Skill UI

This comprehensive guide details the end-to-end process of building Retrieval-Augmented Generation (RAG) pipelines using LangChain. It covers critical steps: loading diverse document types (PDF, CSV, TXT), splitting text into manageable chunks, generating high-quality embeddings (OpenAI), storing them in various vector databases (FAISS, Pinecone), and finally constructing the full runnable chain for accurate and context-aware querying.

Automates guidance on train/test split workflows inside ML training, surfacing best-practice patterns, code snippets, and validation tips for PyTorch, TensorFlow, and sklearn projects.

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

The /hub:status command provides a comprehensive overview of an AgentHub session. It displays the current state of agent branches, commit counts, frontier status, and recent board updates. This is useful for monitoring ongoing collaborative AI tasks, allowing users to track progress and identify which agents are active or have completed their tasks.

This guide provides advanced detection methods and threat hunting queries (KQL/SPL) to identify abuse of Azure service principals. It covers techniques such as privilege escalation, credential compromise, unauthorized role assignment, and enumeration, essential for SOC analysts and security teams monitoring Microsoft Entra ID.

Enforces full, unabridged code and document generation by overriding LLM truncation safeguards, forbidding placeholder patterns, and managing token-limit splits so every requested deliverable is returned complete.