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data-analyze

Execute a full-cycle CRISP-DM analysis workflow from Phase 0 through Phase 8.

Updated Mar 3, 2026
Search Tags:#reporting#modeling#data-analysis#eda#ssot#feature-engineering#crisp-dm
One-click install
npx skills add https://github.com/mutsumi-yamamoto/claude-data-analysis-marketplace --skill data-analyze
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyze
Source: https://github.com/mutsumi-yamamoto/claude-data-analysis-marketplace/tree/main/plugins/data-analysis/skills/data-analyze
Command: npx skills add https://github.com/mutsumi-yamamoto/claude-data-analysis-marketplace --skill data-analyze

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a guided, end-to-end workflow to perform rigorous data analysis from problem definition through modeling and validation, reducing ad-hoc decisions and ensuring reproducible, actionable results aligned with business goals.

Core Features & Use Cases

  • Full-cycle orchestration: Executes Phase 0–8 including issue definition, EDA, cleaning, feature engineering, modeling, interpretation, validation, and report generation.
  • SSOT-driven context: Loads analysis_context.md as the single source of truth to preserve context, hypotheses, and acceptance criteria across phases.
  • Governance & safety checks: Enforces user approval checkpoints, sample size estimation, bias controls, and reproducibility best practices.
  • Use Case: Turn raw project data and an analysis_context.md into an executive summary, technical report, and reproducible analysis scripts for stakeholder decision-making.

Quick Start

Use the data-analyze skill to run a full-cycle CRISP-DM analysis using the project's analysis_context.md and data directory, then produce executive and technical reports.

Frequently Asked Questions about data-analyze

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

FAQPage Schema
How do I execute a full-cycle CRISP-DM data analysis workflow from problem definition to reporting?▼

To execute end-to-end CRISP-DM data analysis, provide a structured dataset and an analysis_context.md file to define hypotheses and acceptance criteria, enabling phase-by-phase user approval checkpoints and report generation.

What is SSOT-driven context loading in data analysis and when do I need it?▼

SSOT-driven context loading is needed when performing multi-phase data analysis requiring consistent governance, bias controls, and reproducibility across exploratory analysis, feature engineering, and modeling stages.

Can I generate executive and technical reports from exploratory data analysis and modeling results?▼

The workflow produces both reproducible analysis scripts and exportable executive and technical reports after completing interpretation and validation phases for structured datasets.

Does the CRISP-DM workflow support user approval checkpoints and governance safety checks during feature engineering?▼

Governance safety checks are integrated across phase-by-phase user approvals, ensuring that feature engineering and model selection meet defined acceptance criteria and bias controls.

What's the best way to ensure reproducible model selection during a data analysis project?▼

Reproducible model selection is achieved by following a guided CRISP-DM extension workflow that enforces sample size estimation, data quality checks, and structured interpretation for structured datasets.

When should I not use an end-to-end CRISP-DM guided workflow for data analysis?▼

Avoid this workflow for rapid ad-hoc analysis tasks that do not require rigorous governance, reproducible model selection, SSOT-driven context loading, or exportable executive and technical reports.

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