Skill UI

This framework detects NTLM relay attacks across Active Directory environments by correlating Windows Security Event 4624 (LogonType 3). It identifies key indicators such as IP-to-hostname mismatches, Responder/LLMNR poisoning artifacts, and non-enforced SMB/LDAP signing, providing comprehensive threat detection for T1557.001.

Transforms Finnish text by spotting 26 AI markers (12 language-specific, 14 universal) and rewriting them to match native rhythms, so prompts or .md/.txt content read like human-written copy.

A comprehensive guide for developers to build advanced, declarative agents for Microsoft 365 Copilot. This skill details the integration process using a Model Context Protocol (MCP) server, allowing the agent to connect with external systems, select tools, and handle complex authentication (OAuth 2.0/SSO). You will learn to generate key JSON manifests required for deployment.

Deploy and govern MCP declarative agents within the Microsoft 365 admin center, managing their lifecycle, assignments, access controls, and compliance so Copilot delivers organizational capabilities securely.

This tool generates a complete, structured TypeSpec declarative agent definition tailored for Microsoft 365 Copilot. It systematically creates necessary components including detailed role instructions, conversation starters, and a comprehensive list of required capabilities (e.g., WebSearch, OneDrive, Teams, CodeInterpreter). This ensures the agent is fully functional and ready for deployment within the M365 ecosystem.

Generate a TypeSpec API plugin tailored for Microsoft 365 Copilot, wiring REST operations, authentication flows, confirmation cards, and Adaptive Cards into a guided workflow so Copilot can interact with external services securely.

Open Notebook is a self-hosted, open-source alternative to major AI research tools. It allows users to ingest diverse content—including PDFs, videos, and web pages—and organize them into private notebooks. Key features include context-aware AI chat, automated summarization, multi-speaker podcast generation, and full programmatic access via a comprehensive REST API. By running on your own infrastructure, it ensures complete data privacy and supports 16+ AI providers.

This AI-powered skill transforms complex scientific concepts into publication-quality visual diagrams. Utilizing Nano Banana 2 AI for generation and Gemini 3.6 Flash for quality review, it specializes in creating detailed neural network architectures, biological pathways, system flowcharts, and technical schematics. Features smart iterative refinement, adjusting the output until it meets stringent, document-type specific quality thresholds (e.g., journal vs. poster).

PyTorch Geometric (PyG) is the definitive standard library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for handling diverse graph types, including heterogeneous graphs. The library offers over 60 GNN layer implementations (such as GCN, GAT, GraphSAGE), full support for dataset loading (Planetoid, TUDataset, OGB), and necessary data transformations. It is essential for deep learning tasks involving node classification, graph classification, and link prediction.

This skill provides comprehensive guidance on implementing HIPAA-compliant security measures for Abridge clinical AI integrations. It covers critical areas such as enforcing TLS 1.3 encryption in transit, AES-256 encryption at rest, implementing role-based access controls (RBAC), detailed PHI-safe audit logging, and best practices for secrets management, ensuring full compliance with HIPAA regulations.

This tool generates production-grade, spec-compliant agent definition files (.md) adhering to the Anthropic 2026 schema. It supports both the creation of new custom subagents and the validation of existing agent files against marketplace compliance rules. Use it when building complex agent architectures, reviewing agent quality, or ensuring agents meet high industry standards.

This skill guides the secure handling and lifecycle management of large-scale data artifacts, including training datasets, model weights, and checkpoint snapshots, within GPU cloud environments. It provides best practices for data pipelines, covering secure import via Kubernetes PVCs, compliant export, and stringent data validation, ensuring adherence to encryption standards (AES-256) and regulatory requirements (PII masking, RBAC).