Skill UI

This workflow guides users through the complete lifecycle of customizing large language models (LLMs). It covers uploading training data, initiating fine-tuning jobs, monitoring training progress, managing model versions, and finally deploying the optimized model to a dedicated, autoscaling inference endpoint using the Together AI API. Ideal for production-level customization.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

This comprehensive checklist guides authors through the final pre-submission review process for demanding academic journals. It ensures the manuscript is scientifically rigorous and complete, focusing on critical areas such as model-driven theoretical advancement, model comparison, data reproducibility (depositing code and verifying fit regeneration), and adherence to open science mandates. Use this guide before uploading to the journal's portal.

This skill helps developers programmatically discover, filter, and inspect the entire OpenMed model registry. It allows users to find the correct model key for specific clinical tasks (e.g., diseases, oncology, PII detection), filter by language, size, or task, and review model metadata before loading. It pairs with `loading-openmed-models`.

This skill manages the loading and efficient caching of OpenMed clinical/biomedical Named Entity Recognition (NER) models. Models are downloaded once from the Hugging Face Hub, enabling full offline operation afterward. It supports model reuse across multiple calls, configuring cache directories, specifying devices (CPU/GPU), and handling various model identifiers (registry keys, full HF IDs, or local paths).

Builds and edits model-driven Power Apps from natural language intent, covering tables, columns, relationships, forms, views, charts, business rules, flows, and generative pages. Runs an interactive multi-turn authoring flow with plan approval and full build, and supports downloading deployed apps back into editable specs.

Learn how to implement data-driven design in Godot using custom Resource classes. This guide covers defining typed data structures (like items, stats, or configs) using `class_name` and `@export`, creating editable `.tres`/`.res` assets, referencing them in scripts, and managing runtime saving and loading via `ResourceLoader` and `ResourceSaver`. Essential for building scalable, data-heavy games without hardcoding values.

Load and animate 3D models in Three.js using GLTFLoader. Supports skinned animations, compressed meshes (DRACO), and KTX2 textures. Use this when importing .gltf or .glb files into a web scene. Includes setting up AnimationMixer for playback and handling compressed asset decoders.

This skill covers setting up a foundational Three.js scene. It includes configuring import maps, initializing the Scene, PerspectiveCamera, and WebGLRenderer trio, establishing a responsive render loop, and adding OrbitControls. Use this when bootstrapping a new Three.js application or debugging rendering issues like blank canvases. It excludes model loading and material lighting setup.

This skill detects supply chain compromise risks in AI agents. It identifies vulnerabilities related to compromised dependencies, malicious plugins, tools, models, and untrusted update sources. It is used when agents dynamically load external resources or pull remote configurations. The skill probes for untrusted loading, dependency integrity, and malicious artifact injection to ensure agent security.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.