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概要と使いどころ
Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, or deciding between Autopilot and Standard modes. Don't use for Workload Identity (use gke-workload-identity), GKE networking (use gke-networking), security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).
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Retrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.
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。
Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
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。
Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.
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。
Generates Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos on Google Cloud from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query 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。
Generates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.
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。
Evolutionary ideation engine — loop-controlled multi-cycle idea generation through phases of dreaming, cross-domain stealing, recombination, fitness testing, selection, and Lamarckian meta-learning, producing ranked novel solution candidates with provenance. USE WHEN ideate, id8, novel ideas, evolve ideas, dream up solutions, innovate, breakthrough ideas, idea evolution, multi-cycle creativity, need genuinely new approaches. NOT FOR quick single-pass brainstorming (use BeCreative).
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。
This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.
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。
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
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 sandbox security policies from plain-language requirements and optional REST API documentation. Produces L4 or fine-grained L7 network policies and ordered network middleware configuration. Use for API access rules, middleware host selection, failure behavior, or built-in and operator-run middleware attachment. Trigger keywords - generate policy, create policy, update policy, change policy, sandbox policy, network policy, API policy, security policy, allow API, restrict API, network middleware, supervisor middleware.
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。
Initialize, adopt, and route work for a separate Obsidian knowledge vault through the portable claude-obsidian core. Use for vault setup, scaffolding, workspace selection, cross-project configuration, or choosing the correct wiki sub-skill. Triggers: /wiki, set up wiki, scaffold vault, create knowledge base, adopt this vault, Obsidian vault, second brain setup, persistent wiki.
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 the CARLA Leaderboard evaluator over a set of routes with your agent — routes/routes-subset/repetitions/track selection, checkpoint and live-results endpoints, resume after a crash, recording, and the debug levels — for leaderboard 1.0, 2.0 or 2.1. Preflights the whole stack (version pairing, maps, ports, agent class, sensor budget) before spending hours, and hands the world back in async mode. Use when the user asks to "run the leaderboard", "evaluate my agent", "run routes_training", "resume an evaluation", or "reproduce my submission locally".
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。
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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 new high-level system architecture, reviewing existing designs, or making architectural decisions. Invoke to create architecture diagrams, write Architecture Decision Records (ADRs), evaluate technology trade-offs, design component interactions, and plan for scalability. Use for system design, architecture review, microservices structuring, ADR authoring, scalability planning, and infrastructure pattern selection — distinct from code-level design patterns or database-only design tasks.
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。
Builds security-focused full-stack web applications by implementing integrated frontend and backend components with layered security at every level. Covers the complete stack from database to UI, enforcing auth, input validation, output encoding, and parameterized queries across all layers. Use when implementing features across frontend and backend, building REST APIs with corresponding UI, connecting frontend components to backend endpoints, creating end-to-end data flows from database to UI, or implementing CRUD operations with UI forms. Distinct from frontend-only, backend-only, or API-only skills in that it simultaneously addresses all three perspectives—Frontend, Backend, and Security—within a single implementation workflow. Invoke for full-stack feature work, web app development, authenticated API routes with views, microservices, real-time features, monorepo architecture, or technology selection decisions.
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。
当用户纠结交朋友/选伴侣/换圈子、感觉被周围人拖累、问「该不该疏远某人」时调用。 核心理念: 五只黑猩猩理论(你的行为由最常接触的5人预测); 同伴是主动选择而非巧合; 只与价值观一致者深交, 远离愤世嫉俗者/愤怒者。 不适用于: 职场必须共事的同事关系(可先设边界)。 Triggers: 朋友/圈子/伴侣/该不该疏远/被拖累/五只黑猩猩/peer/friends/circle/values
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。
当用户困惑「为什么有人攻击我/为什么大家卷来卷去/我怎么不在乎别人眼光/为什么妒忌」时调用。 核心理念: 人生同时存在零和地位游戏、正和财富游戏、单人游戏; 识别你玩的是哪种游戏, 避免地位游戏, 回到内在记分卡。 不适用于: 具体竞争策略制定（如何赢下某个比赛）。 Triggers: 地位/攀比/内卷/妒忌/别人怎么看我/零和/单人游戏/status game/envy
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。
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。
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
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。
Required when terminal_* tools are available. Explains foreground execution, outer collect_result handles, promoted job IDs and their retrieval/cancellation, deadlines, output retention, platform shell selection, and structured editing when enabled.
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。