Skill UI

This module provides comprehensive observability for Anthropic's Claude API integrations. It enables the tracking of critical operational metrics including latency, token usage (input/output), estimated API costs, and error rates. Use it to set up structured logging and Prometheus metrics for robust monitoring and alerting, ensuring reliable and cost-effective AI application deployments.

A specialized skill designed to draft legally binding patent claims based on detailed invention disclosures and prior art reports. It analyzes the core inventive concepts and generates structured claims (independent and dependent) adhering to specific international jurisdictional standards (US, CN, EP) for both methods and apparatuses. This skill defines the legal scope of intellectual property protection.

This guide provides production-ready code patterns for integrating with the Cohere AI API v2 using TypeScript and Python. It covers essential best practices, including singleton client management, robust retry mechanisms, type-safe chat wrappers, streaming, embedding, reranking, and structured JSON output. Use this when establishing team coding standards or refactoring existing Cohere integrations.

This comprehensive guide demonstrates how to implement robust, real-time streaming UIs using Cohere's `chatStream` method. It details handling various event types, including content deltas, citation information for RAG, and structured tool call streams. A practical example is provided for creating a Server-Sent Events (SSE) endpoint, making it ideal for front-end integration with advanced AI features.

Lambda is a compact, machine-native communication protocol specifically designed for agent-to-agent (A2A) messaging. It functions as a shared, unambiguous vocabulary built from 'atoms' (2-character codes), completely bypassing the need for human natural language interpretation. It is ideal for orchestrators, task delegation, and logging structured coordination signals (like heartbeats or state changes) in multi-agent systems. Crucially, Lambda is intended only for machine communication, not for user-facing dialogue.

This harness provides comprehensive, reproducible evaluation pipelines for complex LLM chains and agents (LangChain/LangGraph 1.0). It integrates golden dataset management, LangSmith evaluation, RAGAS metrics, deepeval LLM-as-judge, and structured agent trajectory analysis. Use it when establishing quality benchmarks for new chains, diagnosing performance regressions after model switches, or implementing CI/CD gates to prevent quality drops.

This assistant functions as a professional prompt engineer, specializing in generating highly optimized, production-ready prompts for diverse AI platforms. It analyzes user intent and technical requirements across nine dimensions (Task, Context, Format, etc.), then tailors the prompt structure, constraints, and tone specifically for the target model (e.g., GPT-5.x, Claude, Gemini, Midjourney). Use it when you need to convert a vague idea into a structured, high-performing command for any large language model or generative AI tool.

A comprehensive guide and implementation pattern for handling common errors when working with the Runway AI platform. This skill provides Python SDK examples for tasks like image-to-video generation, along with structured solutions for API issues such as 'Unauthorized' (401) or 'Insufficient credits' (402), ensuring smooth and reliable creative AI workflows.

This debug bundle provides development patterns and SDK integration for RunwayML, a powerful AI video generation and creative platform. Users can leverage this bundle to perform tasks like image-to-video conversion and advanced content creation. It includes structured examples and robust error handling, making it ideal for developers needing to integrate professional-grade AI video capabilities into their applications.

This comprehensive skill guides users through the entire lifecycle of developing AI agent skills. From initial concept (Understand) to structured design, writing the core skill logic, rigorous testing, and final performance optimization. It ensures skills are robust, accurate, and highly triggerable across any domain, including engineering, content creation, and research.

This skill provides a structured framework for capturing, organizing, and maintaining persistent facts about the user. By segmenting facts into topic-specific files (e.g., coding preferences, tech stack, goals), it creates a reliable, objective knowledge base. This structure is crucial for AI agents and applications to achieve highly personalized interactions and provide accurate context.

This meta-skill establishes a rigorous and mandatory workflow for all AI agent interactions. It dictates that before generating any response, the agent must check for and invoke all relevant specialized skills, regardless of the perceived complexity or simplicity of the user request. It defines strict rules for skill prioritization, establishes user instructions as the highest authority, and outlines structured processes for various development scenarios, ensuring systematic and complete task execution.