Skill UI

A structured planning system that uses markdown files (task_plan.md, progress.md) to manage complex, multi-step projects, research tasks, or workflows requiring extensive steps. It provides robust session recovery and includes file tampering detection, treating all plan contents as immutable data. Ideal for highly structured project management.

A comprehensive guide to creating production-grade Product Requirements Documents (PRDs). This skill helps bridge the gap between vague business concepts and concrete technical specifications for modern software systems and AI features. It enforces a structured workflow, ensuring all necessary components—from user stories and success KPIs to technical architecture and risk analysis—are defined.

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

Generates high-converting, SEO-optimized content for competitor comparison and alternative pages. This tool creates structured content for 'X vs Y' layouts, 'Alternatives to X' roundups, and feature matrices, ensuring comprehensive coverage for competitive intent keywords. It includes advanced SEO elements like Schema Markup and strategic CTA placement to maximize conversion rates and search engine ranking.

A comprehensive toolkit designed to automate core Shopify operations via Rube MCP. It provides structured workflows for managing all key aspects of an e-commerce store, including products (listing, creation), orders (tracking, retrieval), customers, collections, and inventory levels. Utilizes direct tool calls for listing, searching, and bulk operations, making it ideal for integrating complex business logic into automated systems.

This comprehensive guide provides a detailed framework for authoring effective and structured SKILL.md files for AI coding agents. It covers best practices for structuring content, including frontmatter fields, defining natural language triggers, establishing core principles, and incorporating advanced elements like decision trees and verification checklists, ensuring agents receive clear, actionable, and consistent technical guidance.

This comprehensive toolkit automates all major Stripe payment operations, including customer management, creating charges, handling subscriptions, generating invoices, and processing refunds. By leveraging structured workflows through Rube MCP, it provides a unified interface for complex financial processes, ensuring reliable and efficient automation of your billing and payment systems.

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 skill provides comprehensive commands to manage the entire AI model evaluation lifecycle using Arize. Users can list, retrieve metadata, and export results from experiments. It is ideal for comparing multiple model runs, benchmarking performance against a dataset, and analyzing model accuracy using structured evaluation workflows (CRUD operations).

This skill provides comprehensive commands for creating, managing, versioning, and querying structured datasets within the Arize platform. It covers dataset CRUD operations, appending examples, and exporting data in various formats (JSON, CSV, Parquet) using the `ax` CLI. Ideal for preparing test data, generating evaluation examples, or retrieving golden datasets for LLM development and model testing.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

This skill analyzes Azure resource groups in depth, identifying all resources, their configurations, and complex interdependencies. It maps relationships—including network connections, data flows, and managed identities—to generate comprehensive, detailed Mermaid architecture diagrams. Use this when you need a clear, structured visualization and full documentation of an Azure environment's topology.