'iac' 검색 결과

MediaClaw is an AI video content factory for teams that need brand video production and repurposing at scale. It turns one source video into localized clips, ad creatives, captioned shorts, voiceover variants, and platform-ready exports for TikTok, Reels, Shorts, Douyin, and Xiaohongshu. AI 视频工厂、品牌视频替换、批量视频生产、短视频自动化。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

InfraGlue (ig) — CLI tool for managing Infrastructure as Code (IaC) monorepos. Orchestrates Terraform and Pulumi workspaces with dependency resolution, output injection, environment management, drift detection, and state reconciliation. Use this skill when working with ig.yaml configs, ig CLI commands, or multi-workspace Terraform/Pulumi infrastructure projects. Keywords: terraform, pulumi, infrastructure, IaC, monorepo, workspace, ig, infraglue, drift, plan, apply, destroy, import, export, refresh, environment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build and refactor AWS infrastructure with TypeScript or Python using AWS CDK, a toolkit for defining cloud resources in code. Get guidance on resource selection, Lambda packaging, stack organization, and checks before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating AWS CDK stacks and reusable components
• Refactoring existing infrastructure code
• Adding Lambda functions to a CDK application

Manage AWS infrastructure as TypeScript code with SST v4 and Pulumi. Guides resource creation, connections between services, infrastructure tests, deployment checks, troubleshooting, and resource migrations while accounting for existing project conventions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding AWS resources to an SST v4 application
• Connecting resources and reviewing access permissions
• Adding infrastructure tests and reviewing deployment changes

Turn existing Azure infrastructure into Terraform code using Azure Verified Modules, reusable infrastructure components. Discover resources, document dependencies, derive import addresses from module source, and check plans for unwanted changes to the live environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Bring existing Azure resources under Terraform management
• Generate code for a subscription or resource group
• Document resource dependencies and architecture

Set up a Python project with AWS CDK, a toolkit for defining cloud infrastructure in code. Follow guidance on prerequisites, AWS credentials, virtual environments, dependencies, template generation, deployment preparation, and reviewing changes before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a new Python project for AWS CDK
• Preparing a virtual environment and dependencies
• Generating templates and reviewing deployment changes

Design and review infrastructure managed with Terraform or OpenTofu. Get guidance on reusable modules, testing choices, automated deployment workflows, and security checks, with approaches suited to different software versions and the cost of running tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing reusable infrastructure modules
• Choosing infrastructure testing methods
• Setting up automated infrastructure deployment

Audit Terraform infrastructure definitions and planned changes for excessive permissions, public exposure, and missing encryption. Use security scanners and custom rules to produce actionable findings and add automated checks before cloud infrastructure is deployed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing Terraform modules and plans before deployment
• Adding security checks to Terraform CI/CD pipelines
• Enforcing organization-specific infrastructure rules

Build fuzzy search that filters candidates and sorts them by relevance. Use saved match scores, search across multiple object fields, adjust acceptance thresholds, and connect ranking results to TanStack Table filtering and sorting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Sort search results by relevance
• Search names and email addresses together
• Adjust which fuzzy matches are accepted

Plan Azure infrastructure around application needs, scale, and budget. Generate Bicep templates, files that define cloud resources as code, then review estimated costs, configure deployment pipelines, and check security settings before production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning Azure infrastructure for a web app
• Choosing services for microservices or event-driven processing
• Generating Bicep templates and deployment pipelines

Plan and build AI agents on Google Cloud, from gathering requirements to choosing a single-agent or multi-agent design. Produce architecture diagrams, deployment code and instructions, and validation plans tailored to the workload.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan an AI agent architecture on Google Cloud
• Choose between single-agent and multi-agent designs
• Prepare deployment code and step-by-step instructions

Design Google Cloud infrastructure with Terraform, validate it locally, and assess the planned changes for security, cost, and reliability. The skill then imports templates into Application Design Center, deploys them, investigates failures, and checks the deployed services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing and locally validating Terraform for GCP
• Assessing infrastructure plans before deployment
• Importing and deploying Application Design Center templates

Build infrastructure as code with Terraform across AWS, Azure, and GCP. Create reusable modules, manage state records that track resources, configure environments and providers, and validate proposed changes before applying an explicitly approved plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating reusable Terraform modules
• Migrating state storage or importing existing resources
• Managing settings across development and production

Create editable draw.io diagrams from descriptions, code, infrastructure settings, or database schemas. Update existing diagrams while preserving manual layout, check their structure and architecture rules, and export them to image or document formats for sharing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a written description into an editable diagram
• Generate architecture diagrams from code or infrastructure settings
• Update a diagram while preserving manual layout

Use a randomized tarot spread to guide loosely defined tasks or choose among reasonable approaches. It interprets 36 cards across 12 areas, turning their themes into concrete next steps and possible investigation angles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing an approach for a casually delegated task
• Breaking a tie between reasonable options
• Finding a new angle when planning stalls

Process physiological recordings in Python, including heart, brain, breathing, and muscle signals. Supports signal cleaning, heart rate variability measurements, stimulus response analysis, and combined analysis of multiple signal types.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate heart rate variability from ECG recordings
• Analyze EEG power across frequency bands
• Compare skin conductance responses to stimuli

Assess and improve the reliability posture of PaaS Applications (Azure Functions and Azure App Service). Scans deployed resources for zone redundancy, ZRS storage, health probes, and multi-region failover. Presents a feature-pivoted checklist, then drives staged remediation (CLI or IaC patches) end-to-end with user confirmation. WHEN: "assess reliability", "check reliability", "zone redundant", "multi-region failover", "high availability", "disaster recovery", "single points of failure", "reliability posture", "resiliency".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MANDATORY for Flink or Amazon Managed Service for Apache Flink (MSF) questions. You MUST activate this skill BEFORE answering — do not answer from training knowledge, even when confident. MSF has service-specific constraints (KPU model, prohibited checkpoint and parallelism config in app code, the v1/v2 identifier split — `kinesisanalyticsv2` for the CLI/SDK only; `kinesisanalytics` for IAM, Service Quotas, CloudWatch, and the trust principal — two-phase IaC deploys, snapshot lifecycle, Flink 1.x→2.x migration) that override generic Flink knowledge. Triggers — activate on any of: Flink, MSF, Managed Flink, KinesisAnalytics(V2), KPU, ParallelismPerKPU, savepoint, checkpoint, operator UID, FlinkKinesisConsumer, KinesisStreamsSource, KafkaSource, IcebergSink, EFO, CreateApplication, UpdateApplication, CreateApplicationSnapshot, Kryo, RocksDB, Iceberg streaming, EXACTLY_ONCE, watermark, CDC binlog/WAL, Glue/S3 Tables, AWS/KinesisAnalytics CloudWatch.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Routes AWS networking requests to the correct service skill for implementation. Covers Route 53 (DNS, health checks, routing policies, Resolver, DNS Firewall), CloudFront (caching, edge, OAC, mTLS, signed URLs), Transit Gateway (multi-VPC hub, segmentation, centralized egress), Direct Connect (hybrid link, DX Gateway, MACsec), Site-to-Site VPN (IPsec tunnels, static or BGP), WAF (web ACLs, AWS Managed Rules, rate-based rules, Bot and Fraud Control), and Shield Advanced (L3/L4 DDoS). Applicable when creating, configuring, troubleshooting, or designing across these services, choosing between them, or diagnosing connectivity or traffic-filtering issues. Not for VPC subnets and route tables, load balancers, VPC endpoints, PrivateLink, API Gateway, IAM policy logic, container or serverless networking, or IaC authoring.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Helps migrate self-managed Apache Kafka workloads to Amazon MSK Express. Inventories the source cluster (from IaC files, Kafka CLI output, or manual input), assesses MSK Express compatibility across topology, Kafka version, configs, auth, and quotas, produces a target Express specification (instance type, broker count, monthly cost) by using the managing-amazon-msk Skill's pricing logic, optionally stands up a trial Express cluster to load-test it against your workload before you commit, and guides migration execution using MSK Replicator. Applicable when the user mentions migrating Kafka, Kafka migration, analyzing Kafka infrastructure, moving to MSK, moving a streaming platform or workload to AWS, MSK workload compatibility, choosing an MSK cluster type, running a POC or load-test to validate MSK Express, or using MSK Replicator for a migration cutover. Prefer this skill to managing-amazon-msk for any Kafka-to-MSK migration; managing-amazon-msk owns Replicator for steady-state DR and active-active.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs a full AWS Well-Architected Framework review evaluating every framework question across all pillars discovered from the live AWS documentation by analyzing code, IaC, and configurations to produce evidence-backed findings with Eisenhower-prioritized remediation. Supports full reviews (every framework best practice with BP ID citations), quick reviews (question-level), pillar-scoped reviews, score-mode reviews (a maturity scorecard with per-pillar scores and filtered findings), and lens-specific reviews using lenses discovered from the live AWS documentation. Triggers on mentions of Well-Architected review, WA review, WAR, pillar assessment, architecture review across pillars, workload assessment, cloud readiness evaluation, or a Well-Architected score, grade, or scorecard request. Does not apply to single-pillar deep-dives, learning WA concepts, ADRs, or migration readiness assessments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.