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First Look

Know what you're dealing with before writing a single query

Drop any dataset — CSV, Excel, Parquet, database table — and get a complete profile in seconds: types, nulls, distributions, outliers, duplicates, correlations, and quality issues. The EDA you always do manually, automated.

CommunitySubmitted by CommunityWork1 min

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PROMPT

Create a skill called "First Look". When I give you a dataset (CSV, Excel, Parquet, or a database table name), produce a comprehensive data profile: (1) Overview: row count, column count, memory size, duplicate row count. (2) Per-column analysis: inferred type, null count and percentage, unique values count, min/max/mean/median/std for numeric, top 10 values for categorical, example values. (3) Distribution analysis: identify skewed distributions, uniform distributions, and constant columns. (4) Outlier detection: flag values beyond 3 standard deviations or 1.5*IQR. (5) Cross-column correlations for numeric pairs above 0.7. (6) Data quality summary: an overall quality score and per-column scores based on completeness, uniqueness, and consistency. (7) Recommended next steps: specific cleaning actions for each issue found. For large datasets (>1M rows), profile a stratified sample and note the sampling approach.

How It Works

Every new dataset starts the same way: df.head(), df.describe(), df.info(),

check for nulls, check for duplicates, look at value distributions...

This skill does all of it in one pass and gives you a structured report.

What You Get

Column-level profiling: type, null rate, unique count, min/max/mean/median
Distribution summaries and histogram sketches for numeric columns
Top values and frequency counts for categorical columns
Duplicate row detection (exact and fuzzy)
Outlier flagging (values beyond 3 standard deviations or IQR-based)
Cross-column correlation matrix for numeric columns
Data quality score per column and overall
Suggested cleaning steps based on issues found

Setup Steps

Ask your Claw to create a "First Look" skill with the prompt below
Drop any data file or point it at a database table
Get back a comprehensive profile report in under a minute

Tips

Run this on every new data source before doing any analysis
The quality score helps prioritize which columns need cleaning attention
The suggested cleaning steps feed directly into the CSV Doctor or Date Whisperer skills
For large datasets, it profiles a sample and notes where sampling might miss issues

Tags:#data-profiling#eda#data-quality#exploration

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