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Agent skill

Engineering Features For Machine Learning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Execute create, select, and transform features to improve machine learning model performance.

MITAuto-check passedData & Analytics

Install Engineering Features For Machine Learning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill engineering-features-for-machine-learning -a claude-code

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace engineering-features-for-machine-learning --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/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/engineering-features-for-machine-learning .claude/skills/engineering-features-for-machine-learning && 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
engineering-features-for-machine-learning
GitHub stars
2.8k
Token cost
~1.1k tokens
SKILL.md length
493 words
Files
8 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Execute create, select, and transform features to improve machine learning model performance.

  • Works in 4 steps: Analyzing Requirements: Claude analyzes… → Generating Code: Claude generates Python… → Executing Task: The generated code is… → …
  • Asked to engineer features
  • SKILL.md covers Overview, How It Works, When to Use This Skill and Examples, plus 7 more sections
  • Runs Python scripts from its folder

What it does

Engineering Features For Machine Learning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Execute create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features". Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/configuration_template.yaml` and `assets/feature_engineering_template.py`). Compatibility notes: Designed for Claude Code

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Asked to engineer features
  • Select features
  • With relevant phrases based on skill purpose

Example prompts

  • “engineer features”
  • “select features”
  • “/engineering-features-for-machine-learning”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

Workflow steps

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

  1. Analyzing Requirements: Claude analyzes the user's request and identifies the specific feature engineering task required.
  2. Generating Code: Claude generates Python code using the feature-engineering-toolkit plugin to perform the requested task. This includes…
  3. Executing Task: The generated code is executed, creating, selecting, or transforming features as requested.
  4. Providing Insights: Claude provides performance metrics and insights related to the feature engineering process, such as the importance of…

What it can do on your machine

Read from SKILL.md and the folder at commit 80f86df. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    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.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Engineering Features For Machine Learning loads about 1.1k tokens when it runs, and up to ~1.1k if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 493 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.1k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit 80f86df, republished under its MIT licence (© jeremylongshore). 493 words, ~1,052 tokens.

Download SKILL.mdSave it as .claude/skills/engineering-features-for-machine-learning/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
engineering-features-for-machine-learning
description
Execute create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features". Trigger with relevant phrases based on skill purpose.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
1.23.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, ml, performance, scaling

Feature Engineering Toolkit

Create, select, and transform features to improve ML model performance, handling scaling, encoding, interaction terms, and importance analysis.

Overview

leverage the feature-engineering-toolkit plugin to enhance machine learning models. It automates the process of creating new features, selecting the most relevant ones, and transforming existing features to better suit the model's needs. Use this skill to improve the accuracy, efficiency, and interpretability of machine learning models.

How It Works

  1. Analyzing Requirements: Claude analyzes the user's request and identifies the specific feature engineering task required.
  2. Generating Code: Claude generates Python code using the feature-engineering-toolkit plugin to perform the requested task. This includes data validation and error handling.
  3. Executing Task: The generated code is executed, creating, selecting, or transforming features as requested.
  4. Providing Insights: Claude provides performance metrics and insights related to the feature engineering process, such as the importance of newly created features or the impact of transformations on model performance.

When to Use This Skill

This skill activates when you need to:

  • Create new features from existing data to improve model accuracy.
  • Select the most relevant features from a dataset to reduce model complexity and improve efficiency.
  • Transform features to better suit the assumptions of a machine learning model (e.g., scaling, normalization, encoding).

Examples

Example 1: Improving Model Accuracy

User request: "Create new features from the existing 'age' and 'income' columns to improve the accuracy of a customer churn prediction model."

The skill will:

  1. Generate code to create interaction terms between 'age' and 'income' (e.g., age * income, age / income).
  2. Execute the code and evaluate the impact of the new features on model performance.
Show full SKILL.md (222 more words)Show less
Example 2: Reducing Model Complexity

User request: "Select the top 10 most important features from the dataset to reduce the complexity of a fraud detection model."

The skill will:

  1. Generate code to calculate feature importance using a suitable method (e.g., Random Forest, SelectKBest).
  2. Execute the code and select the top 10 features based on their importance scores.

Best Practices

  • Data Validation: Always validate the input data to ensure it is clean and consistent before performing feature engineering.
  • Feature Scaling: Scale numerical features to prevent features with larger ranges from dominating the model.
  • Encoding Categorical Features: Encode categorical features appropriately (e.g., one-hot encoding, label encoding) to make them suitable for machine learning models.

Integration

This skill integrates with the feature-engineering-toolkit plugin, providing a seamless way to create, select, and transform features for machine learning models. It can be used in conjunction with other Claude Code skills to build complete machine learning pipelines.

Prerequisites

  • Appropriate file access permissions
  • Required dependencies installed

Instructions

  1. Invoke this skill when the trigger conditions are met
  2. Provide necessary context and parameters
  3. Review the generated output
  4. Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

  • Invalid input: Prompts for correction
  • Missing dependencies: Lists required components
  • Permission errors: Suggests remediation steps

Resources

  • Project documentation
  • Related skills and commands

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/.curated/engineering-features-for-machine-learning of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/configuration_template.yaml
  • assets/example_dataset.csv
  • assets/feature_engineering_template.py
  • references/README.md
  • scripts/README.md
  • scripts/feature_importance_analyzer.py

Open the folder on GitHubat commit 80f86df

Compare with similar skills

Engineering Features For Machine Learning 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.

Engineering Features For Machine Learning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Engineering Features For Machine Learning this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.1kAutomated safety check: PassMIT
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Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT
Senior Data ScientistRaidriar7170/hermes-skilleval1255 repos~1.4kAutomated safety check: PassMIT
Geomlitalo-goncalves/geoML109—~4.6kAutomated safety check: PassGPL-3.0
QuantMind Training Config Generatorqusong0627/QuantMind1.7k—~1.5kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Engineering Features For Machine Learning

What does Engineering Features For Machine Learning do?

Execute create, select, and transform features to improve machine learning model performance. Engineering Features For Machine Learning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Execute create, select, and transform features to improve machine learning model performance.

When should I use Engineering Features For Machine Learning?

Engineering Features For Machine Learning fits situations like: asked to engineer features; select features; with relevant phrases based on skill purpose.

How do I install Engineering Features For Machine Learning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill engineering-features-for-machine-learning -a claude-code`. Or copy the skill folder (skills/.curated/engineering-features-for-machine-learning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/engineering-features-for-machine-learning in your project. Claude Code loads it when a task matches its description.

How do I install Engineering Features For Machine Learning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill engineering-features-for-machine-learning -a codex`. Or copy the skill folder (skills/.curated/engineering-features-for-machine-learning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/engineering-features-for-machine-learning in your project. Codex loads it when a task matches its description.

Can I use Engineering Features For Machine Learning 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 jeremylongshore/tons-of-skills-marketplace --skill engineering-features-for-machine-learning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/engineering-features-for-machine-learning, .gemini/skills/engineering-features-for-machine-learning, .github/skills/engineering-features-for-machine-learning and .opencode/skills/engineering-features-for-machine-learning in your project.

What does Engineering Features For Machine Learning need to run?

Going by SKILL.md and its folder, Engineering Features For Machine Learning needs Python for the scripts in its folder. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Engineering Features For Machine Learning 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 Engineering Features For Machine Learning 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Engineering Features For Machine Learning use?

Engineering Features For Machine Learning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Engineering Features For Machine Learning use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 18 tokens, read only when the agent opens those files.

What are the alternatives to Engineering Features For Machine Learning?

Skills that share tags, products or a category with Engineering Features For Machine Learning: 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 Engineering Features For Machine Learning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,825 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 9, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.