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

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 contr

...