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概要と使いどころ
Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.
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。
This skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great.
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。
Imports a pedestrian into CARLA on UE 5.8 as a spawnable, animating walker — imports the skinned FBX bound to CARLA's shared pedestrian skeleton, duplicates a donor walker blueprint and repoints it at the new mesh, and registers it in WalkerParameters.json as walker.pedestrian.<id>. Can also export a shipped walker to FBX, which is how you obtain a rig-conforming mesh to start from. Use when the user asks to "import a walker/pedestrian", "add a custom character", "clone a walker", or has a walker that spawns but stands still.
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。
Imports an FBX vehicle into a CARLA source build as a spawnable vehicle.<make>.<model> — checks the FBX carries CARLA's 4-wheel bone rig, imports the mesh, then assembles the vehicle through CarlaTools' VehicleAuthoringLibrary (physics asset with kinematic wheel bodies, retargeted anim blueprint, four configured wheel blueprints, the vehicle blueprint), registers it in VehicleFactory through CARLA's own add_vehicle_to_vehicle_factory.py, and verifies it spawns, drives and steers. Use when the user asks to "add a car/truck/van to CARLA", "import a vehicle FBX", or needs a custom drivable vehicle spawnable from the Python API.
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。
Records a running CARLA simulation to a .log file via start_recorder/stop_recorder for later deterministic replay — capturing actor spawns, transforms, traffic-light states and animations (optionally velocities/controls). Use when the user asks to "record the simulation", "start/stop the recorder", "capture this run", or "save a scenario to replay later". Does NOT capture sensor images.
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。
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
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。
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
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。
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
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。
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
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。
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
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。
Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
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。
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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 building .NET 8 applications with minimal APIs, clean architecture, or cloud-native microservices. Invoke for Entity Framework Core, CQRS with MediatR, JWT authentication, AOT compilation.
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。
Create precise SVG technical diagrams, export PNG or offline HTML, and animate supported semantic SVGs to GIF. Use for architecture, UML, agent, cloud or workflow diagrams; not photos, raster art or statistical charts.
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。
Independent verification agent for loop-produced changes. Finds reasons to reject. Runs tests. Confirms diff scope. Use after minimal-fix or any implementer sub-agent — never in the same role as the implementer.
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。
Produce the smallest possible code change that fixes a specific, well-scoped issue (CI failure, reviewer comment, typo). Use only when the fix target is explicit. Never refactor unrelated code.
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。
Generate self-contained HTML visual explanations for systems, code changes, plans, data, and technical concepts. Use for diagrams, architecture overviews, diff or plan reviews, project recaps, comparison tables, slide decks, animated explainers, narrated MP4 videos, and other visual explanations.
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。
Add a new leave to the leave register with the minimum information needed to start tracking deadlines. Use when an employee goes on leave and you want the tracker to watch designation, certification, and exhaustion clocks from day one.
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。
EU AI Act per-system inventory — track each AI system's role (provider, deployer, importer, distributor, authorized representative, product manufacturer) and risk tier (prohibited, high-risk, limited, minimal, GPAI, GPAI+systemic). Role and tier are assessed per system, not per company. Use when the user says "ai inventory", "add an ai system", "what systems do we have", "classify this ai system", "eu ai act register", or "ai system registry".
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。
Generate developer handoff specs from a design. Use when a design is ready for engineering and needs a spec sheet covering layout, design tokens, component props, interaction states, responsive breakpoints, edge cases, and animation details.
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。
像素美术 + kinetic 字体的解说帧, 纯 CSS 循环, 可录视频
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。
8 页单色背景, 160px display + 4px accent + Mono 箭头列表
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。
Record and cut a short showcase video of a game mod, or a compilation of clips, ready to post on X/YouTube. Records the game window with only the game's own audio, picks moments from contact sheets, and edits with one-line animated titles, creator credits, transitions, music, watermark and fade-out via an EDL JSON. Use when the user wants a demo video, trailer, clip or montage of a mod, or wants to combine existing clips into a styled video.
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。
Turn generated or hand-made art into exactly what a game engine loads. Covers sprite cutout, trim and nearest-neighbour fitting to frame sizes, pixelation to a palette, sprite sheets and strips, player/team-colour masks, seamless textures, and rendering a 3D model into sprite frames from the game's own camera (isometric/RTS 8 or 16 headings, side view, top-down) with Blender. Use after generating assets, or when the user asks to make sprites, sprite sheets, animation frames, isometric unit sprites or icons for a game.
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。