62 件 ・ 関連度順
概要と使いどころ
Use when the user wants local voice transcription instead of OpenAI Whisper API. Switches to whisper.cpp running on Apple Silicon. WhatsApp only for now. Requires voice-transcription skill to be applied first.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants local voice transcription instead of OpenAI Whisper API. Switches to whisper.cpp running on Apple Silicon. WhatsApp only for now. Requires voice-transcription skill to be applied first.
日本語の概要は準備中です。原文の説明を表示しています。
Use when designing, implementing, reviewing, debugging, testing, profiling, or porting concurrent, parallel, distributed, high-performance, network dataplane, kernel-bypass, or real-time software, including locks, semaphores, monitors, MPI, CSP rendezvous, DPDK, VPP, eBPF, XDP, AF_XDP, SR-IOV, RSS, RoCEv2/RDMA, RMS/EDF, priority inversion, and environment selection from silicon to edge.
日本語の概要は準備中です。原文の説明を表示しています。
Use Cua's cloud sandboxes via cua.ai. Requires CUA_API_KEY. Cloud sandboxes are isolated VMs run by trycua with billing per usage. Use when the user wants no local Docker/QEMU, wants Windows/macOS guests without Apple Silicon, or explicitly asks for cloud.
日本語の概要は準備中です。原文の説明を表示しています。
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".
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Curate Busabase training examples, create immutable dataset snapshots, run reproducible LoRA or QLoRA fine-tunes on a local Apple Silicon Mac with MLX-LM, compare the untouched baseline and adapter on locked evaluations, and register approved adapters. Use when the user wants to train, fine-tune, evaluate, compare, promote, or manage a local language model with Busabase as the workflow source of truth. Do not use for RAG-only knowledge retrieval, cloud training, or training a foundation model from scratch.
日本語の概要は準備中です。原文の説明を表示しています。
Digital-human implementation and demo skill for choosing, prototyping, and QA-ing low-cost 2D photoreal digital humans and high-control 3D custom digital humans. Use when the user wants a digital-human avatar, AI host, customer-service presenter, multimodal voice-to-lip-sync video stream, vendor comparison for services such as Silicon Intelligence / Tencent Zhiying / ZEGO-like real-time providers, or a UE/Unity 3D digital-human architecture and launch-ready demo.
日本語の概要は準備中です。原文の説明を表示しています。
Download and transcribe videos from YouTube, Bilibili, TikTok and 1000+ platforms. Use when user requests video download, transcription (转录/字幕提取), or converting video to text/markdown. Supports quality selection, audio extraction, playlist downloads, cookie-based authentication, and AI-powered transcription via SiliconFlow API (免费转录).
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Expert in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, LoRA training for character consistency, motion graphics generation, and artist commissioning. Activate on 'AI video production', 'script to video', 'video generation pipeline', 'character consistency', 'LoRA training', 'cloud GPU', 'motion graphics', 'Wan I2V', 'InVideo alternative'. NOT for real-time video editing, video compositing (use DaVinci/Premiere), audio production, or 3D modeling (use Blender/Maya).
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants local voice transcription instead of OpenAI Whisper API. Switches to whisper.cpp running on Apple Silicon. WhatsApp only for now. Requires voice-transcription skill to be applied first.
日本語の概要は準備中です。原文の説明を表示しています。