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eda

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업데이트2026년 5월 5일 11:46

Use this skill when the user wants exploratory analysis of one tabular dataset (Parquet, CSV, JSON, JSONL) — column distributions, data quality, class balance, missing patterns, high-cardinality detection, or summary statistics. Trigger on prompts like "analyze this column", "check the distribution", "look for missing values", "is this target imbalanced", or "run EDA on this file". If the dataset path or column is missing, ask for it after routing. Do NOT use it just to verify a file is readable, list its schema, or count rows; use `dataset-inspector` first for basic access checks. Do NOT use it for model training, feature engineering, non-tabular data (images, text, audio), or remote warehouse exploration.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

argythana

argythana/python-ml-skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Exploratory Data Analysis

Analyze tabular datasets to understand distributions, data quality, and patterns.

When To Use

Understanding a new dataset before modeling
Checking data quality such as missing values, outliers, and duplicates
Analyzing target variable distribution
Identifying class imbalance
Generating summary statistics

Analysis Process

Connect to data and inspect schema.
Analyze the target variable first.
Check each relevant column for distribution, missingness, and cardinality.
Document findings in a compact report.

Helper

For local Parquet, CSV, JSON, or JSONL files, use:

scripts/eda-column-dist --source data/sample.parquet --column status

The helper script requires duckdb in the active Python environment.

Reference

For detailed analysis methodology and output format, read references/eda-analysis.md.

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name

eda

description

Exploratory Data Analysis

Analyze tabular datasets to understand distributions, data quality, and patterns.

When To Use

Understanding a new dataset before modeling
Checking data quality such as missing values, outliers, and duplicates
Analyzing target variable distribution
Identifying class imbalance
Generating summary statistics

Analysis Process

Connect to data and inspect schema.
Analyze the target variable first.
Check each relevant column for distribution, missingness, and cardinality.
Document findings in a compact report.

Helper

For local Parquet, CSV, JSON, or JSONL files, use:

scripts/eda-column-dist --source data/sample.parquet --column status

The helper script requires duckdb in the active Python environment.

Reference

For detailed analysis methodology and output format, read references/eda-analysis.md.