원클릭으로 Manus에서 모든 스킬 실행

programmatic-eda

스타257

포크47

업데이트2026년 4월 25일 15:33

Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.

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

nimrodfisher

nimrodfisher/data-analytics-skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

When to use

You receive a new dataset and need to understand its shape and quality before analysis
An analysis produces surprising numbers and you want to verify the underlying data first
A stakeholder asks "is this data reliable?" or "what's in this table?"
You're about to run a model or statistical test and need data-quality assurance

Process

Load and overview — run scripts/data_overview.py to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).
Null profile — run scripts/null_profiler.py; compare output against thresholds in references/quality_thresholds.md and flag columns above limits.
Outlier detection — run scripts/outlier_detector.py (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?
Distribution summary — run scripts/distribution_summary.py for descriptive stats and univariate histograms on each numeric column.
Correlation exploration — run scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.
EDA checklist sign-off — work through references/eda_checklist.md and confirm each item before declaring the dataset profiled.
Write findings — fill assets/eda_report_template.md with full profiling output; distil top issues into assets/findings_summary.md.

For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see references/pandas_polars_recipes.md.

Inputs the skill needs

Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope
Required: business context — what does one row represent?
Optional: quality threshold overrides (defaults in references/quality_thresholds.md)
Optional: columns to skip (PII, binary blobs, high-cardinality IDs)

Output

assets/eda_report_template.md (filled) — full profiling report with per-column stats
assets/findings_summary.md (filled) — top 3–5 quality issues and recommended next steps
Console output / plots from scripts for interactive inspection

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name	programmatic-eda
description	Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.

When to use

You receive a new dataset and need to understand its shape and quality before analysis
An analysis produces surprising numbers and you want to verify the underlying data first
A stakeholder asks "is this data reliable?" or "what's in this table?"
You're about to run a model or statistical test and need data-quality assurance

Process

Load and overview — run scripts/data_overview.py to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).
Null profile — run scripts/null_profiler.py; compare output against thresholds in references/quality_thresholds.md and flag columns above limits.
Outlier detection — run scripts/outlier_detector.py (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?
Distribution summary — run scripts/distribution_summary.py for descriptive stats and univariate histograms on each numeric column.
Correlation exploration — run scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.
EDA checklist sign-off — work through references/eda_checklist.md and confirm each item before declaring the dataset profiled.
Write findings — fill assets/eda_report_template.md with full profiling output; distil top issues into assets/findings_summary.md.

For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see references/pandas_polars_recipes.md.

Inputs the skill needs

Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope
Required: business context — what does one row represent?
Optional: quality threshold overrides (defaults in references/quality_thresholds.md)
Optional: columns to skip (PII, binary blobs, high-cardinality IDs)

Output

assets/eda_report_template.md (filled) — full profiling report with per-column stats
assets/findings_summary.md (filled) — top 3–5 quality issues and recommended next steps
Console output / plots from scripts for interactive inspection