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-data-preparationgit 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-data-preparation)<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-data-preparation"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-data-preparation/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-data-preparation"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-data-preparation.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 | $0.00043 | $0.01500 |
| Opus 5 | $0.00022 | $0.00750 |
| Sonnet 5 | $0.00009 | $0.00300 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
datarobot-data-preparation 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 12d 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
92% identical to datarobot-data-preparation — 32 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataRobot Data Preparation Skill
This skill provides guidance for preparing and managing data in DataRobot, including uploading datasets, validating data quality, and managing dataset versions.
Quick Start
Most common use case: Upload and validate a dataset
- Upload dataset:
upload_dataset(file_path, dataset_name)to upload data - Validate data:
validate_dataset(dataset_id)to check data quality - Check schema:
get_dataset_schema(dataset_id)to review structure
Example: "Upload sales_data.csv and check if it's ready for training"
When to use this skill
Use this skill when you need to:
- Upload datasets to DataRobot
- Validate data before project creation
- Manage dataset versions and updates
- Check data quality and completeness
- Prepare data for training or predictions
- Handle data format conversions
- Connect to external data sources
Key capabilities
1. Dataset Upload
- Upload CSV, Parquet, and other file formats
- Connect to databases and data warehouses
- Handle large datasets efficiently
- Manage dataset metadata and descriptions
2. Data Validation
- Validate data formats and schemas
- Check for missing values and data quality issues
- Verify column types and formats
- Identify potential data problems
3. Dataset Management
- List and search datasets
- Update dataset metadata
- Create dataset versions
- Delete or archive old datasets
4. Data Preparation
- Clean and preprocess data
- Handle missing values
- Format data for DataRobot requirements
- Prepare prediction datasets
Workflow examples
Example 1: Upload and validate dataset
User request: "Upload my sales_data.csv file and check if it's ready for training."
Agent workflow:
- Upload the CSV file to DataRobot
- Validate the dataset structure and format
- Check for missing values and data quality issues
- Verify column types are appropriate
- Check for potential issues (leakage, formatting)
- Report validation results and recommendations
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 237 lines · 43 tokens per session scan A aa0c3cbfef58
datarobot-data-preparation is a skill published in the GitHub repository datarobot-oss/datarobot-agent-skills (25 stars, last pushed yesterday), licensed Apache-2.0. It adds 43 tokens to every session and 1,500 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to datarobot-data-preparation, differing in 32 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-cost-optimization
Use when an LLM feature costs too much. Covers prompt caching, context reduction, model routing, batching, output limits, and finding where the tokens actually go.
llm-evaluation
Use when measuring the quality of an LLM feature. Covers building an evaluation set, choosing metrics, LLM-as-judge and its pitfalls, regression testing prompts, and evaluating in production.
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.