Skill UI

A comprehensive guide to understanding the complete state (context) available to Large Language Models during inference. Covers core components like System Prompts, Tool Definitions, Message History, and Retrieval Augmented Generation (RAG). Learning to engineer context is crucial for building robust, efficient, and reliable AI agent systems by managing the attention budget and ensuring high-signal information delivery.

This pattern defines a robust, continuous loop structure for autonomous agents. It integrates crucial components like quality gates, evaluation harnesses, and explicit recovery controls. It guides complex workflows by defining decision paths (e.g., PR control, RFC decomposition) and includes strategies to mitigate common failure modes such as infinite loop churn or cost drift, ensuring measurable progress in autonomous systems.

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.

Master advanced patterns for composing LangGraph subgraphs, which is essential for building robust, multi-agent systems like planner-executors or hierarchical teams. This skill addresses critical issues such as shared state contract propagation (P21) and callback scoping (P28). Learn to manage state across independent graph components using sophisticated dispatch patterns: compiled nodes, Send fan-out, and Command bubble-up.

This pattern defines a robust, continuous loop structure for autonomous agents. It integrates crucial components like quality gates, evaluation harnesses, and explicit recovery controls. It guides complex workflows by defining decision paths (e.g., PR control, RFC decomposition) and includes strategies to mitigate common failure modes such as infinite loop churn or cost drift, ensuring measurable progress in autonomous systems.

This skill automates the comprehensive setup and bootstrapping of the ruvector framework. It ensures all necessary optional add-ons—including ONNX, Brain, and LLM components—are installed. Furthermore, it registers the required MCP server and runs a full battery of diagnostics to verify the entire toolchain is functional and ready for immediate use.

A comprehensive guide for designing, auditing, and executing rigorous experimental programs for top-tier computer vision papers (e.g., CVPR). It covers essential scientific components, including establishing fair benchmarks, structured ablation studies, documenting failure modes, quantifying computational efficiency, and demonstrating cross-dataset generalization, ensuring the research is reproducible and robust.

A comprehensive guide for auditing and polishing original long-form manuscripts intended for submission to high-level Pattern Recognition and AI journals. It details mandatory components, formatting standards, anti-plagiarism checks, and ethical guidelines, ensuring the paper meets rigorous peer-review standards for publication.