'aws' 검색 결과

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review,

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Reviews code for objective correctness, security, and reliability.

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AWS, GCP, Azure data platforms, infrastructure as code, and cloud-native data solutions

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You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.

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Build and publish the Remotion Lambda layer zips (chromium, fonts, emoji-apple, emoji-google, cjk) from this repo to s3://remotionlambda-binaries-$region across all AWS regions. Use when the user wants to publish, release, upload, or ship a new version of the chromium binaries / lambda layers from the lambda-binaries repo.

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Develops full-stack Dart backends using the Serverpod framework with PostgreSQL, Redis, and Docker. Use when building type-safe API endpoints, defining YAML data models, configuring Serverpod auth, writing server-side tests, running database migrations, deploying to Docker/AWS/GCP, or using Serverpod Mini for database-less BFF proxy endpoints.

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Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.

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Build reusable Terraform modules for AWS, Azure, and GCP infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.

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Implement secure secrets management for CI/CD pipelines using Vault, AWS Secrets Manager, or native platform solutions. Use when handling sensitive credentials, rotating secrets, or securing CI/CD environments.

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This skill should be used when the user asks to "perform cloud penetration testing", "assess Azure or AWS or GCP security", "enumerate cloud resources", "exploit cloud misconfiguratio...

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Common AWS CDK patterns and constructs for building cloud infrastructure with TypeScript, Python, or Java. Use when designing reusable CDK stacks and L3 constructs.

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Cloud infrastructure and DevOps workflow covering AWS, Azure, GCP, Kubernetes, Terraform, CI/CD, monitoring, and cloud-native development.

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Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns.

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Universal AI design correction rules — SUPERSEDED. The `impeccable` skill now provides 27 deterministic anti-pattern rules plus a 12-rule LLM critique pass, which are more comprehensive and runtime-enforced via the `impeccable` CLI. This file is kept for backward compatibility — the `impeccable` skill's design laws and anti-patterns take precedence.

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Use when generating cloud architecture diagrams as code — creating, modifying, or reviewing infrastructure diagrams using the Python Diagrams library. Covers AWS, Azure, GCP, Kubernetes, on-premises, and SaaS provider nodes. Generates PNG/SVG architecture diagrams from Python code with Graphviz rendering.

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Use when working with Cloud Custodian — cloud Custodian policy engine management. Covers policy authoring, dry-run execution, resource filtering, action enforcement, multi-cloud support (AWS/Azure/GCP), Lambda deployment, compliance reporting, and policy organization. Use when managing Cloud Custodian policies, auditing cloud resources, enforcing compliance rules, or reviewing policy execution results.

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Use when working with Cloudformation Deep — aWS CloudFormation deep stack management. Covers stack operations, change sets, drift detection, nested stacks, stack sets, resource imports, template analysis, and rollback troubleshooting. Use when managing complex CloudFormation deployments, debugging stack failures, detecting drift, operating stack sets, or analyzing template dependencies.

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Use when working with Cloudformation — aWS CloudFormation stack management. Covers stack lifecycle, change sets, drift detection, template validation, nested stacks, stack sets, and event troubleshooting. Use when managing CloudFormation stacks, investigating deployment failures, detecting drift, or validating templates.

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Use when working with Baselime — baselime serverless observability platform for AWS Lambda, CloudFlare Workers, and Vercel functions. Covers log and trace querying, alert management, service discovery, and performance analysis of serverless workloads. Use when investigating serverless function performance, querying Baselime events, managing alerts, or analyzing cold start and duration metrics.

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Use when working with Prowler — prowler cloud security assessment tool. Covers AWS, Azure, and GCP security posture assessment, CIS benchmark evaluation, compliance frameworks (PCI-DSS, HIPAA, SOC2), multi-account scanning, and remediation guidance. Use when assessing cloud security posture, running compliance audits, or investigating security findings across cloud providers.

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X (Twitter) Scraper API and X API Alternative instructions for Xquik scraping and connected X account action planning. Also use for Xquik Radar or Xquik support tickets only when the user names that feature. Do not load or use this Skill for official X developer setup unless the user compares it with Xquik. Trigger when an X or Twitter task asks about posts, replies, likes, follows, messages, search, users, timelines, followers, exports, giveaways, draws, monitors, Xquik webhooks, MCP setup, SDKs, or API comparisons. Start read-only. Require confirmation for write plans, private reads, monitors, webhooks, support access, and metered bulk jobs. Not affiliated with X Corp.

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AI-powered codebase security scanner that reasons about code like a security researcher — tracing data flows, understanding component interactions, and catching vulnerabilities that pattern-matching tools miss. Use this skill when asked to scan code for security vulnerabilities, find bugs, check for SQL injection, XSS, command injection, exposed API keys, hardcoded secrets, insecure dependencies, access control issues, or any request like "is my code secure?", "review for security issues", "audit this codebase", or "check for vulnerabilities". Covers injection flaws, authentication and access control bugs, secrets exposure, weak cryptography, insecure dependencies, and business logic issues across JavaScript, TypeScript, Python, Java, PHP, Go, Ruby, and Rust.

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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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Apply critical rationalist epistemology (Popper, Deutsch) to evaluate reasoning, identify errors, and refine understanding. Use when the user explicitly requests help with reasoning - phrases like "help me think this through", "does this make sense", "any flaws in this", "what am I missing", "critique this", "is this reasoning sound", "stress test this idea", "devil's advocate", or any request to evaluate arguments, identify logical problems, or improve thinking. Also use when errors in reasoning are significant enough to materially affect the user's goals, even if not explicitly requested.

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