CSV Data Analysis Claude Skill

A CSV analysis skill gives Claude a consistent playbook for making sense of tabular data. Hand it a file and it profiles each column's type and distribution, flags anomalies like nulls, duplicates, and outliers, suggests the aggregations and pivots worth running, and generates ready-to-run Python (pandas + matplotlib) for the analysis and charts. It's the difference between staring at a spreadsheet and getting a structured first read in seconds. Set your preferred methods and chart types once, and every dataset gets the same rigorous treatment instead of an ad-hoc poke around.

When to use this skill

• Getting a fast first read of an unfamiliar CSV or export.
• Spotting data-quality issues before analysis (nulls, duplicates, outliers).
• Generating pandas code for pivots, aggregations, and charts.
• Standardizing how your team profiles and reports on datasets.

What it does

• Profiles column types, ranges, and distributions.
• Flags anomalies: missing values, duplicates, outliers, inconsistent formats.
• Suggests useful aggregations, pivots, and visualizations.
• Generates ready-to-run pandas/matplotlib code.

The CSV Data Analysis Claude Skill SKILL.md

This is the complete, ready-to-use skill file. Copy it or download the .zip, then generate a customized version for your exact use case.

---
name: csv-analyzer
description: Analyzes CSV files - identifies data types, spots anomalies, suggests aggregations, and generates Python (pandas/matplotlib) for common analysis and charts. Use when the user shares a CSV or tabular data, or asks to analyze, profile, or visualize a dataset.
---

# CSV Analyzer

You help the user understand a dataset quickly and rigorously.

## Step 1 - Profile

For each column, report: inferred type, % missing, number of unique values, and (for numerics) min/max/mean/median. For categoricals, list the top values and their counts.

## Step 2 - Data-quality check

Flag anything that would distort analysis:
- Missing or null-heavy columns
- Duplicate rows
- Outliers (values far outside the typical range)
- Inconsistent formats (mixed date formats, stray units, trailing whitespace)
- Suspicious constants or IDs masquerading as numbers

## Step 3 - Suggest analysis

Propose the 3–5 most useful questions this data can answer, the aggregations or pivots to answer them, and the chart type that best shows each result.

## Step 4 - Generate code

Provide runnable Python using `pandas` (and `matplotlib` for charts). Assume the file loads with `df = pd.read_csv("data.csv")`. Keep each snippet self-contained and commented.

## Rules

- State your assumptions (e.g. which column is the key/date).
- Never fabricate values - if the data doesn't support a claim, say so.
- Prefer clear, standard approaches over clever one-liners.

How to install this Claude Skill

1. 1Download the skill and unzip the file.
2. 2In Claude, go to Settings → Capabilities → Skills.
3. 3Click Add Skill → Upload Skill.
4. 4Drag and drop the .zip file to upload it.
5. 5Once enabled, open the three-dot menu and choose “Try in chat.”

New to skills? Read the full guides on creating a Claude Skill and installing one.

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