Skill UI

Assesses organizational readiness for migrating cryptographic systems to post-quantum standards (PQC), aligning with NIST guidelines. The tool performs comprehensive cryptographic inventory scanning to identify quantum-vulnerable algorithms (RSA, ECDH, ECDSA) and evaluates the feasibility of implementing hybrid TLS configurations using standards like ML-KEM and ML-DSA. Essential for implementing crypto-agility across enterprise infrastructure.

Guides agents through researching Microsoft technologies via Learn MCP docs, then scaffolds hybrid skills that store essential knowledge locally while enabling dynamic lookups for depth.

PennyLane is a quantum computing library designed for building and training quantum circuits, particularly for quantum machine learning (QML) and quantum chemistry. It offers automatic differentiation capabilities for quantum circuits, making it ideal for developing hybrid quantum-classical models. It is hardware-agnostic, supporting backends from major providers like IBM, Google, and IonQ, and integrates seamlessly with classical ML frameworks such as PyTorch and JAX. Use cases include variational algorithms (VQE, QAOA), quantum neural networks, and molecular simulation.

Master the complexities of building robust Retrieval Augmented Generation (RAG) pipelines using LangChain. This guide addresses common pitfalls in vector search, such as score semantics differences (L2 vs. Cosine), embedding dimension mismatches, and chunking issues for code/Markdown. Learn best practices for selecting between FAISS, Pinecone, Chroma, and PGVector, normalizing scores, and implementing hybrid keyword+vector search for maximum retrieval quality.

Provides comprehensive strategies for migrating embedding models within Qdrant. This guide covers zero-downtime alias swapping, side-by-side A/B testing, and transitioning from dense to hybrid search. It is essential for production environments requiring continuous availability when updating models, dimensions, or switching providers.

A comprehensive guide detailing how to establish robust monitoring for Qdrant. Covers setting up Prometheus metrics scraping, implementing Liveness and Readiness probes for Kubernetes, configuring alerts, and centralizing logs. It provides specific instructions for both self-hosted and Hybrid Cloud environments, ensuring full operational visibility and compliance.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

This guide covers advanced retrieval strategies that significantly boost search quality beyond basic vector similarity search. Techniques include Hybrid Search (combining dense and sparse indexing), Reranking (improving result order), Relevance Feedback (guiding search with iterative user scores), and Maximal Marginal Relevance (MMR) for diversity. Use these methods when standard search results lack relevance, miss critical keywords, or exhibit poor precision.

Methodology for red team testing of on-prem Active Directory, hybrid AD/Entra ID, and ADCS. Covers reconnaissance, credential abuse, privilege escalation, lateral movement, persistence, trust abuse, and Defender for Identity evasion.

Analyze local SEO performance for Google Business Profile, NAP consistency, citations, reviews, local schema, location pages, and multi-location sites. Useful for brick-and-mortar, service area, or hybrid businesses targeting map pack visibility.

This advanced skill acts as a primary research orchestrator. It deterministically analyzes user queries, routing them to specialized modules (e.g., patent landscape, academic literature review, funding intelligence) or executing a comprehensive fallback search workflow. It is designed for complex, multi-faceted research requiring high rigor, structured planning, and verifiable citations.

This skill guides the design of robust, production-grade data extraction pipelines, moving beyond brittle one-off scripts. It supports three modes: API-driven web scraping (Firecrawl), traditional local Python extraction (PDF, CSV), and hybrid workflows. Key features include intelligent routing, mandatory data validation, secure key management, and adherence to scraping best practices.