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Found 245 Skills
Production-ready Docker and docker-compose setup for Odoo with PostgreSQL, persistent volumes, environment-based configuration, and Nginx reverse proxy.
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.
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
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).
Monorepo management (Nx, Turborepo, pnpm workspaces) — task orchestration, caching, code sharing. Use when setting up monorepo, optimizing builds, or managing multi-package projects.
Create optimized, secure multi-stage Dockerfiles for React applications (Vite, CRA, Next.js static). Use when (1) creating a new Dockerfile for a React project, (2) containerizing a React/Vite application, (3) optimizing an existing React Dockerfile, (4) setting up Docker for React with Nginx, or (5) user mentions React and Docker/container together.
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.
Optimizes CI pipelines for monorepos by detecting affected packages/apps and running only necessary builds and tests. Includes Turborepo/Nx strategies, caching, and parallel execution. Use for "monorepo CI", "affected detection", "incremental builds", or "workspace optimization".
Provision new NixOS servers on Proxmox for this nix flake project. Guides through the complete workflow: creating Proxmox LXC containers, SSH setup, Colmena configuration (init/full pattern), and application deployment with nginx proxy, PostgreSQL, and container images. Use when: (1) Creating a new server/container on Proxmox, (2) Setting up a new NixOS host with Colmena, (3) Deploying applications with nginx SSL proxy and/or PostgreSQL database, (4) Adding new container images to the repository.
Fix errors and warnings in Sphinx docs build
DigitalOcean Droplets, Linux server security, Nginx, and UFW.
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. Workflow 1 auto-discovers input shapes and parameters from user source code. Workflow 2 exports PyTorch models to ONNX. Workflow 3 converts ONNX to .om via ATC with multi-CANN version support. Workflow 4 adapts the user's full inference pipeline (preprocessing + model + postprocessing) to run end-to-end on NPU. Workflow 5 verifies precision between ONNX and OM outputs. Workflow 6 generates a reproducible README. Supports any standard PyTorch/ONNX model. Use when converting, testing, or deploying models on Ascend AI processors.