CSV Data Analyzer

DataBeginner

Load a CSV file to auto-analyze column structure, statistical summaries, missing values, and outliers, then extract key insights. No coding knowledge required for data analysis.

Trigger/csv

Frequency2-3x/week

Non-technical ops team member? Use /csv instead of Excel for fast large-dataset analysis

Data analyst? Automate the initial EDA phase to save analysis time

CSVData AnalysisStatisticsNo-Code

How It Works

Run /csv [file] -> load file

↓

Phase 1: 4 analyses in parallel

schema-detect

Identify column structure

stats-calc

Calculate descriptive stats

missing-scan

Detect missing values

outlier-check

Check for outliers

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Summarize key insights

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✓ Data profile + insights + recommended analyses

Skill Code

# CSV Data Analyzer Skill
## Trigger: /csv [file path]

When the user provides a CSV file:

1. Load and profile the data:
 - Row count, column count
 - Column names and detected types
 - Memory usage estimate

2. Generate statistics per column:
 - Numeric: mean, median, std, min, max, quartiles
 - Categorical: unique count, top values, frequency
 - Date: range, gaps, frequency pattern

3. Quality check:
 - Missing values per column (count + %)
 - Duplicate rows
 - Outliers (IQR method)
 - Data type inconsistencies

4. Output format:
---
## 📊 Data Profile: [filename]

**Shape**: [rows] × [columns]

### Column Summary
| Column | Type | Non-null | Unique | Sample Values |
|--------|------|----------|--------|--------------|

### Key Insights
- [insight about distribution or pattern]
- [insight about correlations]
- [data quality warning]

### Recommended Next Steps
- [ ] [suggested analysis or cleaning action]
---

Copy and paste into your CLAUDE.md to start using immediately.

How CSV Data Analyzer Works

CSV Analyzer reads your CSV file, auto-detects column types and data patterns, generates statistical summaries (mean, median, distribution), identifies anomalies and correlations, and produces visualization recommendations.

When to Use CSV Data Analyzer

Perfect for non-technical stakeholders and data-curious developers who need quick insights from spreadsheet data without writing pandas code — just point it at a CSV file and get actionable analysis in seconds.

Key Strengths

Auto-detects data types and patterns without configuration
Generates statistical summaries with key metrics
Identifies anomalies and outliers automatically
Recommends visualizations based on data characteristics

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