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概要と使いどころ
Use when working with Aws Billing — analyze, break down, and report AWS costs and bills. Covers cost breakdown by service, account, or usage type; monthly/daily billing trends; cost anomaly detection; RI/SP utilization; cost forecasting; credit/discount analysis; and multi-account cost comparison. Uses anti-hallucination rules, mandatory currency/credit detection workflow, and reusable Cost Explorer functions.
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Use when working with Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health monitoring.
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Use when working with Couchbase — couchbase bucket analysis, index advisor, N1QL query performance, XDCR status, and cluster health monitoring.
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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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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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Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
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.
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Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.
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 infrastructure AWS (EC2, S3, Lambda, ECS, RDS, CloudFormation, CDK)
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Edge Computing Specialist IA — Expert en edge computing (Cloudflare Workers, Deno Deploy, Vercel Edge, Bun)
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Expert en optimisation des coûts cloud (AWS cost, reserved instances, spot, rightsizing, FinOps)
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Expert en diagrammes d'architecture avancé (dark theme SVG, cloud infra, system design)
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Expert en infrastructure AWS (EC2, S3, Lambda, ECS, RDS, CloudFormation, cost 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。
AWS, GCP, Azure data platforms, infrastructure as code, and cloud-native data solutions
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Expert in Cloudflare Workers and the Edge Computing ecosystem. Covers Wrangler, KV, D1, Durable Objects, and R2 storage.
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Expert Core Data guidance (iOS/macOS): stack setup, fetch requests & NSFetchedResultsController, saving/merge conflicts, threading & Swift Concurrency, batch operations & persistent history, migrations, performance, and NSPersistentCloudKitContainer/CloudKit sync.
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OCI（Oracle Cloud Infrastructure）のアーキテクチャ構成図を draw.io 形式（.drawio）で生成・編集する。AWS/Azure の構成図を OCI へ変換することもできる。Generate, edit, and convert OCI architecture diagrams in draw.io (.drawio) format using official Oracle icons.
Expert at handling file uploads and cloud storage. Covers S3, Cloudflare R2, presigned URLs, multipart uploads, and image optimization. Knows how to handle large files without blocking. Use when: file upload, S3, R2, presigned URL, multipart.
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Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. I
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bu skill covers implementing automated security scanning for Infrastructure as Code (IaC) templates using tools like Checkov, tfsec, and KICS. It addresses Tespit etme misconfigurations in Terraform, CloudFormation, Kubernetes manifests, and Helm charts before Dağıt:ment, establishing policy-based governance, and integrating IaC scanning into CI/CD pipelines to prevent insecure cloud resource provisioning.
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Integrate Hardware Security Modules (HSMs) using PKCS#11 interface for cryptographic key management, signing operations, and secure key storage with python-pkcs11, AWS CloudHSM, and YubiHSM2.
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Performing authorized privilege escalation assessments in AWS environments to identify IAM misconfigurations that allow users or roles to elevate their permissions using Pacu, CloudFox, Principal Mapper, and manual IAM policy analysis techniques.
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bu skill covers security hardening for serverless compute platforms including AWS Lambda, Azure Functions, and Google Cloud Functions. It addresses least privilege IAM roles, dependency vulnerability scanning, secrets management integration, input validation, function URL authentication, and runtime monitoring to protect against injection attacks, credential theft, and supply chain compromises.
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Performing authorized AWS penetration testing using Pacu, the open-source AWS exploitation framework, to enumerate IAM configurations, discover privilege escalation paths, test credential harvesting, and validate security controls through systematic attack simulation.
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。