1,839 件 ・ 関連度順
概要と使いどころ
Use when working with Banana Dev — banana.dev ML inference platform management covering model inventory, deployment status, API call history, GPU allocation, scaling configuration, build logs, and latency metrics. Use for comprehensive Banana.dev model deployment assessment and inference performance analysis.
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 working with Baseten — baseten ML deployment platform management covering model inventory, deployment status, autoscaling configuration, inference call history, GPU allocation, environment management, and performance metrics. Use for comprehensive Baseten workspace assessment and ML serving 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。
Use when working with Argocd — argoCD GitOps continuous delivery management for Kubernetes. Covers application sync status, deployment health, rollback operations, repository management, cluster registration, RBAC analysis, and application diff review. Use when checking deployment status, investigating sync failures, managing GitOps applications, or auditing ArgoCD configurations.
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 working with Anyscale — anyscale platform management covering Ray clusters, services, model deployments, jobs, and usage analytics. Use when monitoring deployed services, analyzing cluster utilization, reviewing job status, managing model endpoints, or troubleshooting Anyscale deployments.
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 working with Aws Amplify — aWS Amplify application management and deployment analysis. Covers Amplify apps, branches, backend environments, build status, domain associations, and webhook configurations. Use when inspecting Amplify deployments, debugging build failures, reviewing hosting configurations, or auditing Amplify app settings.
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 working with Aws App Runner — aWS App Runner service management and health analysis. Covers service inventory, deployment status, auto-scaling configurations, custom domain associations, VPC connector settings, and observability configurations. Use when inspecting App Runner services, debugging deployment failures, reviewing scaling behavior, or auditing service configurations.
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 for enterprise agent deployment tasks. Candidate synthesized from trend radar; awaiting trial promotion.
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。
Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
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。
NVIDIA RAG Blueprint deployment, configuration, troubleshooting, and shutdown guidance for Docker, Helm, and library-based RAG stacks.
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。
Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
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。
Backend Python v2 — FastAPI, SQLAlchemy, async, Pydantic, deployment
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 en déploiement d'agents IA (containerisation, scaling, monitoring, versioning, A/B testing agents)
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。
Kubernetes deployment workflow for container orchestration, Helm charts, service mesh, and production-ready K8s configurations.
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 asked to report the deployment sentinel value. Provides the sentinel string that proves a vendored skill reached the agent's context.
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 knowledge for deploying to Vercel with Next.js Use when: vercel, deploy, deployment, hosting, production.
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。
Manage Claude Code Viewer (CCV): preview in-progress CCV changes on ccv-preview, register HTML artifacts, download files, or schedule jobs via CCV local API. Use when user asks for CCV preview, ccv-preview, browser visual QA, UI/UX validation, CCV artifacts, CCV file download, or CCV scheduler jobs. Treat preview/プレビュー as the CCV app preview service first; use artifacts only when the user explicitly asks for artifact/dashboard/report HTML. Do NOT use for generic web app previews outside CCV, production deployment, Cloudflare config, or Anthropic remote triggers.
Deploys and configures CrowdStrike Falcon EDR agents across enterprise endpoints to enable real-time threat detection, behavioral analysis, and automated response. Use when onboarding endpoints to EDR coverage, configuring detection policies, or integrating Falcon telemetry with SIEM platforms. Activates for requests involving CrowdStrike deployment, Falcon sensor installation, EDR policy configuration, or endpoint detection and response.
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。
Database migrations, schema evolution, data transformation, and zero-downtime deployments. Use when creating migrations, evolving database schema, transforming data, or planning deployment strategies.
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。
CI/CD pipeline design, GitHub Actions workflows, deployment automation, infrastructure as code, and release management. Use when setting up CI/CD, automating deployments, configuring build pipelines, or managing releases.
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。
Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
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。
Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.
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。
Deploy AI models to mobile applications. TRIGGERS - Use when user needs help with ai-mobile-deployment related 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。