Skill UI

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

This skill provides comprehensive methods for implementing intelligent model routing using the OpenRouter API. It allows developers to dynamically select the optimal Large Language Model (LLM) based on specific criteria: task complexity (e.g., classification, code generation), required capability (e.g., deep reasoning vs. simple Q&A), cost budget, and latency tolerance. Implement cost-aware, task-based, and complexity-based routing to maximize system efficiency and minimize operational expenses when building multi-model applications.

This skill provides comprehensive reference architectures for building production-grade AI applications using OpenRouter as a unified LLM gateway. It covers three scaling patterns: simple single-service, standard microservice (with caching and budget enforcement), and enterprise event-driven systems (with message queues and workers). Use this when planning system design, reviewing complex AI pipelines, or ensuring scalability and cost control in your LLM integration.

A comprehensive guide to implementing Perplexity's API integration at various scales. It provides three validated architectural blueprints—Direct Widget, Cached Research Layer, and Multi-Query Pipeline—allowing developers to choose the optimal structure based on required query volume, latency goals, and complexity. Learn how to scale from simple search widgets to automated, high-volume research pipelines.

Basic Text Processor provides word/character statistics, simple text transformations, batch file handling, and dual JSON or human-readable outputs to demonstrate a CLAUDE skill template.

This skill provides a comprehensive walkthrough for setting up and testing Sentry error monitoring using both Node.js and Python SDKs. It demonstrates how to capture various event types, including simple messages and full exceptions, and crucially, how to enrich these events with user context, searchable tags, and detailed breadcrumbs. This is essential for verifying robust error logging and monitoring in a production environment.

Streamlines Simple Analytics tasks by orchestrating Composio toolkits through Rube MCP, guiding tool discovery, connection checks, schema-aware arguments, and execution workflows.

Automate Simplesat through the Rube MCP-composed Composio toolkit: discover up-to-date tool schemas, ensure connections are active, and execute workflows with the recommended Rube commands so schema-sensitive operations stay reliable.

SimPO (Simple Preference Optimization) is a state-of-the-art, reference-free method designed for aligning Large Language Models (LLMs) using human preference data. It is an efficient alternative to DPO and PPO, notably outperforming DPO without requiring a separate reference model. It is ideal for practitioners who need faster, simpler, and more resource-efficient fine-tuning for preference alignment.

This skill provides opinionated and evolving constraints designed to guide AI agents effectively when building user interfaces. It offers structured patterns and guidelines, ensuring that the generated interfaces adhere to specific best practices and development rules. Use this skill when your task requires highly structured, guided output for UI development, moving beyond simple generation to guided design.

This comprehensive guide helps developers select and implement optimal Vercel architecture blueprints based on project scale and complexity. It covers five validated patterns, ranging from simple static JAMstack sites and robust full-stack SaaS applications, to advanced multi-zone micro-frontend enterprise deployments. Use this resource when planning new deployments, optimizing scaling strategies, or structuring complex monorepos.

This guide provides essential patterns and best practices for building robust automated workflows using no-code platforms like Zapier and Make. Learn the difference between simple triggers and complex multi-step processes, including conditional branching and data transformation. Understand when to use each platform's strengths to ensure reliable business process automation.