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Found 424 Skills
Create, verify by read-back, and manage the lifecycle of Runpod's reusable plumbing (templates, network volumes, container registry credentials, account secrets, the account's SSH public keys, and Instant Clusters), plus staging large model files on a volume and diagnosing a private-image pull failure. Use when the user wants one of those resources created, changed, inspected or removed through the Runpod MCP tools, or asks where a volume should live so a GPU can mount it. Also use for an account cleanup request: only what this conversation created may be removed; everything else is reported for the user to decide.
Diagnoses why ROCm, the HIP SDK, PyTorch, or llama.cpp is broken on an AMD GPU on Linux, Windows, or WSL2, then applies a low-risk fix with consent or hands back the exact next step. Also routes Lemonade, LM Studio, and Ollama problems to the right upstream channel. Use when the user reports that ROCm or HIP "isn't working", torch.cuda.is_available() is False, rocminfo / hipInfo can't see the GPU, or hits hipErrorNoBinaryForGpu, HSA_STATUS_ERROR_INVALID_ISA, "invalid device function", "no kernel image is available", cannot open /dev/kfd, permission denied on /dev/kfd, "ROCk module is NOT loaded", a missing libamdhip64.so / amdhip64_6.dll / hipblas.dll / vcruntime140_1.dll, an HSA_OVERRIDE_GFX_VERSION page fault, an iGPU+dGPU crash, a container that can't see the GPU, or an amdgpu-install / DKMS failure. Backed by the `rocm` CLI (`rocm examine` / `rocm diagnose` / `rocm fix`); this skill is a thin driver over those commands, not a re-implementation.
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Manage GPU compute jobs on the Qizhi (启智) platform using qzcli — a kubectl-style CLI tool. Use when user says "qzcli", "启智平台", "submit job", "stop job", "查计算组", "avail", "list jobs", "batch submit", or needs to manage distributed training jobs on a Qizhi instance.
Recommend and customize Megatron Bridge recipes for a user's model, GPU count, and training goal. Indexes library recipes (pretrain/SFT/PEFT) and performance recipes.
Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translations.
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。
Apple Silicon GPU コンピュート／数値計算リファレンス。MSL の kernel 関数属性・address space・ atomic/SIMD-group 関数、threadgroup sizing・argument buffer・indirect command buffer・ unified memory・GPU counters、MPS/MPSGraph（MPSMatrix, MPSNDArray, MPSNNGraph）、 MLX（mx.array, lazy evaluation, grad/vmap, mx.compile, mlx.nn, mlx.optimizers, distributed）。
Answers questions about what rocprofiler-systems configuration options exist and how to use them — e.g. "what hardware counters are available on my system", "what ROCm APIs can I trace", "what settings control sampling/output", "how do I persist my configuration" — by mapping the question to the right `rocprof-sys-avail` or `rocprof-sys-run --help=<topic>` command. Use when the user wants to discover or explore ROCPROFSYS_* settings, Timemory components, hardware counters, ROCm API tracing domains, or GPU metrics, rather than apply them. Do NOT use this for picking a starting --preset for a workload — that's `choosing-a-profiling-preset`.
GPUI Component project style guide based on gpui-component code patterns. Use when writing new components, reviewing code, or ensuring consistency with existing gpui-component implementations. Covers component structure, trait implementations, naming conventions, and API patterns observed in the actual codebase.
Gpu Resource Optimizer - Auto-activating skill for ML Deployment. Triggers on: gpu resource optimizer, gpu resource optimizer Part of the ML Deployment skill category.
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.