CSV Data Analyzer

What it does

When your agent uses it

• Tasks that involve CSV and tabular files

Example prompts

• “/csv-data-analyzer”

Requirements

• Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Remove fully empty rows and columns: Drop rows/columns where all values are NaN.
2. 2Standardize column names: Convert to snake_case, remove special characters.
3. 3Handle missing data: Assess missingness patterns (MCAR/MAR/MNAR) before choosing imputation strategy.
4. 4Detect and handle duplicates: Identify exact and near-duplicates using fuzzy matching.
5. 5Validate value ranges: Flag values outside expected domain ranges.
6. 6Standardize categorical labels: Merge inconsistent spellings (e.g., "Male", "male", "M").

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

CSV Data Analyzer loads about 1.7k tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 362 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~22

When it runs · the whole SKILL.md, loaded when a task matches

~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 362 words, ~1,731 tokens.

name

csv-data-analyzer

description

Load, explore, clean, and analyze CSV data with statistical summaries

import pandas as pd
import numpy as np

def load_and_profile_csv(filepath: str, encoding: str = 'utf-8') -> dict:
    """
    Load a CSV file and generate an initial data profile.
    Handles common encoding issues and delimiter detection.
    """
    # Try multiple encodings if default fails
    encodings = [encoding, 'latin-1', 'utf-8-sig', 'cp1252']
    df = None
    for enc in encodings:
        try:
            df = pd.read_csv(filepath, encoding=enc, low_memory=False)
            break
        except (UnicodeDecodeError, pd.errors.ParserError):
            continue

    if df is None:
        raise ValueError(f"Could not parse {filepath} with any supported encoding")

    profile = {
        'rows': len(df),
        'columns': len(df.columns),
        'memory_mb': df.memory_usage(deep=True).sum() / 1e6,
        'dtypes': df.dtypes.value_counts().to_dict(),
        'missing_pct': (df.isnull().sum() / len(df) * 100).to_dict(),
        'duplicates': df.duplicated().sum(),
        'column_names': df.columns.tolist()
    }
    return df, profile

def infer_semantic_types(df: pd.DataFrame) -> dict:
    """
    Infer semantic column types beyond pandas dtypes.
    Detects dates, identifiers, categorical, continuous, and text columns.
    """
    semantic_types = {}
    for col in df.columns:
        nunique = df[col].nunique()
        ratio = nunique / len(df) if len(df) > 0 else 0

        if ratio > 0.95 and df[col].dtype == 'object':
            semantic_types[col] = 'identifier'
        elif nunique <= 20 and df[col].dtype in ['object', 'int64']:
            semantic_types[col] = 'categorical'
        elif df[col].dtype in ['float64', 'int64']:
            semantic_types[col] = 'continuous'
        elif pd.to_datetime(df[col], errors='coerce').notna().mean() > 0.8:
            semantic_types[col] = 'datetime'
        else:
            semantic_types[col] = 'text'
    return semantic_types

def clean_column_names(df: pd.DataFrame) -> pd.DataFrame:
    """Standardize column names to snake_case."""
    import re
    df.columns = [
        re.sub(r'[^a-z0-9]+', '_', col.lower().strip()).strip('_')
        for col in df.columns
    ]
    return df

def assess_missingness(df: pd.DataFrame) -> pd.DataFrame:
    """Generate a missingness report for each column."""
    report = pd.DataFrame({
        'missing_count': df.isnull().sum(),
        'missing_pct': (df.isnull().sum() / len(df) * 100).round(2),
        'dtype': df.dtypes
    })
    report['action'] = report['missing_pct'].apply(
        lambda x: 'drop' if x > 60 else ('impute' if x > 0 else 'ok')
    )
    return report.sort_values('missing_pct', ascending=False)

def generate_statistical_summary(df: pd.DataFrame) -> dict:
    """
    Generate comprehensive descriptive statistics for all columns.
    Includes measures of central tendency, dispersion, and distribution shape.
    """
    numeric_cols = df.select_dtypes(include=[np.number])
    summary = {
        'numeric': numeric_cols.describe().T.assign(
            skewness=numeric_cols.skew(),
            kurtosis=numeric_cols.kurtosis(),
            iqr=numeric_cols.quantile(0.75) - numeric_cols.quantile(0.25),
            cv=numeric_cols.std() / numeric_cols.mean()  # coefficient of variation
        ),
        'categorical': {
            col: df[col].value_counts().head(10).to_dict()
            for col in df.select_dtypes(include=['object']).columns
        },
        'correlations': numeric_cols.corr().round(3)
    }
    return summary

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/analysis/wrangling/csv-data-analyzer of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

• brycewang-stanford/Auto-Empirical-Research-Skillsskills/43-wentorai-research-plugins/skills/analysis/wrangling/csv-data-analyzer