git:20260825.cfbd8e9 to git:20260827.ad4f673

36 added, 34 removed. Audit A to A.

# DataRobot Agent Skills Library

- This file provides instructions for an agent to use DataRobot skills. An agent will automatically load these instructions when working with DataRobot-related tasks.

+ This file provides instructions for an agent to use DataRobot skills. An agent automatically loads these instructions when working with DataRobot-related tasks.

- ## DataRobot Authentication

+ ## DataRobot authentication

- If any DataRobot skill fails due to missing or invalid credentials, invoke `datarobot-setup` before retrying the original task. Do not print manual instructions — run the skill.

+ If any DataRobot skill fails due to missing or invalid credentials, invoke `datarobot-setup` before retrying the original task. Do not print manual instructions—run the skill.

- ## Should I Add a Skill Here?

+ ## Should I add a skill here?

- There are many places you can add skills for use. This repository is for customer facing skills that help other build more effectively in DataRobot. Here is the goal, intended use, and criteria for determining if your skill is correct for this library.

+ There are many places to add skills for use. This repository is for customer-facing skills that help others build more effectively in DataRobot. This section covers the goal, intended use, and criteria for determining if a skill belongs in this library.

### Goal

- As always, our goal is to ensure that enterprises can get agents into production. Skills offer powerful functionality that tell agents how to THINK while protecting their context window. This allows agent to one/few shot tasks that before needed complex logic built into the agent + would always run into context issues. Skills DR offers should open up enterprise use cases, making them more viable in production.

+ The goal is to ensure that enterprises can get agents into production. Skills offer powerful functionality that tells agents how to *think* while protecting their context window. This allows agents to one-shot or few-shot tasks that previously required complex logic built into the agent and often ran into context window issues. Skills DataRobot offers open up enterprise use cases, making them more viable in production.

- ### Intended Use of This Skill Library

+ ### Intended use of this skill library

- Generally, we expect these skills to be used by code assistants. We will make the skills in the DR Skills repo in code assist marketplaces like Cursor and Claude Code. These skills will also fuel the DR agent assist.

+ These skills are generally used by code assistants. The skills in this repository are available through code assistant marketplaces such as Cursor and Claude Code. These skills also power the DataRobot agent assist.

- ### Criteria for Addint Skills

+ ### Criteria for adding skills

- 1. A Skill should solve a complex enterprise problem. They should tackle a problem or functionality that is required by enterprises to either get an agent in production or deploy an agent that actually provides value.

- 2. Skills should not just proxy to existing MCP servers (though that can be a component of them) - we will proxy to MCP servers via our MCP gateway

- 3. Assess with the following questions

- 1. Is the task complex enough? (or can an LLM with basic tools achieve the same result?)

- 2. Is the output valuable to an enterprise? Does is tackle a problem that’s repeatable? That costs enterprises many dev hours and specialized knowledge? That is error prone and complex for humans to do?

- 3. Is the task viable to be done with an LLM? Skills still cant do everything

+ Evaluate skills against the following criteria:

+ 1. A skill solves a complex enterprise problem. It tackles a problem or functionality required by enterprises to either get an agent into production or deploy an agent that provides real value.

+ 2. A skill does not just proxy to an existing MCP server, though that can be a component of it. DataRobot proxies to MCP servers via the DataRobot MCP gateway.

+ 3. Assess with the following questions:

+ - Is the task complex enough, or can an LLM with basic tools achieve the same result?

+ - Is the output valuable to an enterprise? Does it tackle a repeatable problem that costs enterprises many dev hours and specialized knowledge, and that is error-prone and complex for humans to do?

+ - Is the task viable to be done with an LLM? Skills still can't do everything.

- ## Naming Convention

+ ## Naming convention

All DataRobot skills follow the naming convention `datarobot-<category>` where `<category>` describes the skill's focus area. This ensures:

- Clear identification of DataRobot-specific skills

- Consistent naming across the skill library

- Easy discovery and organization

- In addition to the general `datarobot-<category>` for naming, if there is deeper grouping within the product area such as Workload or Apps and you expect more than one skill in the same area, we recommend using a common prefix for those as well such as `datarobot-app-framework-<skill>` for simpler grouping and code ownership.

+ In addition to the general `datarobot-<category>` naming pattern, if there is deeper grouping within the product area, such as Workload or Apps, and more than one skill is expected in the same area, use a common prefix, such as `datarobot-app-framework-<skill>`, for simpler grouping and code ownership.

## Rules

- We strongly prefer human written skills. When assisting skill library authors, please encourage them to edit

- and adjust their skills themselves. We encourage advise, feedback, and recommendations from LLMs, but to stay brief and

- properly manage the context window itself the human should edit the SKILLs.md. Agent assisted coding for scripts and

- other references within a skill is perfectly acceptable.

- This repo is organized using GitHub Code Owners. Please ensure all new skills developed have a proper github team

- or person for the new skill added.

+ DataRobot strongly prefers human-written skills. When assisting skill library authors, encourage them to edit

+ and adjust their skills themselves. LLM advice, feedback, and recommendations are welcome, but to keep skills brief and

+ manage the context window effectively, the human author edits `SKILL.md` directly. Agent-assisted coding for scripts and

+ other references within a skill is acceptable.

+ This repository is organized using GitHub Code Owners. Ensure all new skills have a GitHub team

+ or person added as the owner.

## Workflow

+ This section covers the day-to-day development workflow and plugin version management for this repository.

### Basic workflow

- We use taskfile.dev for task running in this repo. All changes must be validated regularly with `task lint` that will

- check that all copyrights, Skills.md files are structured, naming conventions are obeyed, Python files are properly formatted, linters are executed, etc. It is the way to validate any changes.

+ This repository uses [Task](https://taskfile.dev/) for running tasks. Validate all changes regularly with `task lint`,

+ which checks that copyrights are present, `SKILL.md` files are structured correctly, naming conventions are followed, Python files are properly formatted, linters run, and more. This is the way to validate changes.

- Install and test the skills after prompting the user for the trigger phrase you expect

+ Install and test the skills after prompting the user for the expected trigger phrase.

### Plugin version management

- Every PR must bump the shared plugin version — in `package.json`, `.claude-plugin/*.json`, `.cursor-plugin/plugin.json`, and `gemini-extension.json` — to the same new value using SemVer rules (patch/minor/major), and rename `CHANGELOG.md`'s `[Unreleased]` section to that version with today's date, adding a fresh empty `[Unreleased]` section. This applies to every PR, not just ones that directly edit a plugin config file. Follow `CONTRIBUTING.md` for the full versioning and changelog rules.

+ Versions are automatic. Add a human-written `[Unreleased]` item describing the change in this pull request. For more information, see [CONTRIBUTING.md](CONTRIBUTING.md).

## SDK usage

- Skills guide you to use the **DataRobot Python SDK** directly. Each skill includes:

+ Skills provide direct guidance for using the **DataRobot Python SDK**. Each skill includes:

- - **SDK operations** - Which SDK methods to use

- - **Code examples** - Complete working examples

- - **Workflows** - Step-by-step guidance

- - **Best practices** - Tips and recommendations

+ - **SDK operations** — the SDK methods to use.

+ - **Code examples** — complete, working examples.

+ - **Workflows** — step-by-step guidance.

+ - **Best practices** — tips and recommendations.

Install the SDK: `pip install datarobot`

Initialize client:

```python

import datarobot as dr

dr.Client()

```

See each skill's "Using DataRobot SDK" section for specific operations and examples.