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Agent skill

Feature Engineering Cookbook

by revfactory in revfactory/harness-100

Feature engineering techniques catalog: numeric/categorical/time-series/text transformations, feature selection, feature store design.

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/en/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
~1.3k tokens
SKILL.md length
183 words
Files
1
Skills in repo
464
Repo updated
First seen
Licence
Apache-2.0

At a glance

Feature engineering techniques catalog: numeric/categorical/time-series/text transformations, feature selection, feature store design.

  • Data preprocessing and feature design involving feature engineering
  • SKILL.md covers Numeric Transformations, Categorical Encoding, Time-Series Features and Feature Selection Methods, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Variable transformation

What it does

Feature Engineering Cookbook is an agent skill from revfactory/harness-100. Feature engineering techniques catalog: numeric/categorical/time-series/text transformations, feature selection, feature store design. Use this skill for data preprocessing and feature design involving 'feature engineering', 'variable transformation', 'encoding', 'scaling', 'feature selection', 'feature store', 'feature importance', etc. Enhances the data-engineer's feature engineering capabilities. Note: model design and training management are outside this skill's scope.

Its SKILL.md is about 1.3k 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

  • Data preprocessing and feature design involving feature engineering
  • Variable transformation
  • Feature selection
  • Feature importance

Example prompts

  • “feature engineering”
  • “variable transformation”
  • “encoding”
  • “/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 1.3k tokens when it runs. Until then it costs about 127 tokens; SKILL.md has 183 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~127
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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). 183 words, ~1,328 tokens.

Download SKILL.mdSave it as .claude/skills/feature-engineering-cookbook/SKILL.md (or your agent's skills folder).
name
feature-engineering-cookbook
description
Feature engineering techniques catalog: numeric/categorical/time-series/text transformations, feature selection, feature store design. Use this skill for data preprocessing and feature design involving 'feature engineering', 'variable transformation', 'encoding', 'scaling', 'feature selection', 'feature store', 'feature importance', etc. Enhances the data-engineer's feature engineering capabilities. Note: model design and training management are outside this skill's scope.

Feature Engineering Cookbook — Feature Engineering Techniques Catalog

Transformation techniques by data type, feature selection methods, and feature store design guide.

Numeric Transformations

Scaling
MethodFormulaSuitableNot Suitable
StandardScaler(x - μ) / σNormal distribution, SVM, logistic regressionSensitive to outliers
MinMaxScaler(x - min) / (max - min)[0,1] required, neural networksSensitive to outliers
RobustScaler(x - Q2) / (Q3 - Q1)When outliers exist—
PowerTransformerBox-Cox / Yeo-JohnsonHighly skewed distributionsNegative values (Box-Cox)
QuantileTransformerQuantile-basedUniform/normal distribution transformationDestroys order relationships
Binning (Discretization)
python
# Equal Width
pd.cut(df['age'], bins=5)

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

# Domain-based
bins = [0, 18, 30, 50, 65, 100]
labels = ['Minor', 'Young Adult', 'Middle-aged', 'Senior', 'Elderly']
pd.cut(df['age'], bins=bins, labels=labels)
Mathematical Transformations
python
# Log transformation (right-skewed distribution)
df['log_income'] = np.log1p(df['income'])

# Square root (count data)
df['sqrt_count'] = np.sqrt(df['count'])

# Reciprocal (inverse relationship)
df['inv_distance'] = 1 / (df['distance'] + 1)

Categorical Encoding

MethodCardinalityOrderTree ModelsLinear Models
Label EncodingAnyYes✅❌
One-Hot EncodingLow (<20)No✅✅
Target EncodingHighN/A✅✅
Frequency EncodingHighN/A✅✅
Binary EncodingMediumN/A✅✅
Ordinal EncodingAnyYes✅✅
Target Encoding (Overfitting Prevention)
python
from sklearn.model_selection import KFold

def target_encode_cv(train, col, target, n_folds=5):
    """K-Fold based target encoding — prevents data leakage"""
    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

Time-Series Features

python
# Date decomposition
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)

# Cyclic encoding (periodic variables like month, hour)
df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)
df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)

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

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

Feature Selection Methods

Filter Methods
MethodNumeric→NumericCategorical→NumericNumeric→Categorical
Pearson Correlation✅——
Mutual Information (MI)✅✅✅
Chi-squared——✅
ANOVA F-test——✅
Variance-based✅ (remove var=0)——
Wrapper/Embedded Methods
python
# Tree-based feature importance
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 (model-agnostic)
from sklearn.inspection import permutation_importance
result = permutation_importance(model, X_test, y_test, n_repeats=10)

# SHAP (interpretable feature importance)
import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)

Missing Value Treatment Decision

Check missing ratio
├── < 5%: Remove or simple imputation (mean/median/mode)
├── 5~30%: Model-based imputation (KNN, MICE, tree-based)
├── 30~50%: Use missingness as a feature + imputation
│           df['col_missing'] = df['col'].isna().astype(int)
└── > 50%: Consider column removal (check business importance)

Data Leakage Prevention Checklist

  • No features derived from the target variable?
  • No features using future information?
  • Encoding/scaling was not done before train/test split?
  • CV was applied to Target Encoding?
  • Time-series data does not reference future data?

© 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 en/31-ml-experiment/.claude/skills/feature-engineering-cookbook of revfactory/harness-100.

Open the folder on GitHubat commit 8e8d35c

Compare with similar skills

Feature Engineering Cookbook next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT
Retention Analysisliangdabiao/claude-data-analysis-ultra-main2901 repos~1.3kAutomated safety check: NotesNone
Geomlitalo-goncalves/geoML108—~4.2kAutomated safety check: PassGPL-3.0

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Categories

Questions about Feature Engineering Cookbook

What does Feature Engineering Cookbook do?

Feature engineering techniques catalog: numeric/categorical/time-series/text transformations, feature selection, feature store design. Feature Engineering Cookbook is an agent skill from revfactory/harness-100. Feature engineering techniques catalog: numeric/categorical/time-series/text transformations, feature selection, feature store design.

When should I use Feature Engineering Cookbook?

Feature Engineering Cookbook fits situations like: data preprocessing and feature design involving feature engineering; variable transformation; feature selection; feature importance.

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 (en/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 (en/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 1.3k tokens (SKILL.md is roughly 5.3k 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, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars) and Retention Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 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 1,290 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.