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Write and optimize Kusto Query Language (KQL) for Azure Data Explorer, Log Analytics, and monitoring workflows. Use this skill when users ask for KQL queries, telemetry analysis, incident investigation, or query optimization.

Provides guidance and best practices for working with curl, covering core concepts, common use cases, troubleshooting, and security considerations.

Write correct, efficient Kusto Query Language queries with coverage of syntax, joins, dynamic types, datetime pitfalls, regex, serialization, memory management, and advanced functions.

Store and process large N-dimensional arrays with chunking, compression, and parallel I/O, integrating with NumPy, Dask, and Xarray for cloud-native scientific computing.

Expert knowledge for Azure Firewall development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing Azure Firewall SKUs, policies/rules, TLS inspection, DNAT/SNAT, logging/Sentinel, or hub‑and‑spoke, and other Azure Firewall related development tasks. Not for Azure Firewall Manager (use azure-firewall-manager), Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager), Azure Web Application Firewall (use azure-web-application-firewall).

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

Develop SurrealDB WASM extensions in Rust, compile them to WebAssembly, and deploy them as database modules callable from SurrealQL.