Skill UI

Provides a comprehensive guide for crafting and maintaining Claude Code skills, covering auto-activation hooks, skill-rules configuration, guardrails, domain skills, trigger testing, and Anthropic-aligned best practices such as the 500-line rule and progressive disclosure.

A deep expert system for Anthropic's Claude Code CLI. This skill maximizes productivity by managing advanced features like hooks, persistent context via CLAUDE.md, memory management, sub-agent orchestration, and integrating with local development ecosystems. Ideal for complex coding tasks, debugging, and advanced workflow automation.

Validates SKILL.md files against the agentskills specification and Anthropic best practices, catching structure, semantic, naming, and quality issues in a single read-only pass before publishing or debugging skills.

Documented best practices for building applications with Anthropic Claude via Python and TypeScript SDKs, covering messaging, streaming, tools, vision, reasoning, caching, batching, and Agent workflows to optimize prompts, tokens, and cost.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

This comprehensive tool validates Claude Code plugins against official Anthropic specifications and internal enterprise standards. It performs two main types of checks: structural validation (checking `plugin.json` fields, file references, and permissions) and content validation (conducting deep grading using `SKILL.md`). Use this when building, auditing, or preparing plugins for marketplace submission to ensure compliance and quality.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

This skill guides developers on building LLM-powered applications using the Claude API, Anthropic SDKs, or the Agent SDK. It provides comprehensive guidance on language detection, best practices (like default model versions and streaming), and choosing the correct development surface (API call, Workflow, or Agent) based on the use case.

Example skill that walks through the Anthropic SKILL.md structure, showcasing when to load it, how to author steps, and how to reference bundled resources for skill creation.

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.

A comprehensive guide for developers encountering complex issues with the Claude API, including context window overflow management, debugging tool use failures, handling streaming corruption, and diagnosing unexpected stop reasons. Provides Python code snippets and diagnostic commands to ensure robust AI integration.

A guide detailing four validated architectural patterns for integrating Claude APIs, catering to diverse scale and use cases. Choose between Serverless, Streaming Microservice, Queue-Based Pipelines, or complex Orchestrators to optimize performance, cost, and latency for your application, whether it's a real-time chatbot or large-scale batch processing.