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Agent skill

Feature Engineering Cookbook

by revfactory in revfactory/harness-100

피처 엔지니어링 기법 카탈로그: 수치형/범주형/시계열/텍스트 변환, 피처 선택, 피처 스토어 설계. An agent skill from revfactory/harness-100.

Apache-2.0Auto-check passedData & Analytics

Install Feature Engineering Cookbook

skills CLI
$ npx skills add revfactory/harness-100 --skill feature-engineering-cookbook -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install revfactory/harness-100 feature-engineering-cookbook --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ko/31-ml-experiment/.claude/skills/feature-engineering-cookbook .claude/skills/feature-engineering-cookbook && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
feature-engineering-cookbook
GitHub stars
1.3k
Token cost
~1k tokens
SKILL.md length
166 words
Files
1
Skills in repo
464
Repo updated
First seen
Licence
Apache-2.0

At a glance

피처 엔지니어링 기법 카탈로그: 수치형/범주형/시계열/텍스트 변환, 피처 선택, 피처 스토어 설계. An agent skill from revfactory/harness-100.

  • Tasks that involve Machine learning
  • SKILL.md covers 수치형 변환, 범주형 인코딩, 시계열 피처 and 피처 선택 방법, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Feature Engineering Cookbook is an agent skill from revfactory/harness-100. 피처 엔지니어링 기법 카탈로그: 수치형/범주형/시계열/텍스트 변환, 피처 선택, 피처 스토어 설계. '피처 엔지니어링', '특성 공학', '변수 변환', '인코딩', '스케일링', '피처 선택', '피처 스토어', '피처 중요도' 등 데이터 전처리 및 피처 설계 시 이 스킬을 사용한다. data-engineer의 피처 엔지니어링 역량을 강화한다. 단, 모델 설계나 학습 관리는 이 스킬의 범위가 아니다.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Machine learning. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Machine learning

Example prompts

  • “/feature-engineering-cookbook”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 8e8d35c. 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

Feature Engineering Cookbook loads about 1k tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 166 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~1k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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 revfactory/harness-100 at commit 8e8d35c, republished under its Apache-2.0 licence (© revfactory). 166 words, ~1,005 tokens.

Download SKILL.mdSave it as .claude/skills/feature-engineering-cookbook/SKILL.md (or your agent's skills folder).
name
feature-engineering-cookbook
description
피처 엔지니어링 기법 카탈로그: 수치형/범주형/시계열/텍스트 변환, 피처 선택, 피처 스토어 설계. '피처 엔지니어링', '특성 공학', '변수 변환', '인코딩', '스케일링', '피처 선택', '피처 스토어', '피처 중요도' 등 데이터 전처리 및 피처 설계 시 이 스킬을 사용한다. data-engineer의 피처 엔지니어링 역량을 강화한다. 단, 모델 설계나 학습 관리는 이 스킬의 범위가 아니다.

Feature Engineering Cookbook — 피처 엔지니어링 기법 카탈로그

데이터 타입별 변환 기법, 피처 선택 방법, 피처 스토어 설계 가이드.

수치형 변환

스케일링
방법공식적합부적합
StandardScaler(x - μ) / σ정규분포, SVM, 로지스틱 회귀이상치 민감
MinMaxScaler(x - min) / (max - min)[0,1] 필요, 신경망이상치 민감
RobustScaler(x - Q2) / (Q3 - Q1)이상치 존재—
PowerTransformerBox-Cox / Yeo-Johnson왜도 큰 분포음수값(Box-Cox)
QuantileTransformer분위수 기반균일/정규 분포 변환순서 관계 파괴
이산화 (Binning)
python
# 등간격 (Equal Width)
pd.cut(df['age'], bins=5)

# 등빈도 (Equal Frequency)
pd.qcut(df['income'], q=5)

# 도메인 기반
bins = [0, 18, 30, 50, 65, 100]
labels = ['미성년', '청년', '중년', '장년', '노년']
pd.cut(df['age'], bins=bins, labels=labels)
수학적 변환
python
# 로그 변환 (오른쪽 꼬리 분포)
df['log_income'] = np.log1p(df['income'])

# 제곱근 (카운트 데이터)
df['sqrt_count'] = np.sqrt(df['count'])

# 역수 (반비례 관계)
df['inv_distance'] = 1 / (df['distance'] + 1)

범주형 인코딩

방법카디널리티순서트리 모델선형 모델
Label Encoding무관있음✅❌
One-Hot Encoding낮음(<20)없음✅✅
Target Encoding높음N/A✅✅
Frequency Encoding높음N/A✅✅
Binary Encoding중간N/A✅✅
Ordinal Encoding무관있음✅✅
Target Encoding (과적합 방지)
python
from sklearn.model_selection import KFold

def target_encode_cv(train, col, target, n_folds=5):
    """K-Fold 기반 타깃 인코딩 — 데이터 누수 방지"""
    global_mean = train[target].mean()
    encoded = pd.Series(index=train.index, dtype=float)

    kf = KFold(n_splits=n_folds, shuffle=True, random_state=42)
    for train_idx, val_idx in kf.split(train):
        means = train.iloc[train_idx].groupby(col)[target].mean()
        encoded.iloc[val_idx] = train.iloc[val_idx][col].map(means)

    encoded.fillna(global_mean, inplace=True)
    return encoded

시계열 피처

python
# 날짜 분해
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['dayofweek'] = df['date'].dt.dayofweek
df['is_weekend'] = df['dayofweek'].isin([5, 6]).astype(int)
df['hour'] = df['date'].dt.hour
df['is_business_hour'] = df['hour'].between(9, 18).astype(int)

# 순환 인코딩 (월, 시간 등 주기적 변수)
df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)

# Lag 피처
df['sales_lag_1'] = df['sales'].shift(1)
df['sales_lag_7'] = df['sales'].shift(7)

# Rolling 통계
df['sales_ma_7'] = df['sales'].rolling(7).mean()
df['sales_std_7'] = df['sales'].rolling(7).std()

피처 선택 방법

필터 방법
방법수치→수치범주→수치수치→범주
상관계수 (Pearson)✅——
상호정보량 (MI)✅✅✅
카이제곱——✅
ANOVA F-test——✅
분산 기반✅ (분산=0 제거)——
래퍼/임베디드 방법
python
# 트리 기반 피처 중요도
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier().fit(X, y)
importances = pd.Series(model.feature_importances_, index=X.columns)
top_features = importances.nlargest(20).index

# Permutation Importance (모델 무관)
from sklearn.inspection import permutation_importance
result = permutation_importance(model, X_test, y_test, n_repeats=10)

# SHAP (해석 가능한 피처 중요도)
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)

결측치 처리 의사결정

결측 비율 확인
├── < 5%: 제거 또는 단순 대체(평균/중앙값/최빈값)
├── 5~30%: 모델 기반 대체 (KNN, MICE, 트리 기반)
├── 30~50%: 결측 자체를 피처로 + 대체
│           df['col_missing'] = df['col'].isna().astype(int)
└── > 50%: 컬럼 제거 고려 (비즈니스 중요도 확인)

데이터 누수 방지 체크리스트

  • 타깃 변수에서 파생된 피처가 없는가?
  • 미래 정보를 사용하는 피처가 없는가?
  • train/test 분할 전에 인코딩/스케일링을 하지 않았는가?
  • Target Encoding에 CV를 적용했는가?
  • 시계열 데이터에서 미래 데이터를 참조하지 않는가?

© revfactory, Apache-2.0. 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 ko/31-ml-experiment/.claude/skills/feature-engineering-cookbook of revfactory/harness-100.

Open the folder on GitHubat commit 8e8d35c

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Categories

Questions about Feature Engineering Cookbook

What does Feature Engineering Cookbook do?

피처 엔지니어링 기법 카탈로그: 수치형/범주형/시계열/텍스트 변환, 피처 선택, 피처 스토어 설계. An agent skill from revfactory/harness-100. Feature Engineering Cookbook is an agent skill from revfactory/harness-100. 피처 엔지니어링 기법 카탈로그: 수치형/범주형/시계열/텍스트 변환, 피처 선택, 피처 스토어 설계.

When should I use Feature Engineering Cookbook?

Feature Engineering Cookbook fits situations like: tasks that involve Machine learning.

How do I install Feature Engineering Cookbook in Claude Code?

Run `npx skills add revfactory/harness-100 --skill feature-engineering-cookbook -a claude-code`. Or copy the skill folder (ko/31-ml-experiment/.claude/skills/feature-engineering-cookbook in revfactory/harness-100) into .claude/skills/feature-engineering-cookbook in your project. Claude Code loads it when a task matches its description.

How do I install Feature Engineering Cookbook in Codex?

Run `npx skills add revfactory/harness-100 --skill feature-engineering-cookbook -a codex`. Or copy the skill folder (ko/31-ml-experiment/.claude/skills/feature-engineering-cookbook in revfactory/harness-100) into .agents/skills/feature-engineering-cookbook in your project. Codex loads it when a task matches its description.

Can I use Feature Engineering Cookbook in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add revfactory/harness-100 --skill feature-engineering-cookbook -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/feature-engineering-cookbook, .gemini/skills/feature-engineering-cookbook, .github/skills/feature-engineering-cookbook and .opencode/skills/feature-engineering-cookbook in your project.

What does Feature Engineering Cookbook need to run?

SKILL.md names no scripts, command-line tools or credentials: Feature Engineering Cookbook is instructions for the agent only. Our summary lists: Python 3.

Does Feature Engineering Cookbook access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Feature Engineering Cookbook safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Feature Engineering Cookbook use?

Feature Engineering Cookbook is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Feature Engineering Cookbook use?

About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Feature Engineering Cookbook?

Skills that share tags, products or a category with Feature Engineering Cookbook: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars) and Geoml (italo-goncalves/geoML, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Feature Engineering Cookbook?

revfactory (a GitHub user) maintains it in revfactory/harness-100, which has 1,295 GitHub stars. The repository holds 464 skills in this directory. The repository was last updated on March 22, 2026.

Source: revfactory/harness-100 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.