Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datarobot-oss/datarobot-agent-skills --skill datarobot-feature-engineeringgit clone --depth 1 https://github.com/datarobot-oss/datarobot-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-feature-engineering)<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-feature-engineering"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-feature-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-feature-engineering"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-feature-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01460 |
| Opus 5 | $0.00024 | $0.00730 |
| Sonnet 5 | $0.00010 | $0.00292 |
| Haiku 4.5 | $0.00005 | $0.00146 |
Grade A, and why
datarobot-feature-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to datarobot-feature-engineering — 35 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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."
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 218 lines · 48 tokens per session scan A 1d85aa453188
datarobot-feature-engineering is a skill published in the GitHub repository datarobot-oss/datarobot-agent-skills (25 stars, last pushed today), licensed Apache-2.0. It adds 48 tokens to every session and 1,460 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to datarobot-feature-engineering, differing in 35 lines, and is treated as a copy.
Other skills, from other repositories
upstash-vector-js
Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model…
fine-tuning
Use when considering fine-tuning a model. Covers when fine-tuning beats prompting or RAG, dataset construction, LoRA and full fine-tuning, evaluation, and the failure modes that waste the effort.
llm-integration
Use when integrating an LLM API into an application. Covers streaming, retries and rate limits, timeouts, caching, fallback across providers, and the production concerns that a tutorial integration ignores.
ml-pipeline
Use when building or operating a machine learning pipeline. Covers feature engineering, training reproducibility, train/serve skew, deployment, monitoring for drift, and retraining.
prompt-engineering
Use when writing or improving prompts for a language model. Covers instruction structure, examples, reasoning elicitation, output formatting, and systematically diagnosing why a prompt fails.
rag
Use when building retrieval-augmented generation. Covers chunking, embedding and hybrid search, reranking, grounding and citation, and diagnosing whether a bad answer is a retrieval failure or a generation failure.