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Vast.ai CLI to manage GPU instances, volumes, serverless endpoints, and billing.
Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.
Audit and fix Three.js and React Three Fiber apps for frame-loop performance, GPU memory leaks, scene-graph correctness, and visual defects like z-fighting, shadow acne, wrong color space, and broken resize handling. Uses React Doctor as the scanning engine plus a visual rubric checked against rendered output. Use when the user asks to improve, audit, scan, or clean up a Three.js, R3F, react-three-fiber, drei, or WebGL app, or types `/improve-threejs`.
Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.
Installs or verifies AMD Quark and ensures the selected Python environment has an accelerator-matched PyTorch. Applies to "install Quark", "set up Quark", "pip install amd-quark", Quark dependency or import failures, ModuleNotFoundError for quark, and Quark kernel compiler errors. Defaults to GPU and requires explicit confirmation before CPU mode. Covers PyPI universal, exactly matched native, local-wheel, and local-source installs. Does not handle standalone PyTorch requests, ONNX Runtime-only setup, or quantization.
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
GPUI UI framework best practices for building desktop applications. Use when writing GPUI code, creating UI components, handling state/events, async tasks, animations, lists, forms, testing, or working with Zed-style Rust GUI code.
Build and deploy applications on inference.sh. Use when getting started, understanding the platform, creating apps, configuring resources, or needing an overview of inference.sh app development. Supports both Python and Node.js. Triggers: inference.sh app, belt app, inf.yml, inference.py, inference.js, deploy app, app development, build app, create app, GPU app, VRAM, app resources, app secrets, app integrations, multi-function app
Add a new cuTile GPU kernel operator to TileGym. Covers dispatch registration in ops.py, cuTile backend implementation, __init__.py exports, test creation, and benchmark in tests/benchmark. Use when adding, creating, or implementing a new cuTile operator/kernel in TileGym, or when asking how to register a new cuTile op.
Build, scaffold, refactor, debug, review, and visually validate production Rust desktop interfaces with GPUI. Use for setting up a production-ready GPUI starter app; new GPUI apps or components; Entity, Context, action, async, and lifecycle architecture; Apple-style macOS UI, Liquid Glass or translucent materials, motion, gestures, focus, keyboard, accessibility, text input, IME, clipboard, drag and drop, menus, multi-window behavior, and restoration; packaging, CI, performance, and testing work; or translating selected Paper.design frames into maintainable GPUI code with screenshot comparison. Covers published GPUI and pinned Zed revisions, macOS/Linux/Windows boundaries, narrow AppKit interop, and stability audits.
How to build desktop applications with GPUI Kit, the Rust framework published as the gpui-kit crate (GPUI plus gpui_kit::component, gpui_kit::base, gpui_kit::assets). Use when setting up a gpui-kit app, choosing or using a component (Button, Input, Select, Dialog, Sheet, Tabs, Sidebar, List, DataTable, Tree, Chart, etc.), handling component state, theming, or window overlays, and for GPUI mechanics: actions and keybindings, async tasks, contexts, custom elements, entities, events, focus, global state, layout and styling, ElementId, and tests. Holds the normative Coding Guides: read them before any architecture, state-ownership, public API, naming, or testing decision. Pairs with the gpui-kit-design-guides skill for the Design Guides.
Vast.ai API Documentation - Affordable GPU Cloud Marketplace