Analyze spreadsheet data, generate insights, create visualizations, and build reports from Excel/CSV data.
npx skills add https://github.com/claude-office-skills/skills --skill data-analysis
Analyze data in spreadsheets, uncover insights, and create compelling visualizations.
This skill helps you:
Exploratory Analysis
"What patterns do you see in this data?"
"Give me an overview of this dataset"
"What are the key statistics?"
Specific Questions
"What was the total revenue by region?"
"Which products had the highest growth?"
"Is there a correlation between X and Y?"
Visualization Requests
"Create a chart showing sales trends"
"Make a comparison chart of Q1 vs Q2"
"Show the distribution of customer ages"
## Dataset Overview
**Rows**: 1,234
**Columns**: 15
**Date Range**: Jan 2025 - Dec 2025
### Column Summary
| Column | Type | Non-null | Unique | Sample Values |
|--------|------|----------|--------|---------------|
| date | Date | 100% | 365 | 2025-01-01 |
| revenue | Number | 98% | 890 | $1,234.56 |
| region | Text | 100% | 5 | North, South |
### Data Quality Issues
- [X] rows have missing values in [column]
- [Y] potential duplicates detected
## Statistical Summary
### [Metric Name]
- **Mean**: X
- **Median**: Y
- **Std Dev**: Z
- **Min/Max**: A / B
### Key Findings
1. [Finding with statistical support]
2. [Finding with statistical support]
### Recommendations
- [Action based on analysis]
## Analysis Report: [Topic]
### Executive Summary
[2-3 sentence overview of key findings]
### Key Metrics
| Metric | Value | Change |
|--------|-------|--------|
| Total Revenue | $X | +Y% |
| Avg Order Value | $Z | -W% |
### Trends
1. **[Trend 1]**: [Description with data]
2. **[Trend 2]**: [Description with data]
### Recommendations
1. [Actionable recommendation]
2. [Actionable recommendation]
1. "Show total sales by month"
2. "Which products are top performers?"
3. "What's the customer segment breakdown?"
4. "Compare this year vs last year"
5. "Forecast next quarter based on trends"
1. "What's the customer distribution by segment?"
2. "Calculate customer lifetime value"
3. "Which customers are at risk of churning?"
4. "What's the acquisition cost vs LTV ratio?"
1. "Calculate profit margins by product"
2. "What's the expense breakdown?"
3. "Show cash flow trends"
4. "Compare budget vs actual"
"Write a formula to calculate year-over-year growth"
"Create a VLOOKUP to match customer data"
"Make a dynamic sum based on criteria"
## Formula: [Purpose]
### Excel/Google Sheets
=SUMIFS(Sales[Amount], Sales[Region], "North", Sales[Date], ">="&DATE(2025,1,1))
### Explanation
- `SUMIFS`: Sums values meeting multiple criteria
- First argument: Column to sum
- Subsequent pairs: Criteria column + criteria value
### Usage
Place in cell [X] where you want the result.
| Data Type | Best Chart |
|-----------|------------|
| Trends over time | Line chart |
| Part of whole | Pie/Donut chart |
| Comparison | Bar chart |
| Distribution | Histogram |
| Correlation | Scatter plot |
| Geographic | Map chart |
## Recommended Chart: [Type]
**Data Series**:
- X-axis: [Column] (e.g., Date)
- Y-axis: [Column] (e.g., Revenue)
- Series: [Column] (e.g., Region)
**Formatting**:
- Title: "[Descriptive title]"
- Colors: Use consistent color scheme
- Labels: Show values on data points
**Chart Description**:
[What this chart shows and why it's useful]
## Pivot Table: [Purpose]
**Rows**: [Field 1], [Field 2]
**Columns**: [Field 3]
**Values**: SUM of [Field 4], AVG of [Field 5]
**Filters**: [Field 6]
Expected Output:
| Region | Q1 | Q2 | Q3 | Q4 | Total |
|--------|----|----|----|----|-------|
| North | $X | $X | $X | $X | $X |
| South | $X | $X | $X | $X | $X |
## Cohort Analysis
**Cohort Definition**: Customers grouped by [first purchase month]
**Metric**: [Retention rate / Revenue / etc.]
**Time Period**: [12 months]
| Cohort | M0 | M1 | M2 | M3 | ... |
|--------|-----|-----|-----|-----|-----|
| Jan 25 | 100%| 45% | 32% | 28% | ... |
| Feb 25 | 100%| 48% | 35% | 30% | ... |
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take claude-office-skills/data-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.