Skill UI

This comprehensive workflow guides the implementation of a complete Retrieval-Augmented Generation (RAG) pipeline using Cohere's core services. It demonstrates the full process: embedding documents, searching for relevant context, reranking results for precision, and finally generating a grounded answer with precise citations using the Chat API. Ideal for building knowledge-intensive, enterprise-grade Q&A systems.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.

This skill wraps the `getCompactContext` functionality to efficiently retrieve a compacted ReasoningBank context, specifically optimized for cost-analysis queries. It generates a detailed report summarizing critical metrics such as tokens saved, memories retrieved, and agentic-flow availability. It is essential for monitoring and optimizing resource usage in complex AI workflows, providing deep visibility into the efficiency and cost of the context retrieval mechanism.

This pattern is designed to solve context limitations in complex multi-agent workflows. Instead of relying on a single, massive context dump, it implements a structured, multi-cycle loop (Dispatch -> Evaluate -> Refine -> Loop). This process iteratively refines the search query by scoring content relevance and identifying missing context gaps, ensuring sub-agents gather the most targeted and highly relevant information needed for tasks like debugging or feature implementation.

This skill covers persistent memory systems for Large Language Model (LLM) conversations. It includes implementing short-term, long-term, and entity-based memory to enhance context retention across sessions. Features include memory consolidation, retrieval strategies, and integrating memory into prompts. Suitable for building conversational AI applications using tools like Mem0, LangChain, or Redis.

Manages agent context by offloading large tool outputs, plans, and sub-agent findings to files for on-demand retrieval, reducing context window bloat and preserving fidelity in long-horizon tasks.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.

Expert in building Retrieval-Augmented Generation systems for LLM applications. Masters embedding models, vector databases, chunking strategies, and retrieval optimization. Capable of designing retrieval pipelines, implementing hybrid search, and optimizing context windows to ensure high-quality generation from retrieved documents.

This skill manages and stores approved team context securely using RuVector isolation. It ensures tenant-local data privacy by excluding sensitive credentials and personal information. During retrieval, it preserves provenance and treats data as untrusted. It prevents memory transfer between teams, maintaining strict security boundaries for AI team operations.

Vexor is a command-line interface tool that utilizes vector embeddings to perform semantic file searches. It is designed to enhance workflow efficiency by allowing users to query files based on meaning rather than keywords, particularly when integrated with AI models like Claude or Codex. Ideal for developers needing context-aware file retrieval within codebases or documentation.