profile

name	profile
description	Profile data files — row counts, column types, missing values, duplicates, statistics
argument-hint	[dataset-name]
risk	safe
disable-model-invocation	false
user-invocable	true
allowed-tools	Read, Write, Bash, Glob, Grep
model	claude-haiku-4-5-20251001
context	fork
agent	general-purpose

10x Analyst — Data Profiler

Profile any CSV, Excel, or JSON dataset to understand its structure, quality, and statistics before analysis.

Overview

Quickly assess data files without running the full pipeline. Reads from input/<dataset>/, produces a data-profile.md in output/<dataset>/ with row/column counts, data types, missing values, duplicates, basic statistics, and a quality score.

When to Use

• User wants to understand a dataset before committing to full analysis
• User asks "what's in this file", "how clean is this data", "describe this dataset"
• First step before running :analyze or :clean

Path Resolution

Parse $ARGUMENTS to get the dataset name (e.g., shopify-data).

• Input: input/<dataset-name>/
• Output: output/<dataset-name>/

Instructions

1. Resolve the dataset name from $ARGUMENTS and set paths:
   • Input: input/<dataset>/
   • Output: output/<dataset>/
2. Find all data files at input/<dataset>/ using Glob patterns: input/<dataset>/**/*.csv, input/<dataset>/**/*.xlsx, input/<dataset>/**/*.xls, input/<dataset>/**/*.json
3. Create output directory: mkdir -p output/<dataset>
4. For each file, run this Python script:

import pandas as pd
import os

def profile(filepath):
    ext = os.path.splitext(filepath)[1].lower()
    if ext == '.csv':
        df = pd.read_csv(filepath)
    elif ext in ['.xlsx', '.xls']:
        df = pd.read_excel(filepath)
    elif ext == '.json':
        df = pd.read_json(filepath)
    else:
        return None

    total_cells = df.shape[0] * df.shape[1]
    missing_cells = df.isna().sum().sum()
    quality_score = round((1 - missing_cells / total_cells) * 100, 1) if total_cells > 0 else 0

    print(f"## {os.path.basename(filepath)}")
    print(f"- **Rows:** {len(df):,}")
    print(f"- **Columns:** {len(df.columns)}")
    print(f"- **Duplicates:** {df.duplicated().sum():,}")
    print(f"- **Quality Score:** {quality_score}%")
    print()
    print("| Column | Type | Missing | Missing % | Unique |")
    print("|--------|------|---------|-----------|--------|")
    for col in df.columns:
        missing = df[col].isna().sum()
        missing_pct = round(df[col].isna().mean() * 100, 1)
        unique = df[col].nunique()
        print(f"| {col} | {df[col].dtype} | {missing:,} | {missing_pct}% | {unique:,} |")
    print()

    num_cols = df.select_dtypes(include='number').columns
    if len(num_cols) > 0:
        print("| Column | Min | Max | Mean | Median | Std |")
        print("|--------|-----|-----|------|--------|-----|")
        for col in num_cols:
            if not df[col].isna().all():
                print(f"| {col} | {df[col].min():.2f} | {df[col].max():.2f} | {df[col].mean():.2f} | {df[col].median():.2f} | {df[col].std():.2f} |")
        print()

5. Write all output to output/<dataset>/data-profile.md
6. Present a summary table to the user:

| File | Rows | Columns | Quality | Issues |
|------|------|---------|---------|--------|

Examples

/10x-analyst:profile shopify-data

Limitations

• Does not clean or transform data — use :clean for that
• Large files (>500MB) may be slow to profile
• Excel files need openpyxl installed

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Developed by 10x.in | 10x-Analyst v1.0.0