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data-interpret

Interpret analysis results and generate executive and technical reports.

Updated Mar 3, 2026
Search Tags:#validation#insights#reporting#visualization#reproducibility#result-interpretation
One-click install
npx skills add https://github.com/mutsumi-yamamoto/claude-data-analysis-marketplace --skill data-interpret
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-interpret
Source: https://github.com/mutsumi-yamamoto/claude-data-analysis-marketplace/tree/main/plugins/data-analysis/skills/data-interpret
Command: npx skills add https://github.com/mutsumi-yamamoto/claude-data-analysis-marketplace --skill data-interpret

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about data-interpret

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

FAQPage Schema
How do I generate an executive summary and technical report from model outputs?▼

To generate an executive summary and technical report, this Skill interprets model outputs and analysis_context.md, then exports structured markdown files with actionable insights, quantified impact, and documented limitations to your data/docs directory.

What is the best way to validate analysis results for reproducibility and robustness?▼

Validating analysis results for reproducibility involves running sensitivity analyses, cross-validation guidance, and common-sense domain checks to prevent spurious conclusions and ensure model stability before reporting.

How do I translate raw data analysis findings into actionable business insights?▼

Translating raw data analysis findings into actionable business insights uses a structured Fact, Insight, Action, and Impact framework to map evidence-backed conclusions to prioritized actions with estimated business impact.

Can I automate report generation with recommended visualizations like ROC and SHAP?▼

Yes, report generation includes recommended visualizations such as ROC curves, SHAP plots, and residual charts, alongside an execution log appended to analysis_context.md for full workflow tracking.

Does this approach work for producing separate reports for leadership and data science teams?▼

This approach supports producing a one-page executive summary tailored for leadership and a detailed technical appendix for data science teams, both saved as markdown files for stakeholder distribution.

What are the limitations of automating result interpretation and bias checks?▼

Limitations of automating result interpretation include the necessity of human oversight for domain-specific common-sense validations, as automated bias checks and robustness validations cannot fully replace expert contextual review.

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