Browse Skills

Golang Database provides best practices for safe Go database access using database/sql, sqlx, and pgx. Use when writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite.

Golang Dependency Injection explains when and how to apply DI in Go, covering manual constructor injection and libraries like google/wire, uber-go/dig, uber-go/fx, and samber/do. Use it when designing service architecture, refactoring coupled code, or selecting a DI library.

AIQ Deploy installs, runs, validates, and stops a local or self-hosted NVIDIA AI-Q Blueprint server. Use it when asked to deploy, troubleshoot, or shut down AI-Q infrastructure for the aiq-research skill.

AIQ Research runs deep research requests through a reachable NVIDIA AI-Q Blueprint backend. Use it for research-shaped queries like "deep research on..." or "ask AI-Q about...", and to poll, resume, or follow up on AI-Q research reports.

AMC RTSP Calibration records clips from live RTSP camera streams and runs AutoMagicCalib calibration via its REST API. Use it when a user provides RTSP URLs or asks to calibrate live cameras.

AMC Setup Calibration Stack launches the AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when deploying auto calibration, launching AMC, starting MS+UI, or setting up auto-magic-calib with an NGC API key.

Calibrate Sample Dataset runs end-to-end calibration on the bundled sample dataset against a running AutoMagicCalib (AMC) microservice to verify a fresh AMC stack. Use it when the user asks to test the sample dataset, run sample calibration, or verify the AMC install.

CUDA-Q Guide walks users through installing CUDA-Q, writing quantum kernels, GPU-accelerated simulation, connecting to QPU hardware, and exploring built-in quantum applications.

cuDF GPU DataFrames accelerates pandas data processing on NVIDIA GPUs for ETL, joins, groupby, and CSV/Parquet I/O. Use it to migrate pandas workloads to GPU or scale to multi-GPU with dask-cuDF.

CuOpt Developer guides contributions to the NVIDIA cuOpt codebase, covering C++/CUDA, Python, server, and CI changes, build and test workflows, DCO signoff, and code conventions. Use it when modifying cuOpt internals rather than using cuOpt to solve problems.

cuOpt Install helps you install NVIDIA cuOpt for Python, C, or as a REST server via pip, conda, or Docker, and verify the installation. Use it when setting up cuOpt on systems with NVIDIA Compute Capability 7.0+ and CUDA 12.x/13.x.

CuOpt Numerical Optimization API models and solves linear programming (LP), mixed-integer linear programming (MILP), and quadratic programming (QP) problems using NVIDIA cuOpt's GPU-accelerated solver. Use it when the user needs to solve optimization problems with any cuOpt interface (Python, C, CLI).