Claude Data Analysis Assistant

A modern, intelligent data analysis platform built with Claude Code's sub-agents, slash-commands, skills, and hooks. Transform your data analysis workflow with AI-powered assistance and specialized analysis tools.

简单的一句话： 2个命令， /do-all 常规数据分析 ； /do-more 互联网数据分析 。 而分析数据是放在 /data_storage 。就这么简单，用起来吧！

注意： 下载项目下来，分析数据是放在 /data_storage [删去原来的demo数据] ，你需要先删除 complete_analysis 和 do_more_analysis 这两个文件夹。我这里放着是给你参考最终的分析结果，作为例子。

🚀 Quick Start

1. Set Up Your Data

Place your dataset in the data_storage/ directory:

cp your_data.csv ./data_storage/

2. Start Analysis

Use intuitive slash commands to analyze your data:

# Complete interactive workflow with human feedback checkpoints
/do-all

# ⭐ NEW: Automatic multi-skill analysis
/do-more

# Basic exploratory analysis
/analyze user_behavior_sample.csv exploratory

# Create visualizations
/visualize user_behavior_sample.csv all

# Generate analysis code
/generate python data-cleaning

# Create comprehensive report
/report user_behavior_sample.csv complete markdown

🎯 Key Features

⭐ /do-more vs /do-all: Which Should You Use?

/do-more: Automatic Multi-Skill Analysis

Best for: Quick, automated analysis without configuration

/do-more  # No parameters needed!

What it does:

• ✅ Automatically scans data_storage/ directory
• ✅ Identifies data types (e-commerce, user behavior, etc.)
• ✅ Intelligently matches 7+ relevant skills
• ✅ Executes skills in optimal order
• ✅ Generates comprehensive HTML report
• ✅ No human intervention required
• ✅ Fast execution (2-5 minutes)

Output: do_more_analysis/integrated_results/Comprehensive_Analysis_Report.html

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/do-all: Complete Interactive Analysis Workflow

Best for: Thorough analysis with human oversight and feedback

/do-all

What it does:

• ✅ Reads data from data_storage/ (no parameters needed!)
• ✅ 6-stage workflow with quality checks
• ✅ 3 Human feedback checkpoints at critical stages
• ✅ Interactive hypothesis generation
• ✅ Custom code generation
• ✅ Comprehensive documentation
• ✅ Multiple output formats (HTML, PDF, Markdown, DOCX)

Workflow Stages:

1. Data Quality Assessment → ⚠️ [human checkpoint #1] - Confirm data quality
2. Exploratory Analysis - Statistical summaries, patterns, trends
3. Hypothesis Generation → ⚠️ [human checkpoint #2] - Review research directions
4. Visualization → ⚠️ [human checkpoint #3] - Approve visualization strategy
5. Code Generation - Reproducible analysis pipeline
6. Report Generation - Comprehensive final report

Output Directory:

complete_analysis/
├── data_quality_report/          # Stage 1 output
├── exploratory_analysis/         # Stage 2 output
├── hypothesis_reports/           # Stage 3 output
├── visualizations/               # Stage 4 output
├── generated_code/               # Stage 5 output
├── final_report/                 # Stage 6 output
└── workflow_log/                 # Execution logs

Execution Time: 10-30 minutes (depends on data size)

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Comparison Summary

| Feature | /do-more | /do-all | |---------|-----------|-----------| | Data Source | Auto-scans data_storage/ | Reads from data_storage/ | | Parameters | None required | None | | Human Feedback | No | Yes (3 checkpoints) | | Execution Time | 2-5 minutes | 10-30 minutes | | Skills Used | 7+ auto-selected | Complete workflow (no skills) | | Output Format | HTML report | Multi-format (HTML/PDF/MD/DOCX) | | Code Generation | No | Yes (complete pipeline) | | Analysis Stages | Integrated execution | 6 separate stages | | Interactive | No | Yes (at checkpoints) | | Report Detail | Comprehensive | Extensive + technical | | Best For | Quick insights | Thorough analysis | | Customization | Automatic | Interactive |

Specialized Analysis Skills

12 domain-specific skills for expert-level analysis:

Customer Analysis:

• rfm-customer-segmentation - Customer value segmentation
• ltv-predictor - Lifetime value prediction
• retention-analysis - Customer retention and churn
• user-profiling-analysis - User behavior profiling

Marketing Analysis:

• attribution-analysis-modeling - Marketing attribution
• growth-model-analyzer - Growth hacking analysis
• ab-testing-analyzer - A/B test validation
• funnel-analysis - Conversion funnels

Data Analysis:

• data-exploration-visualization - Automated EDA
• regression-analysis-modeling - Predictive modeling
• content-analysis - Text and NLP analysis
• recommender-system - Recommendation engines

Intelligent Sub-Agents

• data-explorer: Expert statistical analysis and pattern discovery
• visualization-specialist: Beautiful, insightful charts and graphs
• code-generator: Production-ready analysis code
• report-writer: Comprehensive analysis reports
• quality-assurance: Data validation and quality control
• hypothesis-generator: Research hypothesis and insights

Intuitive Slash Commands

• /do-more - ⭐ RECOMMENDED Automatic multi-skill analysis (no parameters)
• /do-all - Complete interactive workflow with human feedback (no parameters)
• /analyze [dataset] [type] - Perform data analysis
• /visualize [dataset] [type] - Create visualizations
• /generate [language] [type] - Generate analysis code
• /report [dataset] [format] - Generate reports
• /quality [dataset] [action] - Quality assurance
• /hypothesis [dataset] [domain] - Generate hypotheses

Automated Workflows

• Data Validation: Automatic quality checks on data upload
• Smart Context: Project-aware analysis suggestions
• Reproducible Analysis: Complete documentation and code generation
• Beautiful Reports: HTML, Markdown, and PDF output formats

📊 Usage Examples

⭐ Automatic Multi-Skill Analysis

# Easiest way - no parameters needed!
/do-more

# Output (2-5 minutes):
# do_more_analysis/integrated_results/
# └── Comprehensive_Analysis_Report.html

Interactive Complete Analysis

# For thorough analysis with human feedback checkpoints
/do-all

# Includes:
# ✓ Data Quality Assessment → [your confirmation]
# ✓ Exploratory Analysis
# ✓ Hypothesis Generation → [your approval]
# ✓ Visualizations → [your review]
# ✓ Code Generation
# ✓ Comprehensive Report

E-commerce Data Analysis

# Quick automated analysis
/do-more

# Or specific customer analysis
/rfm-customer-segmentation olist_orders.csv
/ltv-predictor order_items.csv
/retention-analysis orders.csv customers.csv

User Behavior Analysis

# Complete analysis workflow
/analyze user_behavior.csv exploratory
/visualize user_behavior.csv trends
/quality user_behavior.csv clean
/report user_behavior.csv complete html
/generate python user-segmentation

Sales Data Analysis

# Sales performance analysis
/analyze sales_data.csv statistical
/visualize sales_data.csv trends
/generate sql revenue-analysis
/report sales_data.csv executive pdf

Customer Analytics

# Customer segmentation
/analyze customer_data.csv predictive
/visualize customer_data.csv distribution
/generate r clustering-analysis
/hypothesis customer_data churn-prediction

🛠️ Project Structure

claude-data-analysis/
├── .claude/
│   ├── agents/          # Sub-agent configurations
│   ├── commands/        # Slash command definitions
│   │   ├── do-more.md   # ⭐ NEW! Automatic multi-skill analysis
│   ├── hooks/          # Automation scripts
│   ├── settings.json   # Claude Code settings
│   └── skills/         # ⭐ 12 Specialized analysis skills
│       ├── rfm-customer-segmentation/
│       ├── ltv-predictor/
│       ├── retention-analysis/
│       ├── funnel-analysis/
│       ├── growth-model-analyzer/
│       ├── content-analysis/
│       └── ... (9 more skills)
├── data_storage/       # Your data files
│   ├── Orders.csv
│   ├── Customers.csv
│   └── ... (Olist datasets included)
├── do_more_analysis/   # ⭐ NEW! /do-more output directory
│   ├── skill_execution/  # Individual skill results
│   │   ├── data-exploration-visualization/
│   │   ├── rfm-customer-segmentation/
│   │   ├── ltv-predictor/
│   │   ├── retention-analysis/
│   │   ├── funnel-analysis/
│   │   ├── growth-model-analyzer/
│   │   └── content-analysis/
│   └── integrated_results/
│       └── Comprehensive_Analysis_Report.html  # ⭐ Interactive report
├── analysis_reports/   # Generated analysis reports
├── visualizations/     # Generated charts
├── generated_code/     # Analysis code
└── examples/          # Example datasets

🎨 Sample Data

The project includes Olist Brazilian E-commerce datasets in data_storage/:

• Orders.csv (99,441 records): Order information, status, timestamps
• Customers.csv (99,441 records): Customer demographics, location
• Order Items.csv: Order details, products, pricing
• Order Payments.csv: Payment methods, installments
• Products.csv: Product catalog, categories
• Reviews.csv (99,224 records): Customer reviews, ratings, comments
• Categories.csv: Product categories
• Sellers.csv: Seller information
• Geolocation.csv: Geographic data

Sample Workflow:

# 1. Data already in data_storage/
# 2. Run automatic analysis
/do-more

# 3. View results
# Open: do_more_analysis/integrated_results/Comprehensive_Analysis_Report.html

🔧 Configuration

Environment Setup

The project uses Claude Code's configuration system. Key settings:

1. Hooks: Automated validation and context loading
2. Sub-agents: Specialized AI assistants for different tasks
3. Commands: Custom slash commands for common operations

Requirements

• Python 3.8+ for data analysis
• Claude Code with sub-agents enabled
• Data files in CSV, JSON, or Excel format

📚 Getting Started Guide

For New Users

1. Place your data in data_storage/
2. **Run exploratory a