Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities.
Install Datarobot Feature Engineering
$ npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-feature-engineering --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/datarobot-feature-engineering .claude/skills/datarobot-feature-engineering && rm -rf skills-srcUse ~/.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/
Install the "datarobot-feature-engineering" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-feature-engineering into .claude/skills/datarobot-feature-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-feature-engineering", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-feature-engineeringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-feature-engineering --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/datarobot-feature-engineering .agents/skills/datarobot-feature-engineering && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "datarobot-feature-engineering" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-feature-engineering into .agents/skills/datarobot-feature-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-feature-engineering", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-feature-engineering --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/datarobot-feature-engineering .cursor/skills/datarobot-feature-engineering && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "datarobot-feature-engineering" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-feature-engineering into .cursor/skills/datarobot-feature-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-feature-engineering", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Kilo-Org/kilo-marketplace.git --path skills/datarobot-feature-engineering--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-feature-engineering --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/datarobot-feature-engineering .gemini/skills/datarobot-feature-engineering && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "datarobot-feature-engineering" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-feature-engineering into .gemini/skills/datarobot-feature-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-feature-engineering", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-feature-engineeringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/datarobot-feature-engineering .github/skills/datarobot-feature-engineering && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "datarobot-feature-engineering" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-feature-engineering into .github/skills/datarobot-feature-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-feature-engineering", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-feature-engineering --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/datarobot-feature-engineering .opencode/skills/datarobot-feature-engineering && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "datarobot-feature-engineering" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-feature-engineering into .opencode/skills/datarobot-feature-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-feature-engineering", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
datarobot-feature-engineering- GitHub stars
- 190
- Token cost
- ~1.9k tokens
- SKILL.md length
- 656 words
- Files
- 2
- Skills in repo
- 86
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities.
- Works in 4 steps: Feature Discovery → Feature Importance Analysis → Feature Optimization → …
- Working with feature engineering
- SKILL.md covers Quick Start, When to use this skill, Key capabilities and Workflow examples, plus 8 more sections
- Calls pip; needs DATAROBOT_API_TOKEN
What it does
Datarobot Feature Engineering is an agent skill from Kilo-Org/kilo-marketplace. Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities. Use when working with feature engineering, feature discovery, or analyzing feature importance in DataRobot.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.
It sits in Data & Analytics, covering Machine learning. The repository describes itself as: Kilo Marketplace - A curated collection of Skills, MCP Servers, and Modes for enhancing AI agent capabilities across the Kilo ecosystem—including Kilo Code (VS Code extension)… The licence is Apache-2.0.
When your agent uses it
- Working with feature engineering
- Feature discovery
- Analyzing feature importance in DataRobot
Example prompts
- “/datarobot-feature-engineering”
Requirements
- Python 3
- A credential in DATAROBOT_API_TOKEN
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit ff51758. 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
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
docs.datarobot.comdatarobot-public-api-client.readthedocs-hosted.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
DATAROBOT_API_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Datarobot Feature Engineering loads about 1.9k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 656 words of instructions outside code blocks.
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 Kilo-Org/kilo-marketplace at commit ff51758, republished under its Apache-2.0 licence (© Kilo-Org). 656 words, ~1,878 tokens.
Download SKILL.mdSave it as
.claude/skills/datarobot-feature-engineering/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- datarobot-feature-engineering
- description
- Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities. Use when working with feature engineering, feature discovery, or analyzing feature importance in DataRobot.
- metadata.category
- data
DataRobot Feature Engineering Skill
This skill provides guidance for working with features in DataRobot, including understanding automated feature engineering, analyzing feature importance, and optimizing feature sets.
Quick Start
Most common use case: Analyze feature importance for a model
- Get feature importance:
get_feature_importance(model_id)to get importance scores - Analyze top features: Sort by importance and identify key drivers
- Export feature list:
export_feature_list(project_id)to document features
Example: "Show me the top 10 most important features for model xyz123"
When to use this skill
Use this skill when you need to:
- Understand what features DataRobot creates automatically
- Analyze feature importance for models
- Discover which features drive predictions
- Optimize feature sets for better performance
- Understand feature types and transformations
- Export feature lists and definitions
Key capabilities
1. Feature Discovery
- Understand automated feature engineering in DataRobot
- Review derived features and transformations
- Identify feature types (numeric, categorical, text, date)
- Explore feature relationships and interactions
2. Feature Importance Analysis
- Get feature importance scores for models
- Understand which features drive predictions
- Compare feature importance across models
- Identify redundant or low-value features
3. Feature Optimization
- Select important features for model performance
- Remove low-importance features to reduce complexity
- Understand feature impact on predictions
- Optimize feature sets for deployment
4. Feature Documentation
- Export feature lists and definitions
- Document feature transformations
- Understand feature derivation logic
- Share feature information with stakeholders
Workflow examples
Example 1: Analyze feature importance
User request: "Show me the top 10 most important features for model xyz123 and explain what they mean."
Agent workflow:
- Get feature importance scores for the model
- Sort features by importance (descending)
- Get top 10 features with their scores
- Retrieve feature metadata and descriptions
- Explain what each feature represents and why it's important
- Provide insights on feature relationships
Example 2: Optimize feature set for deployment
User request: "Create a simplified feature set for deployment abc123, keeping only features with importance > 0.1."
Agent workflow:
- Get feature importance for the deployed model
- Filter features by importance threshold (> 0.1)
- Verify filtered features are sufficient for predictions
- Document the optimized feature set
- Update deployment configuration if needed
Using DataRobot SDK
This skill guides you to use the DataRobot Python SDK directly. Install the SDK if needed:
pip install datarobotShow full SKILL.md (286 more words)Show less
Key SDK Operations
Use these DataRobot SDK methods for feature analysis:
Feature Information:
model.get_features()- List all features in a modelmodel.get_feature_impact()- Get feature importance scoresproject.get_features()- List features in a project
Feature Analysis:
feature.name- Feature namefeature.feature_type- Feature type (Numeric, Categorical, etc.)feature.importance- Feature importance score
See the Common Patterns section below for complete examples.
Best practices
- Review automated features: DataRobot creates many derived features automatically - review them
- Focus on important features: Pay attention to high-importance features for insights
- Understand feature types: Different feature types require different handling
- Feature documentation: Document important features for stakeholders
- Feature selection: Consider removing very low-importance features for simplicity
- Feature stability: Consider feature stability over time, not just importance
Common patterns
Pattern 1: Feature importance analysis
import datarobot as dr
import os
# Initialize client
client = dr.Client(
token=os.getenv("DATAROBOT_API_TOKEN"),
endpoint=os.getenv("DATAROBOT_ENDPOINT")
)
# Get model and feature importance
model = dr.Model.get("xyz123")
feature_impact = model.get_feature_impact()
# Sort by importance
sorted_features = sorted(
feature_impact,
key=lambda x: x.get('impactNormalized', 0),
reverse=True
)
# Get top 10 features
top_features = sorted_features[:10]
for feature in top_features:
print(f"{feature['featureName']}: {feature.get('impactNormalized', 0):.3f}")Pattern 2: Feature filtering
import datarobot as dr
# Get model and feature importance
model = dr.Model.get("xyz123")
feature_impact = model.get_feature_impact()
# Filter by importance threshold (> 0.1)
important_features = [
f for f in feature_impact
if f.get('impactNormalized', 0) > 0.1
]
print(f"Found {len(important_features)} features with importance > 0.1")Feature types in DataRobot
Numeric Features
- Continuous numeric values
- Automatically scaled and normalized
- Can be used in mathematical operations
Categorical Features
- Discrete categories or labels
- Automatically encoded (one-hot, target encoding)
- Important for many model types
Text Features
- Text data (descriptions, comments)
- Automatically processed with NLP techniques
- Creates multiple text-derived features
Date/Time Features
- Temporal data
- Automatically creates time-based features
- Important for time series models
Understanding feature importance
Feature importance scores indicate:
- High importance (> 0.1): Feature significantly impacts predictions
- Medium importance (0.05-0.1): Feature contributes to predictions
- Low importance (< 0.05): Feature has minimal impact
Note: Importance thresholds vary by model type and problem domain.
Error handling
Common errors and solutions:
- Feature not found: Verify feature name and model compatibility
- Importance unavailable: Some model types don't provide importance scores
- Feature access errors: Check project and model permissions
SDK Setup
Install DataRobot SDK
pip install datarobotInitialize Client
import datarobot as dr
import os
client = dr.Client(
token=os.getenv("DATAROBOT_API_TOKEN"),
endpoint=os.getenv("DATAROBOT_ENDPOINT", "https://app.datarobot.com")
)Resources
© Kilo-Org, 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
SKILL.md and 1 other file in skills/datarobot-feature-engineering of Kilo-Org/kilo-marketplace.
- SKILL.md
- LICENSE
Open the folder on GitHubat commit ff51758
Compare with similar skills
Datarobot Feature Engineering 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Datarobot Feature Engineering this skillKilo-Org/kilo-marketplace | 190 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.7k | 16 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill | 188 | — | ~4k | Automated safety check: Pass | MIT | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 5 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Geomlitalo-goncalves/geoML | 109 | — | ~4.6k | Automated safety check: Pass | GPL-3.0 | |
| QuantMind Training Config Generatorqusong0627/QuantMind | 1.7k | — | ~1.5k | Automated safety check: Pass | AGPL-3.0 |
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Categories
Questions about Datarobot Feature Engineering
What does Datarobot Feature Engineering do?
Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities. Datarobot Feature Engineering is an agent skill from Kilo-Org/kilo-marketplace. Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities.
When should I use Datarobot Feature Engineering?
Datarobot Feature Engineering fits situations like: working with feature engineering; feature discovery; analyzing feature importance in DataRobot.
How do I install Datarobot Feature Engineering in Claude Code?
Run `npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a claude-code`. Or copy the skill folder (skills/datarobot-feature-engineering in Kilo-Org/kilo-marketplace) into .claude/skills/datarobot-feature-engineering in your project. Claude Code loads it when a task matches its description.
How do I install Datarobot Feature Engineering in Codex?
Run `npx skills add Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a codex`. Or copy the skill folder (skills/datarobot-feature-engineering in Kilo-Org/kilo-marketplace) into .agents/skills/datarobot-feature-engineering in your project. Codex loads it when a task matches its description.
Can I use Datarobot Feature Engineering 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 Kilo-Org/kilo-marketplace --skill datarobot-feature-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/datarobot-feature-engineering, .gemini/skills/datarobot-feature-engineering, .github/skills/datarobot-feature-engineering and .opencode/skills/datarobot-feature-engineering in your project.
What does Datarobot Feature Engineering need to run?
Going by SKILL.md and its folder, Datarobot Feature Engineering needs the command-line tools its instructions call (pip) and credentials named DATAROBOT_API_TOKEN. Our summary lists: Python 3; A credential in DATAROBOT_API_TOKEN.
Does Datarobot Feature Engineering access the network?
SKILL.md names 2 domains. As links in the text: docs.datarobot.com and datarobot-public-api-client.readthedocs-hosted.com. This is read from the text; nothing was executed.
Is Datarobot Feature Engineering 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 Datarobot Feature Engineering use?
Datarobot Feature Engineering is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Datarobot Feature Engineering use?
About 1.9k tokens (SKILL.md is roughly 7.5k 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 Datarobot Feature Engineering?
Skills that share tags, products or a category with Datarobot Feature Engineering: 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 Datarobot Feature Engineering?
Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 190 GitHub stars. The repository holds 86 skills in this directory. The repository was last updated on September 28, 2026.
Source: Kilo-Org/kilo-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.