csv-analyzer
by Kevin Cline
Automate data profiling with type detection, statistical analysis, and quality flags saved to a Markdown report.
- Generate comprehensive data quality reports for new datasets automatically.
- Detect outliers and missing value clusters before training ML models.
- Identify redundant columns using Pearson correlation analysis.
$12
One-time purchase · Own forever
Included in download
- Generate comprehensive data quality reports for new datasets automatically.
- Detect outliers and missing value clusters before training ML models.
- terminal automation included
- Includes example output and usage patterns
See it in action
CSV Analysis: sales_data.csv (12,500 rows) Top findings: - 3 columns with HIGH_MISSING data (>25% nulls) - 124 outliers detected in 'unit_price' - Strong correlation (r=0.92) between 'subtotal' and 'tax' Full report saved to ./CSV_REPORT.md with 5 cleanup recommendations.
csv-analyzer
by Kevin Cline
Automate data profiling with type detection, statistical analysis, and quality flags saved to a Markdown report.
$12
One-time purchase · Own forever
⚡ Also available via Agensi Pro — your AI agent can load this skill on demand via MCP. Learn more →
Included in download
- Generate comprehensive data quality reports for new datasets automatically.
- Detect outliers and missing value clusters before training ML models.
- terminal automation included
- Includes example output and usage patterns
- Instant install
See it in action
CSV Analysis: sales_data.csv (12,500 rows) Top findings: - 3 columns with HIGH_MISSING data (>25% nulls) - 124 outliers detected in 'unit_price' - Strong correlation (r=0.92) between 'subtotal' and 'tax' Full report saved to ./CSV_REPORT.md with 5 cleanup recommendations.
About This Skill
Deep Data Quality Profiling for CSVs
The CSV Analyzer is a specialized skill designed to automate the initial 80% of data science and engineering workflows: understanding the shape, quality, and hidden patterns of a dataset. Instead of manually writing repetitive pandas or SQL scripts to check for nulls and outliers, this skill performs an exhaustive audit of any CSV file in seconds.
What it does
- Smart Type Detection: Goes beyond basic strings/ints to identify emails, URLs, UUIDs, dates, and categorical data.
- Statistical Deep-Dive: Calculates distributions, IQR-based outliers, and skewness for numeric data, alongside high-cardinality analysis for text.
- Data Quality Auditing: Flags mixed types, constant columns, leading/trailing whitespace, and near-duplicate columns.
- Relationship Mapping: Identifies strong Pearson correlations between numeric features to surface potential redundancies.
Why use this skill?
While a standard LLM can look at a few rows of data, it cannot accurately calculate statistics or scan 10,000+ rows for anomalies without help. This skill leverages Bash and file-system tools to process large datasets reliably, generating a structured CSV_REPORT.md that serves as a permanent documentation artifact for your project. The output provides actionable recommendations for data cleaning (imputation, deduplication, etc.) that you can hand off to your agent or a data team.
📖 Learn more: Best Testing & QA Skills for Claude Code →
Use Cases
- Generate comprehensive data quality reports for new datasets automatically.
- Detect outliers and missing value clusters before training ML models.
- Identify redundant columns using Pearson correlation analysis.
- Surface data integrity issues like mixed types or hidden whitespace.
How to Install
unzip csv-analyzer.zip -d ~/.claude/skills/Reviews
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Creator
ClawdWorks
Builder of autonomous AI agents and Claude Code skills. ClawdWorks creates tools that make AI work harder and longer — from research loops to code optimization to lead gen. Powered by Claude Opus 4.6 + Codex 5.4.
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