrics Calculator

When to use

  • Preparing a board or investor deck and need accurately defined metrics
  • The team disagrees on how a key metric (e.g., churn) should be calculated
  • Benchmarking performance against industry standards
  • Building a metrics report for a new business or new metric set
  • Validating that existing metric calculations match the standard definition

Process

  1. Identify the business model and period — confirm the model type (SaaS subscription, e-commerce, marketplace, product/app) and the calculation period (month, quarter, trailing 12M). Model type determines which metrics apply. See references/metric_definitions.md.
  2. Load and validate the underlying data — check for expected row counts, missing values, and plausible date ranges. A metrics report is only as good as the data feeding it.
  3. Calculate primary metrics — for SaaS: MRR, ARR, new MRR, churned MRR, expansion MRR, customer churn rate, revenue churn rate. For e-commerce: GMV, AOV, conversion rate, ROAS. Use scripts/saas_metrics.py or adapt for other models.
  4. Calculate unit economics — LTV (simple average and cohort-based), CAC, LTV:CAC ratio, payback period, and quick ratio. Document which assumptions were used for LTV lifetime.
  5. Compare to benchmarks — grade each metric against the industry benchmark thresholds in references/metric_definitions.md (good / average / poor). Flag anything outside the acceptable range.
  6. Produce the metrics report — assemble results into assets/metrics_report_template.md with trend charts, benchmark comparison, and 3–5 key insights. Document any definition choices that differ from industry standard.

Inputs the skill needs

  • Subscription or transaction data with at minimum: customer ID, date, value, status
  • Marketing spend data (for CAC calculation)
  • Monthly targets or goals (for vs-target comparisons)
  • The agreed-upon metric definitions (or default to industry standard)
  • Time period and any segmentation required (by plan, region, cohort)

Output

  • scripts/saas_metrics.py — calculates standard SaaS metrics from a subscriptions CSV; includes MRR waterfall, churn, LTV/CAC
  • references/metric_definitions.md — canonical definitions and benchmark thresholds by model type
  • assets/metrics_report_template.md — structured report: revenue metrics, customer metrics, unit economics, benchmark comparison, insights

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/nimrodfisher/data-analytics-skills/business-metrics-calculator">View business-metrics-calculator on skillZs</a>