Skill UI

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.

This guide outlines best practices for building highly flexible and maintainable React components. It addresses common architectural pitfalls, such as 'boolean prop proliferation,' by recommending advanced patterns like compound components, state lifting, and context providers. Use this when refactoring large component libraries, designing robust APIs, or reviewing component architecture, including considerations for React 19 APIs.

Automates React Context setup within frontend development, guiding users through best practices, production-ready code, and validation for React-related modern web patterns.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

This skill provides implementation patterns for policy guardrails when utilizing Retell AI for voice agents and phone call automation. It is designed to help ensure that AI interactions in a telephony context adhere strictly to predefined business rules and safety policies, preventing unauthorized actions or deviations from the intended conversational flow.

Centralizes pipeline health checks, forecast accuracy tracking, and go-to-market efficiency measurement for SaaS revenue teams, generating text or JSON outputs via CLI scripts to monitor coverage, MAPE, GTM metrics, and risk flags before leadership reviews.

Automates Rosette Text Analytics actions through Composio’s Rube MCP, guiding users to discover current tool schemas, verify connections, and execute workflows with schema-compliant inputs.

RWKV is an RNN+Transformer hybrid architecture with linear-time inference, infinite context, and GPT-style training. It delivers constant-memory, streaming generation on CUDA and CPU, ideal for long-context, memory-constrained deployments.

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.

Implements schema.org JSON-LD across webpages so search engines grasp content intent and serve rich results like FAQs, breadcrumbs, and products, with validation checklists and contextual guidance.

This skill specializes in transforming theoretical threat models, risk assessments, and business context into concrete, actionable security requirements. It is essential for writing detailed security user stories, defining robust acceptance criteria, building comprehensive security test cases, and ensuring compliance requirement mapping. Use it when bridging the gap between identifying a vulnerability and specifying how the system must be secured.

Sentence Transformers provides 5000+ pre-trained models that generate sentence, text, and image embeddings for RAG, semantic search, clustering, and multilingual production workloads without relying on external APIs.