Csv Analyzer

    2

    Automate data profiling with type detection, statistical analysis, and quality flags saved to a Markdown report.

    $12

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    Csv Analyzer

    Example session with this skill installed

    Run a deep quality audit on sales_data.csv and tell me about any anomalies or correlations you find.

    • Read your context and instructions
    • Compiled the csv analyzer
    • Generated the data export

    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.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    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.

    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.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

      Free skills download straight away. Paid skills unlock right after purchase.

    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

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    Fresh listing

    Recently published to Agensi

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    Trust & safety

    Security scanned

    Verified clean 5 months ago

    • Passed all security checks, Safe to install

    Listed5 months ago

    What's inside

    Frequently Asked Questions