566 个 ・ 集成工具: Terraform · 按人气排序
Guides a pull request from local checks through a recorded demo, GitHub upload, merge, and development environment verification. Explains the project's test and preview labels, attachment requirements, and rules for keeping the pull request current.
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Write and manage infrastructure as code with Terraform and OpenTofu. Design reusable modules, review planned changes before applying them, bring existing resources under management, and organize shared state so teams can track infrastructure across environments.
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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.
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Design cloud architectures and migration plans for AWS, Azure, and GCP. Produce service diagrams, selection rationale, security designs, cost estimates, and recovery strategies, with deployment and rollback plans that account for availability and operational monitoring.
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Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.
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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.
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Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
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Choose Cloudflare services and implement applications using guidance for compute, storage, AI, networking, and security. The skill directs the agent to relevant references and current official documentation for configuration, APIs, limits, and pricing.
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Plan AWS architectures, compare services, and review security using documentation verified through AWS MCP tools, which connect agents to external information. Get implementation examples and trade-offs tailored to the team's existing tools, experience, and constraints.
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Deploy applications and infrastructure to Cloudflare by choosing services that fit the project. Covers Workers, Pages, storage, and related platform options, with authentication checks and references for configuring and publishing the selected setup.
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Set up monitoring for deployed agents to inspect execution paths, latency, and model interactions. It explains Cloud Trace, prompt and response logging, BigQuery analytics, and external monitoring integrations, with checks for missing data and unwanted content capture.
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Guide AI agent development from requirements and project setup through implementation, behavior evaluation, deployment, and monitoring. Provides coding rules, debugging steps, and links to specialized skills so each phase follows a defined process.
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Create starter projects for AI agents, add deployment configuration and automated build and release pipelines to existing projects, or update generated project files. Supports Python and Go ADK templates and a prototype-first approach.
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Plan and carry out agent deployments on Google Cloud using agents-cli. The guide covers deployment targets, permissions, secrets, automated build and deployment pipelines, verification, and recovery procedures for agents built with supported frameworks.
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Plan and configure Kubernetes deployments, Git-based continuous delivery, and cloud resources defined in code. Covers Helm, Kustomize, ArgoCD, Flux, Terraform, Pulumi, and AWS CDK, with patterns for environment separation, secrets, validation, updates, and rollback.
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Review build and deployment pipelines for unsafe workflows, exposed credentials, and configuration flaws across GitHub Actions, Jenkins, GitLab CI, and Terraform state files. Use concrete evidence and false-positive checks to distinguish exploitable issues from suspicious settings.
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Build a planned feature by following an existing task list and technical plan. The skill respects task dependencies, runs tests before related implementation where applicable, records progress, and checks the completed work against the specification.
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Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
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[omh] Infrastructure-as-code change -- Terraform, OpenTofu, Pulumi, a Kubernetes manifest, a Helm chart: read the drift, the blast radius and the cost delta from the saved plan, then stage the apply behind a health gate with a rollback per stage. Use when the user says: iac-change, iac change, infrastructure as code, infrastructure-as-code, terraform plan, terraform apply, terraform state, terraform drift.
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Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.
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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.
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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".
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Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
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Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.
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