Campaign Analytics

This is a pure Python implementation of campaign analytics that runs locally with no dependencies, no API calls, and no ML models. It gives you five attribution models (first-touch through position-based), funnel conversion analysis with bottleneck detection, and ROI calculations across standard marketing metrics like ROAS, CPA, and CAC. The scripts take JSON input and spit out either human-readable tables or JSON for pipeline integration. What's refreshing is the included reference guides on attribution model selection and channel benchmarks, which actually help you interpret the numbers instead of just generating them. Best for marketing teams that want reproducible analytics they can version control and run in CI, though you'll need external tools for statistical significance testing on A/B tests.

skills.sh installs: 134
GitHub stars: 1.1K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add borghei/claude-skills --skill campaign-analytics --agent claude-code

Installs into .claude/skills of the current project.

Files

Campaign Analytics

Production-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.