Loading...
Loading...
Found 272 Skills
Monorepo tooling, task orchestration, and workspace architecture for JavaScript/TypeScript repositories. Use when setting up Turborepo, Nx, pnpm workspaces, or npm workspaces; designing package boundaries; configuring remote caching; optimizing CI for affected packages; managing versioning with Changesets; or untangling circular dependencies. Activate on "monorepo", "turborepo", "nx", "pnpm workspace", "task pipeline", "remote cache", "changesets", "CODEOWNERS", "circular dependency", "affected packages", "workspace". NOT for git submodules or multi-repo federation strategies, non-JavaScript monorepos (Bazel, Pants, Buck), or single-package repository setup.
Documentation generation patterns for technical specs, API docs, user guides, and knowledge bases using real tools like Sphinx, MkDocs, TypeDoc, and Nextra. Use when creating docs from code, building doc sites, or automating documentation workflows.
Production-ready Docker and docker-compose setup for Odoo with PostgreSQL, persistent volumes, environment-based configuration, and Nginx reverse proxy.
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CLIP to ONNX/TensorRT.
Configures nginx load balancing with upstream servers, health checks, and failover strategies. Use when setting up load balancing, distributing traffic across multiple servers, or configuring upstream backends.
Guidance for setting up legacy Windows VMs (like Windows 3.11) in QEMU with web-based remote access via noVNC. This skill should be used when tasks involve running legacy operating systems in virtual machines, configuring QEMU for older OS images, setting up VNC/noVNC web interfaces, or establishing programmatic keyboard control via QMP. Covers VM boot verification strategies, nginx reverse proxy configuration, and websockify setup.
Expert blueprint for VR platforms (Meta Quest, PSVR, SteamVR, Pico) covering XR toolkit (OpenXR), comfort settings (vignetting, snap turning, teleport), motion controls, hand tracking, and 90+ FPS requirements. Use when targeting VR headsets or implementing immersive 3D experiences. Keywords VR, XR, OpenXR, Meta Quest, motion sickness, comfort, locomotion, XRController3D, foveated rendering.
Expert in monorepo architecture, build systems, and dependency management at scale. Masters Nx, Turborepo, Bazel, and Lerna for efficient multi-project development. Use PROACTIVELY for monorepo setup,
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
Use this skill for project schedule management — tracking modules, milestones, and delivery phases stored in YAML. Invoke whenever the user asks about: project progress or delivery status, module status (planned/in_progress/done/deferred), weekly task breakdown, milestone countdowns, risk analysis, linking OpenSpec changes to modules, or syncing schedule data to Yunxiao. Triggers on: "planning", "schedule", "progress", "milestone", "what's this week", "what's left", "mark as done", "排期", "进度", "本周任务", "里程碑", "模块状态", "还剩多少". Do NOT trigger for: calendar reminders, weekly work reports, or Yunxiao tasks without schedule context.
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
DigitalOcean Droplets, Linux server security, Nginx, and UFW.