Skill UI

This skill provides a comprehensive, read-only audit of a development workspace, including active repositories, installed plugins, connected services, and environment configurations. It analyzes existing capabilities against best practices to identify functional gaps and recommends the most valuable ECC-native skills, hooks, and workflows needed to enhance the development experience. Ideal for setup planning and architectural reviews.

A comprehensive collection of agent skills for rigorously auditing digital interfaces and user flows. It evaluates designs against 168 research-backed UX/UI principles, detects common design antipatterns, and provides specialized review for AI-powered interfaces, ensuring adherence to modern best practices.

Provides a read-only report showing how much the context-mode feature has preserved session data, including token consumption, savings ratio, and tool-by-tool breakdown for evaluating context efficiency.

Generates structured peer-review quality critiques of academic papers, covering summaries, methodology scrutiny, contribution evaluation, literature positioning, and recommendations across domains whenever a user requests reviews, analyses, or summaries of research publications.

A comprehensive, generics-first utility library designed for Go developers who prefer declarative, functional programming patterns over manual loops. It provides 500+ type-safe functions for common data transformations (Map, Filter, Reduce, GroupBy, etc.) on slices, maps, and strings. By defaulting to immutable operations, it ensures thread safety and predictable state changes. The library also offers specialized packages (lop, lom, loi) to address performance bottlenecks, such as CPU-bound parallel processing or lazy evaluation, allowing engineers to choose the optimal approach for their c...

This skill enables users to create new AI skills from scratch, modify existing ones, and optimize performance through evaluation and benchmarking. It supports iterative development loops, including drafting, testing with prompts, quantitative analysis, and refining descriptions for better triggering accuracy. Ideal for developers building custom agent workflows.

Conducts multi-dimensional code review with quality gates before merging any change. Evaluates correctness, readability, architecture, security, and performance, and proposes concrete structural remedies for flagged problems.

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.

A comprehensive code review tool designed for C#/.NET projects. It evaluates the implementation of key design patterns (Command, Factory, Repository) and adherence to SOLID principles. The review covers architecture, best practices (async/await, logging), security, testability, and clean code principles, providing actionable recommendations for improvement.

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 management capabilities for AI Integrations within the Arize platform. Users can create, list, retrieve, and update credentials for various Large Language Model (LLM) providers, including OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and Vertex AI. These integrations are crucial for enabling Arize's advanced monitoring and evaluation features that require invoking external LLMs.

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).