Skill UI

This skill provides a comprehensive workflow for querying documented, authoritative public APIs across scientific, environmental, and medical domains (e.g., NASA, PubChem, USGS). It ensures that data retrieval is reproducible, auditable, and traceable by defining explicit endpoints, handling pagination, and verifying data provenance, rather than relying on general knowledge inference.

A comprehensive skill designed for finance-related information retrieval. It provides dual search capabilities: real-time web searching across multiple engines (Jina, DDG, Baidu) for general market knowledge, and Retrieval-Augmented Generation (RAG) for querying local, proprietary document stores (e.g., daily news). Ideal for financial analysts needing both broad market context and specific internal data insights.

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.

DeepXiv provides a structured and progressive method for accessing open-access academic literature. Instead of forcing the download of entire papers, it allows users to search by topic or ID and progressively read content—from brief overviews and metadata maps to specific sections. This is ideal for researchers who need layered paper access, trend analysis, or quick, targeted knowledge retrieval without overwhelming context.

A comprehensive guide and implementation pattern for querying, searching, and paginating OneNote content using the Microsoft Graph API. This skill addresses the limitations of the standard OneNote search endpoint, providing robust methods for cross-notebook querying, advanced OData filtering on metadata, and client-side full-text indexing for body content. Ideal for building complex knowledge retrieval systems that handle large, distributed result sets.

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.

This skill guides users through structured, adaptive learning experiences for any topic. It diagnoses the user's current knowledge level and goal to create a personalized study path. It utilizes techniques like worked examples, retrieval questions, and spaced repetition to ensure deep understanding, making it ideal for self-study, skill acquisition, or subject review.

This skill transforms the current workspace into a comprehensive, stateful teaching environment. It guides the user through deep, multi-session learning by structuring content into core components: a defined mission, reference materials, curated resources, structured lessons, and continuous learning records. It employs educational best practices (spaced repetition, retrieval practice) to ensure long-term knowledge retention.

This tool provides a comprehensive security assessment framework for Retrieval-Augmented Generation (RAG) systems and vector databases. It tests critical vulnerabilities such as embedding inversion, cross-tenant data leakage, knowledge-base poisoning, and retrieval manipulation, aligning with OWASP LLM08:2025 standards. It is essential for validating the security posture of multi-tenant and vector-search applications.

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.