Data Cog Guide

What it does

When your agent uses it

• Tasks that involve CSV and tabular files

Example prompts

• “/data-cog-guide”

Requirements

• Python 3

Workflow steps

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

1. 1Schema overview: Column names, inferred types, semantic roles (ID, feature, target, timestamp).
2. 2Univariate statistics: Mean, median, mode, std, skewness, kurtosis for numeric columns; frequency tables for categoricals.
3. 3Missing data matrix: Heatmap-style report of missingness patterns across all columns.
4. 4Correlation analysis: Pairwise Pearson, Spearman, and Cramér's V correlations.
5. 5Distribution flags: Columns that are heavily skewed, zero-inflated, or constant.
6. 6Duplicate detection: Exact row duplicates and near-duplicate clusters.

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

  Links to these hosts (documentation or services it may open):

  • pandas.pydata.org

  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

Data Cog Guide loads about 1.8k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 456 words of instructions outside code blocks.

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

~21

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

~1.8k

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). 456 words, ~1,782 tokens.

name

data-cog-guide

description

Upload messy CSVs with minimal prompting for deep automated analysis

import pandas as pd
import chardet
import io

def smart_load_csv(filepath: str) -> tuple:
    """
    Intelligently load a CSV file, auto-detecting encoding,
    delimiter, header row, and comment lines.
    """
    # Step 1: Detect encoding
    with open(filepath, 'rb') as f:
        raw = f.read(100000)
    encoding = chardet.detect(raw)['encoding']

    # Step 2: Detect delimiter
    import csv
    with open(filepath, 'r', encoding=encoding, errors='replace') as f:
        sample = f.read(8192)
    sniffer = csv.Sniffer()
    try:
        dialect = sniffer.sniff(sample)
        delimiter = dialect.delimiter
    except csv.Error:
        delimiter = ','

    # Step 3: Detect header row (skip comment lines)
    skip_rows = 0
    with open(filepath, 'r', encoding=encoding, errors='replace') as f:
        for line in f:
            if line.startswith('#') or line.startswith('//') or line.strip() == '':
                skip_rows += 1
            else:
                break

    # Step 4: Load with inferred parameters
    df = pd.read_csv(
        filepath, encoding=encoding, delimiter=delimiter,
        skiprows=skip_rows, low_memory=False
    )

    metadata = {
        'encoding': encoding,
        'delimiter': repr(delimiter),
        'skipped_rows': skip_rows,
        'shape': df.shape
    }
    return df, metadata

def auto_cast_columns(df: pd.DataFrame) -> pd.DataFrame:
    """
    Automatically cast columns to their most appropriate types.
    Handles dates, numerics stored as strings, booleans, and categories.
    """
    for col in df.columns:
        # Try numeric conversion
        numeric = pd.to_numeric(df[col], errors='coerce')
        if numeric.notna().mean() > 0.85:
            df[col] = numeric
            continue

        # Try datetime conversion
        datetime = pd.to_datetime(df[col], errors='coerce', infer_datetime_format=True)
        if datetime.notna().mean() > 0.85:
            df[col] = datetime
            continue

        # Try boolean detection
        unique_lower = df[col].dropna().astype(str).str.lower().unique()
        if set(unique_lower).issubset({'true', 'false', 'yes', 'no', '1', '0', 'y', 'n'}):
            df[col] = df[col].astype(str).str.lower().map(
                {'true': True, 'false': False, 'yes': True, 'no': False,
                 '1': True, '0': False, 'y': True, 'n': False}
            )
            continue

        # Convert low-cardinality strings to category
        if df[col].nunique() / len(df) < 0.05 and df[col].nunique() < 50:
            df[col] = df[col].astype('category')

    return df

User: Analyze /data/survey_results_2025.csv
      Question: What factors predict participant satisfaction?
      Output: full_report

Data Cog will:
  1. Load and profile the dataset (auto-detect everything)
  2. Clean and transform (handle missing data, encode categoricals)
  3. Run correlation analysis focused on satisfaction-related columns
  4. Generate regression models predicting satisfaction
  5. Produce a structured report with findings and visualizations

© 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/data-cog-guide 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/data-cog-guide