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agency-analytics-reporter

Transform raw business data into analytics reports with SQL and Python.

Updated Apr 11, 2026
Search Tags:#analytics#python#dashboard#sql#attribution#segmentation#data-quality
One-click install
npx skills add https://github.com/omeraltn/ice_cream_website_testing --skill agency-analytics-reporter-omeraltn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agency-analytics-reporter
Source: https://github.com/omeraltn/ice_cream_website_testing/tree/main/.antigravity/agency-analytics-reporter
Command: npx skills add https://github.com/omeraltn/ice_cream_website_testing --skill agency-analytics-reporter-omeraltn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill reduces the time and expertise required to transform fragmented, low-quality business data into trustworthy analytics, dashboards, and executive recommendations that drive measurable decisions.

Core Features & Use Cases

  • Dashboard & KPI Tracking: SQL templates and metric definitions for monthly revenue, growth rates, active customers, average order value, and revenue per customer.
  • Customer Analytics & Segmentation: RFM, CLV calculations, and clustering workflows to identify Champions, Loyal Customers, At-Risk cohorts, and re-engagement opportunities.
  • Statistical Analysis & Forecasting: Regression, time-series forecasting, A/B test evaluation, confidence interval reporting, and significance testing to support decisions.
  • Marketing Attribution & ROI: Multi-touch attribution and campaign ROI analysis to allocate marketing spend and measure channel performance.
  • Reproducibility & Data Quality: Data validation checks, documented transformation steps, and reproducible workflows for reliable, auditable analyses.
  • Use Case: Produce an executive dashboard showing 12 months of revenue with growth classifications, customer segmentation, and campaign-attributed revenue to inform quarterly strategy.

Quick Start

Produce a 12-month revenue dashboard with monthly growth rates, active customer counts, customer segmentation insights, campaign-attributed revenue, and a one-paragraph executive summary with confidence levels.

Frequently Asked Questions about agency-analytics-reporter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate an executive dashboard from raw business data?▼

To generate an executive dashboard from raw business data, use SQL templates and Python statistical analysis to transform transactional datasets into concise reports with monthly growth rates, active customer counts, and campaign-attributed revenue.

What is the best way to perform customer segmentation and calculate CLV?▼

Customer segmentation and CLV calculation are best performed using RFM analysis and clustering workflows to identify cohorts like Champions and At-Risk customers, enabling targeted re-engagement strategies from transactional data.

How do I run marketing attribution analysis to measure campaign ROI?▼

Marketing attribution analysis to measure campaign ROI is run by applying multi-touch attribution models to event streams, evaluating channel performance and allocating marketing spend based on statistical significance checks.

Can I use Python and SQL for time-series forecasting and A/B test evaluation?▼

Python and SQL can be used for time-series forecasting and A/B test evaluation through regression workflows, confidence interval reporting, and significance testing to support data-driven decisions.

How do I ensure data quality validation in reproducible analytics reports?▼

Data quality validation in reproducible analytics reports is ensured by enforcing data validation checks, documenting transformation steps, and maintaining auditable workflows for reliable executive recommendations.

Do I need prior Python statistical knowledge to automate KPI tracking?▼

Prior Python statistical knowledge is not strictly required to automate KPI tracking, as the Skill provides predefined SQL templates and metric definitions for revenue, growth rates, and average order value.

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