62 skills · sorted by relevance
Build and manage Linux containers on Apple silicon Macs with Apple's container CLI. Run each container in its own lightweight virtual machine, and get guidance on images, networking, storage, and translating familiar Docker workflows to this tool.
Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.
Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.
Convert language models to GGUF and reduce their size through quantization, which stores model weights at lower precision. Guides cover compression choices, local inference on CPUs and Apple Silicon, and integration with tools such as Ollama.
Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.
Compress screen recordings on Apple Silicon Macs using dedicated video hardware. Choose quality settings for sharing or archiving, inspect video properties, and check the resulting file size and storage savings.
Compress screen recordings on Apple Silicon Macs using dedicated HEVC video encoding hardware. Choose quality presets for sharing or archiving, inspect source files, and review file size savings through command output.
Apple's open-source `container` CLI to build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS — no Docker daemon required. Use when the user mentions the `container` CLI, "apple container", running or building containers on macOS without Docker/Podman, `container run`, `container build`, `container images`, `container system start`, pushing/pulling images to a registry, or container networking on macOS. This is Apple's `container` tool specifically (repo `apple/container`), NOT Docker, Podman, containerd, nerdctl, or Kubernetes — the CLI is Docker-like but is a different tool.
The English summary is being prepared. Showing the original description.
Build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS using Apple's open-source container CLI, no Docker daemon required.
The English summary is being prepared. Showing the original description.
Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp. Use when choosing an Apple-local model runtime, building an Apple Intelligence chatbot or tool-calling feature, running an LLM on Apple Silicon, converting or compressing a Python model for Core ML, or comparing on-device inference backends. For Swift Core ML loading and prediction code, use the coreml skill.
The English summary is being prepared. Showing the original description.
Executa inferência de LLM em CPU, Apple Silicon e GPUs consumer sem hardware NVIDIA. Use para edge deployment, Macs M1/M2/M3, GPUs AMD/Intel ou quando CUDA não está disponível. Suporta quantização GGUF (1,5-8 bits) para redução de memória e aceleração de 4-10× vs PyTorch em CPU.
The English summary is being prepared. Showing the original description.
Formato GGUF e quantização llama.cpp para inferência eficiente em CPU/GPU. Use ao implantar modelos em hardware de consumidor, Apple Silicon, ou quando precisar de quantização flexível de 2-8 bits sem requisitos de GPU.
The English summary is being prepared. Showing the original description.
Build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS using Apple's open-source container CLI, no Docker daemon required.
The English summary is being prepared. Showing the original description.
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
The English summary is being prepared. Showing the original description.
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
The English summary is being prepared. Showing the original description.
Apple Silicon GPU コンピュート／数値計算リファレンス。MSL (address space, atomic, SIMD-group, TensorOps, cooperative_tensor, matmul2d), threadgroup sizing, argument buffer, indirect command buffer, unified memory, GPU counters, MTLTensor, MTL4MachineLearningCommandEncoder, MPS / MPSGraph / MPSMatrix / MPSNDArray / MPSNNGraph, MLX (mx.array, lazy evaluation, grad / vmap, mx.compile, mx.fft, export_function, mlx.nn, distributed)。
The English summary is being prepared. Showing the original description.
YomiTokuでPDF書籍をOCR処理しMarkdownに変換、目次解析・章分割・テーブル抽出まで 一貫実行するパイプラインスキル。4モード（通常/figure/デュアル/辞書）対応。 Apple Silicon MPS最適化。並列バッチ処理で大規模PDFも高速処理。 「書籍OCR」「PDF→Markdown」「章分割」「YomiToku」「本のデジタル化」 「OCRして」「この本をテキスト化」「PDFをMarkdownに」などのキーワードで使用。 PDFや書籍のOCR処理を依頼されたら、たとえユーザーが明示的にスキル名を言わなくても 積極的にこのスキルを使うこと。
The English summary is being prepared. Showing the original description.
本地录音转文字工具。当用户发送已有录音、音频或视频文件，并希望把语音转成 Markdown 文稿和 SRT 字幕时使用。Apple Silicon 优先用 MLX/Apple GPU 和 whisper-large-v3-turbo-q4，本地转写，不生成 txt/json/vtt，不用于现场临时录音，也不默认调用云端语音识别服务。
The English summary is being prepared. Showing the original description.
Use when tuning or profiling native code on Apple M-series Macs: unified memory, 16 KiB pages, Accelerate and Metal for matrix work, xctrace and leaks, Rosetta 2, or sysctl hardware queries.
The English summary is being prepared. Showing the original description.
Apple's native `container` CLI for running Linux containers as lightweight per-container VMs on macOS 26 Apple Silicon. Load when creating, configuring, managing, backing up, sharing, or deleting containers, images, volumes, or networks with `container`; when `container` or `container system start` fails; or when choosing between `container` and Docker/Podman/Colima on a Mac.
The English summary is being prepared. Showing the original description.
Run Cua sandboxes locally - no API key required. Covers Docker (XFCE/Kasm) for Linux containers, QEMU for full VMs, Lume for macOS guests on Apple Silicon, and Tart for Apple VZ VMs. Use when the user wants a local sandbox, wants to keep things off the cloud, or asks "spin up an Ubuntu desktop", "open a sandbox VM", "let me try this safely first".
The English summary is being prepared. Showing the original description.
Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.
The English summary is being prepared. Showing the original description.
Set up and optimize large language model inference, or response generation, on NVIDIA GPUs. Use quantization, batch processing, and multiple GPUs to improve serving performance, and expose models through a chat completion API.
Analyze and modify refusal behavior in open-weight language models without retraining. Use OBLITERATUS to select methods suited to available hardware, compare approaches, and evaluate changes in refusal rates and response quality.