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Found 267 Skills
Expert blueprint for roguelikes including procedural generation (Walker method, BSP rooms), permadeath with meta-progression (unlock persistence), run state vs meta state separation, seeded RNG (shareable runs), loot/relic systems (hook-based modifiers), and difficulty scaling (floor-based progression). Use for dungeon crawlers, action roguelikes, or roguelites. Trigger keywords: roguelike, procedural_generation, permadeath, meta_progression, seeded_RNG, relic_system, run_state.
Build resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like: lambda durable functions, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, and reliable serverless applications.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Use when a migration is already known to stay on the LangGraph orchestration side, including stages, routing, checkpoints, interrupts, persistence, streaming, and subgraph boundaries.
Implements Syncfusion React TreeGrid for hierarchical data with sorting, filtering, editing, exporting, paging, virtual scrolling, and advanced features. Supports configuration, CRUD, aggregates, templates, state persistence, and performance optimization in React applications.
Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring geo-replication, persistence, VNet/Private Link, CLI/PowerShell automation, or Blob import/export, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure HPC Cache (use azure-hpc-cache), Azure Blob Storage (use azure-blob-storage), Azure Table Storage (use azure-table-storage).
Expert knowledge for Azure Managed Redis development including troubleshooting, best practices, decision making, security, configuration, integrations & coding patterns, and deployment. Use when using Entra auth, geo-replication, persistence, Private Link, or ARM/Bicep deployments for Azure Managed Redis, and other Azure Managed Redis related development tasks. Not for Azure Cache for Redis (use azure-cache-redis), Azure Cosmos DB (use azure-cosmos-db), Azure Table Storage (use azure-table-storage).
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Comprehensive guide for implementing Syncfusion MainFrameBarManager menu and toolbar system in Windows Forms. Use when creating menus, toolbars, command bars, or menu structures. Covers hierarchical menu models, BarItem types, interactive features, keyboard support, MDI integration, and state persistence for building professional menu-driven applications with shortcuts, mnemonics, tooltips, and customizable toolbars.
Core patterns for AI coding agents based on analysis of Claude Code, Codex, Cline, Aider, OpenCode. Triggers when: Building an AI coding agent or assistant, implementing tool-calling loops, managing context windows for LLMs, setting up agent memory or skill systems, or designing multi-provider LLM abstraction. Capabilities: Core agent loop with while(true) and tool execution, context management with pruning and compression and repo maps, tool safety with sandboxing and approval flows and doom loop detection, multi-provider abstraction with unified API for different LLMs, memory systems with project rules and auto-memory and skill loading, session persistence with SQLite vs JSONL patterns.
Scaffolds a complete agent TUI in TypeScript using @openrouter/agent — like create-react-app for terminal agents. Generates a customizable terminal interface with three input styles, four tool display modes, ASCII banners, streaming output, session persistence, and configurable tools. Use when building an agent, creating a TUI, scaffolding an agent project, or building a coding assistant.
Guide to implementing Syncfusion Blazor Sidebar component for responsive navigation sidebars. Use this when building Blazor WebAssembly and .NET 8 Web Apps that need sidebars. Covers setup, open/close control, docking, state persistence, multiple sidebars, and complete styling. Includes ListView and TreeView integration.