Skill UI

This skill provides guidance and patterns for generating pairwise test cases. Pairwise testing is an advanced testing technique used in software quality assurance (QA) to ensure that every combination of two input parameters is tested. It is highly effective for complex systems with multiple interacting variables, helping developers improve test coverage efficiently while minimizing the overall number of required test cases.

Expert in designing and implementing advanced Retrieval-Augmented Generation (RAG) pipelines. This role focuses on optimizing the entire information retrieval process—from document chunking and embedding model selection to vector database architectures and hybrid search strategies. The goal is to bridge the gap between raw knowledge documents and accurate LLM understanding, drastically reducing hallucination and enhancing context relevance for enterprise applications.

Orchestrates Sage ERP tasks through Composio’s Sage toolkit via Rube MCP, enforcing tool discovery, connection checks, schema compliance, and structured multi-step execution with remembered sessions.

Configure GDPR-compliant data handling, PII scrubbing, and data retention policies within Sentry. This skill provides detailed instructions on implementing client-side `beforeSend` hooks and server-side scrubbing rules to securely redact sensitive data (passwords, emails, IP addresses, CC numbers). Essential for achieving compliance with regulations like GDPR, HIPAA, and meeting SOC 2 audit controls.

A comprehensive guide providing expert patterns for building modern, robust Shopify applications. This skill covers the entire development lifecycle, including React Router app setup, handling complex webhooks, embedding the app within the Shopify Admin using App Bridge, and interacting with the GraphQL Admin API. Ideal for developers building e-commerce integrations.

Expert capability for designing and implementing complex semantic search systems using vector databases. Proficient in embedding strategies, index optimization (HNSW, IVF), and advanced retrieval methods like RAG and hybrid search. Masters major platforms including Pinecone, Weaviate, Qdrant, Milvus, and pgvector for building sophisticated recommendation and similarity search applications.

Automatically generates two complementary onboarding documents for any codebase: a Principal-level guide focusing on design philosophy, technical debt, and architectural 'why'; and a Zero-to-Hero guide providing step-by-step setup, development workflows, and practical 'how-to' instructions. Ideal for new team members or documenting complex systems.

Guides symbolic mathematics work in Python, covering algebraic simplification, calculus, equation solving, linear algebra, physics, number theory, geometry, and code generation to deliver exact symbolic results instead of numeric approximations.

This skill guides developers on using the `@expo/ui/swift-ui` package to integrate native SwiftUI views and modifiers into Expo applications. It is essential for building authentic, platform-specific iOS user interfaces within a cross-platform React Native context, especially when targeting Expo SDK 55. Learn how to wrap SwiftUI trees and embed React Native components.

This skill fetches unread GitHub Pull Requests (PRs) that require review from a specified team or were opened by a team member. It is ideal for users who need to check their team's review queue, find PRs needing attention, or automate the process of reviewing open code submissions without manually browsing GitHub notifications. Requires GitHub CLI authentication.

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.

This tool allows users to explicitly save critical, project-specific facts, conventions, or debugging insights to a dedicated auto-memory file. Use it when the knowledge is too important or specialized to rely on the AI's automatic context capture. It ensures persistent recall of architectural decisions, tool gotchas, or team preferences across multiple sessions and projects.