data-interpret

What problem does it solve?

Translating raw analysis outputs into clear, business-ready conclusions and reproducible deliverables is time-consuming and error-prone; this Skill standardizes interpretation, bias checks, robustness validation, and report generation so stakeholders receive actionable recommendations with quantified impact and documented limitations.

Core Features & Use Cases

• Fact → Insight → Action → Impact: Structure findings into evidence-backed insights and prioritized actions with estimated business impact.
• Validation & Robustness: Reproducibility checks, sensitivity analyses, cross-validation guidance, and common-sense domain checks to avoid spurious conclusions.
• Reporting & Visuals: Produce an executive summary and a technical report saved to data/docs, with recommended visualizations (ROC, SHAP, residuals) and an execution log for analysis_context.md.
• Use Case: From a churn prediction model, validate stability, produce an executive one-page summary for leadership, and a technical appendix for data science teams.

Quick Start

Generate an executive summary and a technical report from the current analysis_context.md and model outputs and save them to data/docs/07_executive_summary.md and data/docs/08_technical_report.md.