322 件 ・ 関連度順
概要と使いどころ
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
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 fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.
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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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。
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.
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。
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
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。
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
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。
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
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。
Guided customization of your product counsel practice profile — change one thing without re-running the whole cold-start interview. Adjust risk calibration, escalation contacts, launch review framework, marketing claims posture, or matter workspace paths. Use when the user says "change my [thing]", "update my profile", "edit my framework", "retune my calibration", or "customize".
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。
Help a clinic supervisor author a practice-area guide that configures how student-facing skills behave — intake questions, pedagogy posture (assist / guide / teach), review gates, cross-plugin checks, and local rules. Use when a supervising attorney wants to build or revise a per-practice-area guide, tune how the clinic skills behave for their clinic type, or set their teaching philosophy as plugin configuration.
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。
Guided customization of your commercial contracts practice profile — change one thing without re-running the whole cold-start interview. Adjust risk posture, escalation contacts, playbook positions, NDA triage preferences, house style, review preferences, or matter workspace paths. Use when the user says "change my [thing]", "update my profile", "edit my playbook", "tune my config", or "customize".
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。
Guided customization of your AI governance practice profile — change one thing without re-running the whole cold-start interview. Adjust risk posture, escalation contacts, use-case registry entries, vendor AI positions, AI policy commitments, impact-assessment house style, or matter workspace paths. Use when the user says "change my [thing]", "update my profile", "edit my config", "tune my playbook", or "customize".
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。
Optimize CircleCI configuration for speed, reliability, and maintainability. Use when users ask to improve `.circleci/config.yml`, reduce CI runtime, tune caching/workspaces/parallelism, remove pipeline waste, or fix flaky pipeline behavior caused by configuration choices.
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 and tune animated liquid-metal WebGL borders with the React `metal-fx` package. Use when buttons, icon controls, chips, tabs, cards, or selected surfaces need a metallic active, selected, hover, focus, or premium border; when implementing the MetalFx component from metal.jakubantalik.com; or when troubleshooting its presets, themes, strength, glow, reflections, sizing, radius, animation, accessibility, SSR, or performance.
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, tune, or test a Three.js game for mobile web. Use for touch movement, action controls, target selection, touch inventory, safe areas, portrait/landscape layouts, responsive HUD, battery/performance budgets, and real mobile browser QA.
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。
Design, implement, tune, or test Three.js action-game encounters. Use for arena layout, enemy composition, spawn pacing, objectives, boss phases, reward cadence, encounter fixtures, and difficulty validation.
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, debug, balance, or test combat enemy AI for playable action games. Use for aggro, target selection, navigation, spacing, attack choices, telegraphs, retreats, boss behavior, behavior-state machines, and deterministic AI regression tests.
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。
Design, implement, tune, or test readable tactical action combat for web games. Use for attack timing, guard and dodge windows, hit contact, posture, lock-on, weapons, boss phases, combat feedback, and deterministic combat tests.
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。
Implement, tune, debug, or validate soft wall-aware fog of war and gameplay perception in Three.js action games. Use for orthographic or isometric visibility masks, obstacle-aware line of sight, player and enemy vision ranges, hidden-enemy targeting rules, fog shader artifacts such as spokes or seams, mobile ray budgets, lifecycle and menu-state integration, and deterministic fog-of-war tests.
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。
Implement or tune Three.js game cameras. Use for isometric framing, follow behavior, orbit/zoom limits, occlusion, lock-on, camera shake, touch camera controls, and camera regression tests.
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。
Polish and refine existing motion against the transitions.dev motion-token scale — duration, distance, scale, blur, and easing — plus the rules for WHEN each token applies (open/close asymmetry, hover-in vs hover-out, stagger offsets, and intent delays). An add-on to the transitions-dev skill, focused on tuning what already animates rather than adding new transitions. Use when the user asks to "polish my transitions", "refine the motion", "tune the timing / easing", "make the animation feel better / less janky", "tighten the durations", "fix the stagger", "align to the motion tokens", "audit the motion", "review my animations", "scan for ad-hoc transitions", "tokenize my animations", or runs the commands transitions review or transitions polish. Also drives the Refine panel's Small refinement feature. Triggers on "motion polish", "transition polish", "refine motion", "timing feels off", "too slow / too fast", "stagger", "delay", "open close timing", "hover in out".
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。
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For probabilistic single-cell models (scVI etc.), use the scvi-tools library.
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。
Translate natural language to SQL, optimize query performance, and interpret EXPLAIN plans for SQLite and PostgreSQL. Triggered when users ask to convert questions into SQL, improve slow queries, tune indexes, analyze execution plans, or mention keywords like NL2SQL, query tuning, or full table scan.
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 a photo into a specific AI look (new hairstyle, collectible figurine, royal pet portrait, wedding photo, 90s yearbook, sticker, HD restore) or a photo into a dance video, using Kavel (www.kavel.ai). Use when the user wants to restyle/transform a photo, asks "how do I make an AI X from my photo", or wants a photorealistic image edit that keeps the same face. Gives the right tool, a model-tuned prompt, and the generate link.
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 standard or fixed-channel softmax finetuning of NV-Segment-CT VISTA3D on CT NIfTI image/label datasets, with optional MONAI-native MLflow tracking and checkpoint evidence. Uses softmax for predefined, mutually exclusive classes; keeps the standard workflow when point prompts or runtime-variable classes are needed. Not for clinical validation.
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。