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datarobot-data-preparation

Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Use when uploading datasets, managing data, or validating data for DataRobot.

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Preis unbestätigt★ 25 GitHub-StarsVerzeichnis aktualisiert · 12. Sept. 2026agent-skill

Übersicht

Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Use when uploading datasets, managing data, or validating data for DataRobot.

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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

Example 2: Prepare prediction dataset

User request: "Prepare a prediction dataset based on the training data structure from project abc123."

Agent workflow:

Get the training dataset structure from the project
Identify required columns and data types
Create a template with the same structure
Validate the template matches requirements
Provide guidance on filling in prediction values

Using DataRobot SDK

This skill guides you to use the DataRobot Python SDK directly. Install the SDK if needed:

pip install datarobot

Key SDK Operations

Use these DataRobot SDK methods for data management:

Dataset Operations:

dr.Dataset.create_from_file(file_path, name) - Upload dataset
dr.Dataset.get(dataset_id) - Get dataset details
dr.Dataset.list() - List all datasets
dataset.row_count - Get row count
dataset.column_count - Get column count

Dataset Information:

dataset.name - Dataset name
dataset.id - Dataset ID
dataset.created_at - Creation timestamp

See the Common Patterns section below for complete examples.

Helper Scripts

This skill includes executable helper scripts that Claude can run directly:

scripts/upload_dataset.py - Upload a dataset file to DataRobot

Usage example:

# Upload dataset
python scripts/upload_dataset.py sales_data.csv "Sales Data Q4 2024"

Claude can run this script directly or use it as reference when writing code.

Best practices

Data quality: Clean and validate data before upload
File formats: Use appropriate formats (CSV for small, Parquet for large)
Naming conventions: Use clear, descriptive dataset names
Metadata: Add descriptions and tags for better organization
Versioning: Create versions for important datasets
Data validation: Always validate data before using in projects

Common patterns

Pattern 1: Upload and validate

import datarobot as dr

# Initialize client
dr.Client()

# Upload dataset
dataset = dr.Dataset.create_from_file(
    file_path="sales_data.csv", name="Sales Data Q4 2024"
)

print(f"Dataset ID: {dataset.id}")
print(f"Rows: {dataset.row_count}, Columns: {dataset.column_count}")

# Get dataset details
dataset_info = dr.Dataset.get(dataset.id)
print(f"Dataset name: {dataset_info.name}")
print(f"Created: {dataset_info.created_at}")

Pattern 2: Dataset management

import datarobot as dr

# List all datasets
datasets = dr.Dataset.list()
print(f"Found {len(datasets)} datasets")

# Search for specific dataset
for dataset in datasets:
    if "sales" in dataset.name.lower():
        print(f"Found: {dataset.name} (ID: {dataset.id})")

# Get specific dataset
dataset = dr.Dataset.get("abc123")
print(f"Dataset: {dataset.name}")
print(f"Size: {dataset.row_count} rows x {dataset.column_count} columns")

Data format requirements

CSV Files

UTF-8 encoding recommended
Headers in first row
Consistent delimiters (comma, tab)
Proper date/time formatting

Parquet Files

Columnar format, efficient for large datasets
Preserves data types
Better compression than CSV

Database Connections

Support for various databases
Connection credentials required
Query-based data access

Data quality checks

Common checks to perform:

Missing values: Identify columns with high missing value rates
Data types: Verify columns have correct types
Value ranges: Check for outliers and invalid values
Duplicates: Identify duplicate records
Consistency: Check for data consistency issues

Error handling

Common errors and solutions:

Upload failures: Check file format, size limits, encoding
Validation errors: Fix data quality issues before proceeding
Schema mismatches: Ensure data structure matches expectations
Access issues: Verify permissions for dataset operations

SDK Setup

Install DataRobot SDK

pip install datarobot

Initialize Client

import datarobot as dr

dr.Client()

Resources

Quelle ansehen ↗

Dateimetadaten

name: datarobot-data-preparation
description: Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Use when uploading datasets, managing data, or validating data for DataRobot.

Originaltext anzeigen

---
name: datarobot-data-preparation
description: Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Use when uploading datasets, managing data, or validating data for DataRobot.
---

# 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

1. **Upload dataset**: `upload_dataset(file_path, dataset_name)` to upload data
2. **Validate data**: `validate_dataset(dataset_id)` to check data quality
3. **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**:
1. Upload the CSV file to DataRobot
2. Validate the dataset structure and format
3. Check for missing values and data quality issues
4. Verify column types are appropriate
5. Check for potential issues (leakage, formatting)
6. Report validation results and recommendations

### Example 2: Prepare prediction dataset

**User request**: "Prepare a prediction dataset based on the training data structure from project abc123."

**Agent workflow**:
1. Get the training dataset structure from the project
2. Identify required columns and data types
3. Create a template with the same structure
4. Validate the template matches requirements
5. Provide guidance on filling in prediction values

## Using DataRobot SDK

This skill guides you to use the DataRobot Python SDK directly. Install the SDK if needed:

```bash
pip install datarobot
```

### Key SDK Operations

Use these DataRobot SDK methods for data management:

**Dataset Operations**:
- `dr.Dataset.create_from_file(file_path, name)` - Upload dataset
- `dr.Dataset.get(dataset_id)` - Get dataset details
- `dr.Dataset.list()` - List all datasets
- `dataset.row_count` - Get row count
- `dataset.column_count` - Get column count

**Dataset Information**:
- `dataset.name` - Dataset name
- `dataset.id` - Dataset ID
- `dataset.created_at` - Creation timestamp

See the [Common Patterns](#common-patterns) section below for complete examples.

## Helper Scripts

This skill includes executable helper scripts that Claude can run directly:

- `scripts/upload_dataset.py` - Upload a dataset file to DataRobot

**Usage example**:
```bash
# Upload dataset
python scripts/upload_dataset.py sales_data.csv "Sales Data Q4 2024"
```

Claude can run this script directly or use it as reference when writing code.

## Best practices

1. **Data quality**: Clean and validate data before upload
2. **File formats**: Use appropriate formats (CSV for small, Parquet for large)
3. **Naming conventions**: Use clear, descriptive dataset names
4. **Metadata**: Add descriptions and tags for better organization
5. **Versioning**: Create versions for important datasets
6. **Data validation**: Always validate data before using in projects

## Common patterns

### Pattern 1: Upload and validate
```python
import datarobot as dr

# Initialize client
dr.Client()

# Upload dataset
dataset = dr.Dataset.create_from_file(
    file_path="sales_data.csv", name="Sales Data Q4 2024"
)

print(f"Dataset ID: {dataset.id}")
print(f"Rows: {dataset.row_count}, Columns: {dataset.column_count}")

# Get dataset details
dataset_info = dr.Dataset.get(dataset.id)
print(f"Dataset name: {dataset_info.name}")
print(f"Created: {dataset_info.created_at}")
```

### Pattern 2: Dataset management
```python
import datarobot as dr

# List all datasets
datasets = dr.Dataset.list()
print(f"Found {len(datasets)} datasets")

# Search for specific dataset
for dataset in datasets:
    if "sales" in dataset.name.lower():
        print(f"Found: {dataset.name} (ID: {dataset.id})")

# Get specific dataset
dataset = dr.Dataset.get("abc123")
print(f"Dataset: {dataset.name}")
print(f"Size: {dataset.row_count} rows x {dataset.column_count} columns")
```

## Data format requirements

### CSV Files
- UTF-8 encoding recommended
- Headers in first row
- Consistent delimiters (comma, tab)
- Proper date/time formatting

### Parquet Files
- Columnar format, efficient for large datasets
- Preserves data types
- Better compression than CSV

### Database Connections
- Support for various databases
- Connection credentials required
- Query-based data access

## Data quality checks

Common checks to perform:

- **Missing values**: Identify columns with high missing value rates
- **Data types**: Verify columns have correct types
- **Value ranges**: Check for outliers and invalid values
- **Duplicates**: Identify duplicate records
- **Consistency**: Check for data consistency issues

## Error handling

Common errors and solutions:

- **Upload failures**: Check file format, size limits, encoding
- **Validation errors**: Fix data quality issues before proceeding
- **Schema mismatches**: Ensure data structure matches expectations
- **Access issues**: Verify permissions for dataset operations

## SDK Setup

### Install DataRobot SDK

```bash
pip install datarobot
```

### Initialize Client

```python
import datarobot as dr

dr.Client()
```

## Resources

- [DataRobot Python SDK Documentation](https://datarobot-public-api-client.readthedocs-hosted.com/)
- [DataRobot Data Management Documentation](https://docs.datarobot.com/en/docs/data/index.html)
- [Data Management: Uploading Datasets](https://docs.datarobot.com/en/docs/data/index.html)
- [Data Management: Data Quality and Validation](https://docs.datarobot.com/en/docs/data/index.html)

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: Apache-2.0
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quick Start references validate_dataset and get_dataset_schema functions that are not defined in the helper scripts or explicitly in the SDK operations list, which may cause confusion.
The skill does not explicitly state prerequisites such as needing a DataRobot account and API key configuration, though it is implied.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 25 GitHub stars
Stars/forks activity: 25 stars, 23 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstKI-geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: datarobot-oss/datarobot-agent-skills
Lizenz: Apache-2.0
Version: Unknown
Letzter GitHub-Push: 12. Sept. 2026
Verzeichnis aktualisiert: 12. Sept. 2026
Anleitungspfad: skills/datarobot-data-preparation/SKILL.md @ e6dddbe327dc

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

58/100

Vielversprechend

Vertrauen

52/100

Do not auto-install

Audit

68/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Quick Start references validate_dataset and get_dataset_schema functions that are not defined in the helper scripts or explicitly in the SDK operations list, which may cause confusion.
The skill does not explicitly state prerequisites such as needing a DataRobot account and API key configuration, though it is implied.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 25 GitHub stars
Stars/forks activity: 25 stars, 23 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 68,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Quick Start references validate_dataset and get_dataset_schema functions that are not defined in the helper scripts or explicitly in the SDK operations list, which may cause confusion.",
      "The skill does not explicitly state prerequisites such as needing a DataRobot account and API key configuration, though it is implied.",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 25 GitHub stars"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 58,
    "label": "Promising"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "Quick Start references validate_dataset and get_dataset_schema functions that are not defined in the helper scripts or explicitly in the SDK operations list, which may cause confusion.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The skill does not explicitly state prerequisites such as needing a DataRobot account and API key configuration, though it is implied."
  ],
  "agent_contract": {
    "task_input": "Use datarobot-data-preparation in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 60/100 Manual review",
      "Audit: 68/100 Needs review",
      "Safety: 24/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "datarobot-oss-datarobot-data-preparation (datarobot-data-preparation)",
      "install_command": "npx skills add datarobot-oss/datarobot-agent-skills --skill datarobot-data-preparation",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "datarobot-oss-datarobot-data-preparation",
      "task": "Use datarobot-data-preparation in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/datarobot-oss-datarobot-data-preparation",
    "api": "https://www.openagentskill.com/api/agent/skills/datarobot-oss-datarobot-data-preparation",
    "audit": "https://www.openagentskill.com/skills/datarobot-oss-datarobot-data-preparation/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=datarobot-oss-datarobot-data-preparation&task=Use%20datarobot-data-preparation%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20datarobot-data-preparation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20datarobot-data-preparation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/datarobot-oss-datarobot-data-preparation/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/datarobot-oss-datarobot-data-preparation"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: datarobot-oss
Quelle: datarobot-oss/datarobot-agent-skills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird datarobot-oss zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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