Skill UI

PIP kicks in when tasks fail repeatedly or the assistant becomes passive, layering Western big-tech performance rhetoric, a systematic debugging methodology, and proactivity enforcement so every code, ops, research, deployment, or writing task is owned, verified, and driven to completion.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

This skill generates deep, clickable URLs to various resources within the Arize monitoring UI. It supports linking to specific traces, spans, sessions, datasets, evaluators, and annotation configurations. It is essential for sharing specific debugging artifacts or navigating directly to key data points within the MLOps platform by requiring precise resource IDs and time ranges.

This advanced prompt simulates a Senior Project Manager and DevOps specialist. It takes comprehensive feature artifacts (PRD, technical breakdowns, testing plans) and generates a structured, automated GitHub project plan. It enforces Agile best practices (Epic > Feature > Story > Enabler > Task) and includes templates for issue creation, dependency management, risk assessment, and Definition of Done, ensuring robust project tracking within GitHub.

This toolkit provides comprehensive automated control over CircleCI CI/CD pipelines via Rube MCP. Users can trigger new builds, monitor workflow and job statuses, retrieve detailed job information, download build artifacts, and extract detailed test metadata. It streamlines complex DevOps operations by managing the entire CI/CD lifecycle.

The comprehensive command-line interface (CLI) for interacting with all major features of GitHub. This tool allows developers to manage repositories, create and review pull requests, track issues, utilize Codespaces, and perform advanced organizational tasks—all directly from the terminal. It is essential for streamlining DevOps workflows and automating GitHub operations.

This guide provides comprehensive patterns for migrating Enzyme unit tests to React Testing Library (RTL), especially when upgrading to React 18. It emphasizes the crucial philosophical shift from testing internal implementation details (like state or props) to asserting visible user behavior. Learn best practices for rewriting shallow/mount/simulate calls into modern, accessible, and resilient RTL queries, ensuring your tests reflect how a user genuinely interacts with the component.

Provides a comprehensive reference for migrating deprecated and unsafe class component lifecycle methods (such as componentWillMount, componentWillReceiveProps, and componentWillUpdate) to modern, React 18 compliant patterns. This guide is essential when updating legacy codebases, helping developers correctly identify the semantic purpose of the method and choose the correct replacement hook (e.g., componentDidMount or getDerivedStateFromProps), thus preventing common migration errors.

A comprehensive guide detailing best practices for building, reviewing, and deploying Salesforce Flows. This skill covers selecting the correct Flow type (e.g., Screen, After-Save), ensuring bulk safety by preventing DML/SOQL in loops, implementing robust fault handling paths, and performing automation density checks to ensure scalable and resilient automation.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).

This comprehensive CLI tool enables users to manage all core resources within Azure DevOps from a command line interface. It provides full control over CI/CD pipelines, source code repositories (Git), work items (Boards), artifacts, and service connections. Ideal for scripting, advanced automation workflows, and integrating DevOps processes into larger continuous delivery systems.

Builds production-ready rollout plans covering checks, procedures, verification signals, rollback, and communications for infrastructure or application changes across environments.