2.2K skills · sorted by relevance
Plan and implement infrastructure, containers, CI/CD, deployment safety, observability, and rollback changes. Use for Docker, IaC, cloud runtime, Kubernetes, pipeline, deployment, or operational reliability work. Does not read dotenv files or approve/release deployments.
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Validates Azure deployments before they run: Bicep syntax, what-if previews, and permission checks. Use before azd up, azd provision, or az deployment, when preparing or reviewing Bicep files, or to preview what a deployment will change.
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Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.
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Create, revise, and validate agent skills through tests that compare behavior with and without the instructions. It helps identify unclear guidance, choose an appropriate document structure, and refine wording before deployment.
Prepare web applications for production with Docker configurations, automated testing and deployment workflows, health checks, and rollback plans. Compare release strategies and review application, infrastructure, monitoring, security, and operational readiness before launch.
Manage continuously running or cloud-hosted agents with lifecycle controls, logs, metrics, restricted permissions, and emergency stops. It guides incident response, staged deployments, rollbacks, and tracking success rates and cost per successful task.
Review Django authentication, permissions, input handling, and production settings. Use concrete configuration and code examples to address malicious queries, unwanted requests, injected scripts, unsafe uploads, and API access, alongside secret management and security logging.
Publish static websites and built web apps to GitHub Pages, Cloudflare Pages, or Netlify. The workflow includes preview approval, git-tagged deployment versions, live URL checks, and restoring an earlier version when an update needs reversing.
Organize software releases on GitHub, from version suggestions and release notes to testing, builds, and deployment. Coordinate multiple packages or repositories, roll changes out in stages, and configure rollback to restore an earlier release when problems occur.
Create and improve GitHub Actions workflows for automated testing and deployment. Coordinate multiple AI agents to review pull requests, investigate failed runs, scan for security issues, and manage releases across repositories.
Create and improve GitHub Actions workflows that automate building, testing, and deployment. The skill covers testing across multiple environments, dependency caching, build output management, and security settings such as limited permissions and protected deployment environments.
Review deployment evidence and recovery conditions when assessing a release. The skill directs the agent to consult the accompanying restore reference and include a timestamp in its decision, providing a narrowly defined release review procedure.
Build AI agents with Cline SDK using guidance on choosing APIs, adding custom tools, saving sessions, and handling live events. References also cover model providers, scheduled runs, multi-agent coordination, and production deployment.
Operate an existing Twenty app by syncing changes, managing server connections, building and deploying releases, and inspecting function logs. It also guides troubleshooting and CI/CD, the workflows used to automate testing and deployment.
Implement .NET workflows for Azure AI Foundry projects, including agent creation, resource connections, dataset uploads, model deployment inspection, search index management, and evaluations. Examples also cover authentication, asynchronous operations, and resource cleanup.
Audit an AI agent's completion claims using existing logs, artifacts, approvals, and deployment records. Reconstruct the run and assess each claim separately, distinguishing proven results, partial support, contradictory evidence, and missing proof without rerunning the work.
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.
Build AWS applications that run without managing servers directly. Covers Lambda functions, APIs, DynamoDB storage, message-driven workflows with SQS/SNS, deployment through SAM/CDK, and reducing the delay when a function starts for the first time.
Manage coding work from issue intake through implementation, review, deployment, and acceptance checks. Track each completion requirement against fresh evidence, with defined retry limits and explicit escalation when progress needs human input.
Investigate AI-agent and cloud infrastructure challenges in a CTF sandbox. Trace untrusted content into tool actions, compare deployment configuration with runtime behavior, and follow software artifacts from source through build and release to their actual consumers.
Build Helm charts, reusable packages for deploying Kubernetes applications. Organize templates, manage configuration values for different environments, document settings, and validate charts before packaging or installation, with guidance for troubleshooting deployment and dependency problems.
Retrieve past operational experience from OpenViking before or during coding, configuration, deployment, or recovery work. It reads relevant records, checks whether their assumptions match the current environment, and applies suitable procedures and checks.
Design or review distributed systems, where multiple computers work together, using 42 cloud design patterns. Organizes options for reliability, performance, messaging, security, and deployment, emphasizing why to choose each pattern and the trade-offs involved.
Investigate AWS production incidents using reusable CloudWatch log queries and metric calculations. Narrow affected resources, compare alarms with deployment events, and reconstruct a timeline to identify changes that may explain the symptoms.