engineering-features-for-machine-learning
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: engineering-features-for-machine-learning
description: | Use when this capability is needed. This skill enables Claude to leverage the feature-engineer…
category: other
runtime: Python
---
# engineering-features-for-machine-learning output preview
## PART A: Task fit
- Use case: | Use when this capability is needed. This skill enables Claude to 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. By using this skill, you can improve the accuracy, e….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / How It Works / When to Use This Skill” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “| Use when this capability is needed. This skill enables Claude to 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. By using this skill, you can improve the accuracy, e…”.
- **02** When the source has headings, the agent prioritizes “Overview / How It Works / When to Use This Skill” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Overview / How It Works / When to Use This Skill”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: engineering-features-for-machine-learning
description: | Use when this capability is needed. This skill enables Claude to leverage the feature-engineer…
category: other
source: tomevault-io/skills-registry
---
# engineering-features-for-machine-learning
## When to use
- | Use when this capability is needed. This skill enables Claude to leverage the feature-engineering-toolkit plugin to…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / How It Works / When to Use This Skill” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "engineering-features-for-machine-learning" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / How It Works / When to Use This Skill
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Overview
This skill enables Claude to 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. By using this skill, you can improve the accuracy, efficiency, and interpretability of your machine learning models.
How It Works
- Analyzing Requirements: Claude analyzes the user's request and identifies the specific feature engineering task required.
- Generating Code: Claude generates Python code using the feature-engineering-toolkit plugin to perform the requested task. This includes data validation and error handling.
- Executing Task: The generated code is executed, creating, selecting, or transforming features as requested.
- 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:
- Generate code to create interaction terms between 'age' and 'income' (e.g., age * income, age / income).
- Execute the code and evaluate the impact of the new features on model performance.
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:
- Generate code to calculate feature importance using a suitable method (e.g., Random Forest, SelectKBest).
- 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.
Source: ComeOnOliver/skillshub — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review