「endpoint」の検索結果

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.

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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 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.

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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.

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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.

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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.

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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.

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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".

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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.

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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.

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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.

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Templates and structural patterns for API docs, feature docs, config guides, and REST endpoint documentation. Use when structuring docs, applying Markdown templates, or standardizing doc formats.

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Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices

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When addressing slow application endpoints, high database CPU usage, or standardizing data access patterns.

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Verify device legitimacy and app integrity using DeviceCheck (DCDevice per-device bits) and App Attest (DCAppAttestService key generation, attestation, and assertion flows). Use when implementing fraud prevention, detecting compromised devices, validating app authenticity with Apple's servers, protecting sensitive API endpoints with attested requests, or adding device verification to a backend architecture.

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Create Convex queries, mutations, and actions with proper validation, authentication, and error handling. Use when implementing new API endpoints.

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GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.

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Use this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provides comprehensive security checklist and patterns.

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 comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and 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。

Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE).

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Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team operations.

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 comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and 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。

Kubernetes NetworkPolicies provide pod-level network segmentation by defining ingress and egress rules that control traffic flow between pods, namespaces, and external endpoints. Combined with CNI plu

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Securing API Gateway endpoints with AWS WAF by configuring managed rule groups for OWASP Top 10 protection, creating custom rate limiting rules, implementing bot control, setting up IP reputation filtering, and monitoring WAF metrics for security effectiveness.

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。