Skill UI

This tool aggregates breaking news from over 50 authoritative cryptocurrency sources using real-time RSS feeds. Users can filter news by specific coins (e.g., BTC, ETH), categories (DeFi, NFT), time periods, or relevance score. It is ideal for market analysis, monitoring major announcements, or tracking sector developments in depth.

This advanced workflow uses Claygent AI to perform deep research on target companies by scraping websites. It extracts critical data points such as funding rounds, tech stacks, and potential pain points. These insights are then leveraged to generate highly personalized, human-sounding email openers at scale, ensuring high relevance for targeted outreach campaigns.

A sophisticated system for recovering and reconstructing complex project context across distributed, multi-agent AI workflows. It utilizes advanced semantic vector search, relevance filtering, and optimized rehydration patterns to maintain high knowledge fidelity and ensure seamless continuity in long-running, complex AI projects. Essential for advanced RAG implementations.

This guide provides a comprehensive method for optimizing CodeRabbit's review process, focusing on improving speed, relevance, and the signal-to-noise ratio. Learn best practices for managing PR size, selecting the right review profile (e.g., 'assertive' vs. 'chill'), using path instructions to guide feedback, and filtering out low-value files. Ideal for teams looking to reduce review fatigue and ensure consistent code quality checks.

A sophisticated memory management system designed to recover, reconstruct, and maintain project context across complex, distributed, and multi-agent AI workflows. It specializes in advanced semantic memory rehydration, utilizing semantic vector search, relevance ranking, and conflict resolution to ensure seamless continuity and high fidelity of historical knowledge.

Guides multi-agent workflows through an iterative context retrieval loop—dispatching broad queries, evaluating relevance, refining criteria, and looping up to three times so sub-agents gather exactly the files they need without overwhelming limits.

This workflow facilitates comprehensive candidate sourcing using the Juicebox platform. It enables users to execute highly specific people searches by applying robust filters (e.g., location, years of experience, specific skills). Furthermore, the system scores the identified candidates based on relevance and allows bulk export of top profiles to major Applicant Tracking Systems (ATS) like Greenhouse or Lever, streamlining the entire recruiting pipeline.

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.

A comprehensive, model-agnostic toolkit for advanced LLM development. It enables iterative prompt optimization through static analysis (token usage, clarity), measures critical RAG metrics (relevance, faithfulness, coverage), and validates complex agent workflows and configurations. Essential for rigorous LLM quality assurance and cost management.

A comprehensive tool designed to audit the quality, relevance, and redundancy of all Claude skills and commands. It supports two modes: a rapid incremental scan for recently changed skills, and a deep full stocktake using AI judgment and expert checklists to assess viability (Keep, Improve, Update, Retire, Merge).

This tool is designed to help optimize and refine prompts used with Large Language Models (LLMs). By structuring inputs, it ensures that the instructions given to AI systems are clear, comprehensive, and contextually rich, resulting in significantly higher quality and relevance of the generated output. Useful for developers, writers, and researchers.

This skill guides the end-to-end management of the Cyber Threat Intelligence (CTI) lifecycle. It covers defining Priority Intelligence Requirements (PIRs), planning data collection, processing data, performing multi-level analysis (Strategic, Operational, Tactical), disseminating tailored reports, and establishing continuous feedback loops to ensure intelligence relevance and maturity.