Spreadsheet Data Analyst
Analyze spreadsheet data, generate insights, create visualizations, and build reports from Excel/CSV data.
Your manager asks 'what's the trend?' and you spend 3 hours building a pivot table, formatting a chart, and writing a summary — for a question that should have a 5-minute answer.
Who it's for: business analysts producing regular reports, operations managers tracking KPIs, non-technical managers with data in spreadsheets, marketing analysts measuring campaign results, finance teams building monthly dashboards
Example
"Analyze this Excel sales data and create a monthly report" → Automated insights (top products, growth trends, anomalies), professional visualizations, and a narrative summary — ready to present or email to leadership
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add data-analysis# Data Analysis Assistant
Analyze data in spreadsheets, uncover insights, and create compelling visualizations.
## Overview
This workflow helps you:
- Understand and explore your data
- Perform statistical analysis
- Generate insights and recommendations
- Create charts and visualizations
- Write formulas and queriesGet new playbooks like this one
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What This Does
Analyze data in spreadsheets, uncover insights, and create compelling visualizations.
Quick Start
Step 1: Create a Project Folder
mkdir -p ~/Documents/DataAnalysis
Step 2: Download the Template
Click Download above, then:
mv ~/Downloads/CLAUDE.md ~/Documents/DataAnalysis/
Step 3: Start Working
cd ~/Documents/DataAnalysis
claude
How to Use
Getting Started
- Share your spreadsheet or data file
- Describe what you want to analyze
- Get insights, formulas, or visualizations
Analysis Types
Exploratory Analysis
Specific Questions
Visualization Requests
Output Format
Data Overview
Best Practices
For Better Analysis
- Clean data first: Handle missing values, duplicates
- Define metrics clearly: What exactly are you measuring?
- Consider context: Industry benchmarks, seasonality
- Validate findings: Cross-check with other data sources
For Better Visualizations
- Keep it simple: One main message per chart
- Label clearly: Title, axes, legend
- Use appropriate scale: Don't truncate misleadingly
- Consider colorblind users: Use patterns or distinct colors
Limitations
- Cannot directly execute code on your data
- Large datasets may need sampling
- Complex statistical models need specialized tools
- Real-time data requires live connections
- Cannot guarantee 100% accuracy on OCR'd data