Verified Data Analysis with pandas
pipeshub-ai/pipeshub-ai
Loads, cleans, aggregates and joins tabular data with pandas under a verification rule: every number reported must be one that the code actually printed.
Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects.
Install Datarobot Data Preparation
$ npx skills add Kilo-Org/kilo-marketplace --skill datarobot-data-preparation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-data-preparation --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-data-preparation .claude/skills/datarobot-data-preparation && 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-data-preparation" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-data-preparation into .claude/skills/datarobot-data-preparation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-data-preparation", 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-data-preparationType 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-data-preparation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-data-preparation --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-data-preparation .agents/skills/datarobot-data-preparation && 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-data-preparation" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-data-preparation into .agents/skills/datarobot-data-preparation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-data-preparation", 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-data-preparation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-data-preparation --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-data-preparation .cursor/skills/datarobot-data-preparation && 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-data-preparation" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-data-preparation into .cursor/skills/datarobot-data-preparation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-data-preparation", 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-data-preparation--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-data-preparation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Kilo-Org/kilo-marketplace datarobot-data-preparation --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-data-preparation .gemini/skills/datarobot-data-preparation && 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-data-preparation" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-data-preparation into .gemini/skills/datarobot-data-preparation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-data-preparation", 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-data-preparationInstalls 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-data-preparation -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-data-preparation .github/skills/datarobot-data-preparation && 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-data-preparation" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-data-preparation into .github/skills/datarobot-data-preparation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-data-preparation", 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-data-preparation -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-data-preparation --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-data-preparation .opencode/skills/datarobot-data-preparation && 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-data-preparation" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/datarobot-data-preparation into .opencode/skills/datarobot-data-preparation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datarobot-data-preparation", 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-data-preparation- GitHub stars
- 190
- Token cost
- ~1.8k tokens
- SKILL.md length
- 655 words
- Files
- 4 (incl. scripts)
- Skills in repo
- 85
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects.
- Works in 4 steps: Dataset Upload → Data Validation → Dataset Management → …
- Uploading datasets
- SKILL.md covers Quick Start, When to use this skill, Key capabilities and Workflow examples, plus 9 more sections
- Runs Python scripts from its folder; calls pip and python; needs DATAROBOT_API_TOKEN
What it does
Datarobot Data Preparation is an agent skill from Kilo-Org/kilo-marketplace. 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.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `scripts/upload_dataset.py`).
It sits in Data & Analytics, covering Data cleaning and CSV and tabular files. 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
- Uploading datasets
- Validating data for DataRobot
Example prompts
- “Use the datarobot-data-preparation skill to tool and guidance for data upload, dataset management, data validation, and preparing data for DataRobot…”
- “/datarobot-data-preparation”
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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pippython
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 Data Preparation loads about 1.8k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 655 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); the scripts in this folder 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). 655 words, ~1,815 tokens.
Download SKILL.mdSave it as
.claude/skills/datarobot-data-preparation/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- 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.
- metadata.category
- data
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 datarobotKey SDK Operations
Use these DataRobot SDK methods for data management:
Dataset Operations:
dr.Dataset.create_from_file(file_path, name)- Upload datasetdr.Dataset.get(dataset_id)- Get dataset detailsdr.Dataset.list()- List all datasetsdataset.row_count- Get row countdataset.column_count- Get column count
Dataset Information:
dataset.name- Dataset namedataset.id- Dataset IDdataset.created_at- Creation timestamp
See the Common Patterns section below for complete examples.
Show full SKILL.md (255 more words)Show less
Helper Scripts
This skill includes executable helper scripts that the agent 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"The agent 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
import os
# Initialize client
client = dr.Client(
token=os.getenv("DATAROBOT_API_TOKEN"),
endpoint=os.getenv("DATAROBOT_ENDPOINT")
)
# 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 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 3 other files (scripts) in skills/datarobot-data-preparation of Kilo-Org/kilo-marketplace.
- SKILL.md
- LICENSE
- local.patch
- scripts/upload_dataset.py
Open the folder on GitHubat commit ff51758
Compare with similar skills
Datarobot Data Preparation 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 Data Preparation this skillKilo-Org/kilo-marketplace | 190 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Verified Data Analysis with pandaspipeshub-ai/pipeshub-ai | 3.8k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Portaljs Check Data Qualitydatopian/portaljs | 2.4k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Dataset Quality Auditzebbern/claude-code-guide | 4.7k | — | ~996 | Automated safety check: Pass | MIT | |
| Splitting Datasetsjeremylongshore/tons-of-skills-marketplace | 2.8k | 1 repos | ~836 | Automated safety check: Pass | MIT | |
| Data Table AnalysisNVIDIA-AI-Blueprints/deep-researcher-agent | 883 | — | ~2.5k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Datarobot Data Preparation
What does Datarobot Data Preparation do?
Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects. Datarobot Data Preparation is an agent skill from Kilo-Org/kilo-marketplace. Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects.
When should I use Datarobot Data Preparation?
Datarobot Data Preparation fits situations like: uploading datasets; validating data for DataRobot.
How do I install Datarobot Data Preparation in Claude Code?
Run `npx skills add Kilo-Org/kilo-marketplace --skill datarobot-data-preparation -a claude-code`. Or copy the skill folder (skills/datarobot-data-preparation in Kilo-Org/kilo-marketplace) into .claude/skills/datarobot-data-preparation in your project. Claude Code loads it when a task matches its description.
How do I install Datarobot Data Preparation in Codex?
Run `npx skills add Kilo-Org/kilo-marketplace --skill datarobot-data-preparation -a codex`. Or copy the skill folder (skills/datarobot-data-preparation in Kilo-Org/kilo-marketplace) into .agents/skills/datarobot-data-preparation in your project. Codex loads it when a task matches its description.
Can I use Datarobot Data Preparation 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-data-preparation -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-data-preparation, .gemini/skills/datarobot-data-preparation, .github/skills/datarobot-data-preparation and .opencode/skills/datarobot-data-preparation in your project.
What does Datarobot Data Preparation need to run?
Going by SKILL.md and its folder, Datarobot Data Preparation needs Python for the scripts in its folder, the command-line tools its instructions call (pip and python) and credentials named DATAROBOT_API_TOKEN. Our summary lists: Python 3; A credential in DATAROBOT_API_TOKEN.
Does Datarobot Data Preparation 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 Data Preparation 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Datarobot Data Preparation use?
Datarobot Data Preparation 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 Data Preparation use?
About 1.8k tokens (SKILL.md is roughly 7.3k 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 Data Preparation?
Skills that share tags, products or a category with Datarobot Data Preparation: Verified Data Analysis with pandas (pipeshub-ai/pipeshub-ai, 3.8k stars), Portaljs Check Data Quality (datopian/portaljs, 2.4k stars), Dataset Quality Audit (zebbern/claude-code-guide, 4.7k stars) and Splitting Datasets (jeremylongshore/tons-of-skills-marketplace, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Datarobot Data Preparation?
Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 190 GitHub stars. The repository holds 85 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.