3,700 件 ・ 関連度順
概要と使いどころ
Use when working with Cachet — cachet open-source status page management, component tracking, incident reporting, and metric visualization. Covers component group management, incident lifecycle, scheduled maintenance, metric points, and subscriber management. Use when managing self-hosted status pages, creating incidents, tracking component health, or reviewing uptime metrics in Cachet.
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 Cilium — cilium eBPF-based networking and security management for Kubernetes. Covers network policy enforcement, eBPF map inspection, Hubble flow visibility, cluster mesh, endpoint health, service load balancing, and identity management. Use when managing Cilium network policies, debugging connectivity, monitoring traffic flows with Hubble, or configuring cluster mesh.
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 Azure Iot Hub — azure IoT Hub management including device registry, device twins, message routing, endpoints, jobs, and IoT Edge deployments. Covers device connectivity status, message throughput, routing health, quota utilization, and Edge module monitoring.
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 Azure Eventhubs — azure Event Hubs partition analysis, checkpoint management, capture status, consumer group monitoring, and throughput 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 Aws Sam — aWS Serverless Application Model (SAM) management. Covers template validation, local testing, build and packaging, deployment orchestration, stack inspection, log tailing, and API Gateway debugging. Use when building serverless applications with SAM, testing locally, deploying Lambda functions, or troubleshooting API Gateway endpoints.
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 Efs — aWS EFS file system management and performance analysis. Covers file system inventory, mount targets, access points, throughput modes, lifecycle policies, replication configurations, and storage metrics. Use when inspecting EFS file systems, debugging mount issues, reviewing performance settings, or optimizing storage costs.
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 Great Expectations — great Expectations data quality framework analysis. Covers expectation suite management, validation result review, data docs generation, checkpoint execution, datasource configuration, and batch analysis. Use when reviewing data quality results, managing expectation suites, investigating validation failures, or auditing data quality 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。
Roleplay a hostile user to find and triage UX pain points.
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 PowerPoint decks headless with python-pptx.
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 100-point QA validation of all skills + auto-fix + report.
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。
Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
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, read, edit .pptx decks, slides, notes, templates.
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。
Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.
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。
Legacy compatibility entry for prompts or documentation that explicitly name `using-superpowers`. Route new skill-discovery workflows to `using-skills`; do not activate both entrypoints for the same 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。
Run an iterative Stitch website-building loop using `.stitch/next-prompt.md`, SITE.md, DESIGN.md, generated pages, and verification checkpoints.
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 OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.
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。
PHP 8.0+ development — XAMPP, RESTful APIs, PDO/MySQL/MariaDB, and authentication. Use when building PHP backends, creating API endpoints, configuring XAMPP, or integrating PHP with databases.
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。
Write, configure, and deploy Netlify serverless functions in TypeScript, JavaScript, or Go. Use this when adding an API endpoint or backend route, adding a contact form handler, wiring auth or Identity signup/login hooks, building streaming or AI-proxy responses, scheduling cron jobs, running long background jobs (batch processing/scraping), reacting to deploy or form events, setting up rate limiting or region/memory config, or reading environment variables and secrets inside a function. Covers file locations, the Request/Context/Response handler shape, path routing, config options, and local testing with netlify dev.
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 MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
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。
Transform, resize, crop, reformat, and optimize images on demand via Netlify Image CDN's /.netlify/images endpoint. Use when adding responsive images, generating thumbnails, converting formats (avif/webp/png), cropping to aspect ratios, tuning image quality, creating blurred placeholders, allowlisting remote image domains, serving user-uploaded images, or wiring framework image components (Next.js, Astro, Nuxt, Angular, Gatsby) to Netlify. Triggers on tasks like "optimize images", "add image thumbnails", "resize images on the fly", "serve images from an external domain", or "add blur placeholders".
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。
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.
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。
Execute detailed plans in batches with review checkpoints
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。