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Found 10 Skills
Early-access skill for Mimiry's softlaunch GPU compute platform. Use this skill whenever the user wants to run a GPU job, start a compute session, train a model, launch a container on a GPU, check their balance, manage running sessions, or build a compute job script on the Mimiry softlaunch environment. Also triggers when the user mentions Mimiry softlaunch, Mimiry compute, GPU sessions, SSH-ing into a session, or asks to "run this on a GPU" or "launch a training job". Covers both quick one-liners and interactive job-building workflows. This is the softlaunch (early beta) version — the API and features may change.
Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.
Executes Python scripts, tests, or benchmarks on a provisioned remote cluster (GPU or TPU) using SkyPilot. Use this skill when the user asks to run code on GPU, TPU, or any "remote" cluster.
DigitalOcean compute services covering Droplets, App Platform, Functions, Kubernetes (DOKS), GPU Droplets, and Bare Metal GPUs. Use when selecting or provisioning compute for applications, containers, or serverless workloads.
CUDA/GPU computing guardrails, patterns, and best practices for AI-assisted development. Use when working with CUDA files (.cu, .cuh), or when the user mentions CUDA/GPU programming. Provides kernel design patterns, memory hierarchy guidelines, and occupancy optimization specific to this project's coding standards.
AMD ROCm / HIP GPU コンピューティングリファレンス。 hipMalloc, hipLaunchKernelGGL, HIP Graph, HIPRTC, hipify, rocBLAS, MIOpen, RCCL, rocprofv3, amd-smi, rocminfo, CDNA1-4 / RDNA1-4, MI300 / MI350, gfx942, Matrix Core, wave32/64。
Representation-first Three.js WebGPU/TSL particles, ribbon or history trails, and effects. Use for analytic or recurrent particle motion, stable-slot or scan-compacted GPU pools, flow-conforming shells and wakes, dissolving debris, or effect-specific HDR and depth integration.
Use Google Colab CLI from a terminal agent to provision CPU, GPU, or TPU runtimes, run Python scripts and notebooks remotely, install packages, move files, export logs, diagnose authentication or allocation failures, and clean up sessions. Use for Colab runtime work, remote GPU/TPU execution, QLoRA or fine-tuning on Colab, notebook execution, Colab CLI troubleshooting, or `colab` command guidance.