Results for “deploy”

Deploy osquery on Windows, macOS, and Linux to inspect processes, listening ports, software, and system settings using SQL, a database query language. Configure scheduled checks and centralized management to support threat hunting and compliance monitoring.

• Deploying osquery across Windows, macOS, and Linux
• Investigating suspicious processes and network connections with SQL
• Periodically checking software inventories and system settings

Audit a codebase before production deployment across eight areas, including security, databases, dependencies, and AI usage. Receive findings with file locations, manual checklists, and a release verdict that blocks shipping when critical issues remain.

• Check an application before its first production release
• Find security and database issues before deployment
• Review backup restoration and rollback readiness

Deploy applications and infrastructure on Cloudflare by matching project needs to services such as Workers and Pages. Guides service selection and authentication checks, with references covering storage, AI, networking, security, and infrastructure configuration.

• Publish a website or application on Cloudflare
• Deploy serverless functions with Workers
• Choose storage for databases and files

Deploy n8n on a Linux server using Docker Compose and Caddy for automatic HTTPS. Covers single-instance setups, workers for parallel execution, secret management, deployment checks, updates, backups, and restoration.

• Installing n8n on a VPS
• Running workflows in parallel with workers
• Publishing n8n with automatic HTTPS

Review Google Cloud operations and develop guidance for monitoring, incident response, resource sizing, and automated deployments. Use framework-based questions and checklists to identify gaps in production readiness and plan ongoing operational improvements.

• Reviewing production readiness
• Planning monitoring and logging
• Improving incident response procedures

Helps deploy, manage, and troubleshoot applications on Kubernetes using kubectl and Helm. Covers container logs, resource settings, network access, and deployment strategies to investigate failures and review how applications run.

• Investigating repeatedly crashing containers
• Reviewing deployment and resource settings
• Troubleshooting service connectivity and DNS

Build an analytics web app from a Wren project's data definitions and business context. Create charts and tables, validate the app, and deploy to Vercel or Cloudflare with a checked URL for sharing.

• Building a sales dashboard from a Wren project
• Sharing a report with bundled data
• Creating an analytics app that fetches current data

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.

• Setting up automated build and deployment pipelines
• Packaging applications with Docker
• Managing Kubernetes and cloud infrastructure as code

Build and run Omnigent’s server and Postgres together with Docker Compose on a laptop or existing Docker host. Use the supplied configuration and troubleshooting guidance to rebuild images, investigate startup problems, or adapt deployment to another platform.

• Running Omnigent locally with Postgres
• Starting the stack on an existing VPS or EC2 host
• Investigating startup and web interface problems

Deploy and operate Vercel projects from the command line. Manage environment variables and domains, investigate build failures with logs and metrics, and handle platform settings while checking which project and team each operation targets.

• Deploy preview and production versions
• Manage environment variables, domains, and DNS
• Investigate build failures and performance issues

Helps prevent changes to the wrong Convex environment by identifying and announcing the target before each operation. Requires fresh consent for production actions and enforces a session-wide read-only mode when requested.

• Checking the target environment before a Convex deployment
• Requiring explicit consent for each production operation
• Keeping a session read-only during an investigation

Set up and operate NVIDIA AI-Q Blueprint locally or on your own infrastructure. Choose a deployment mode, configure credentials, check service health, troubleshoot failures, and provide a verified server address for aiq-research.

• Starting a backend for aiq-research
• Running AI-Q through a terminal or browser
• Deploying AI-Q to a Kubernetes cluster

Deploy n8n on a Linux server using Docker Compose and Caddy for automatic HTTPS. Choose a single instance or queue workers, configure secrets and Python task runners, and follow procedures for updates, backups, and recovery.

• Deploy n8n on a personal VPS with HTTPS
• Set up queue workers for parallel workflow execution
• Update, back up, or restore an n8n instance

Check release readiness by reviewing tests, type checks, linting, builds, security findings, documentation, and environment settings. Get a report separating deployment blockers from warnings, plus action items and checks for post-deployment health and rollback preparation.

• Checking readiness before a production deployment
• Reviewing staging environment settings
• Adding release checks to CI/CD

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.

• Planning an agent's requirements and architecture
• Running and debugging an agent locally
• Evaluating responses and tool use before deployment

Register a deployed agent with Gemini, choose the appropriate registration method, and troubleshoot setup problems. Also covers managing agents and registering MCP servers, which expose tools to agents, in Google Cloud Agent Registry.

• Registering a deployed agent with Gemini
• Automating registration in a release pipeline
• Listing and updating registered agents

Add a deployable service by following a structured guide for application code, infrastructure, configuration, and related integrations. It tracks applicable steps, reports local verification results, and leaves deployment and secret creation as explicit follow-up work.

• Adding a Rust service or Kafka consumer
• Creating a Cloudflare Worker
• Adding a Pulumi infrastructure stack

Deploy code changes to a live hero training run by starting a new run from a permanent checkpoint, a saved training state. Compare matching training steps against agreed criteria and roll back if the trial fails.

• Deploy backend or optimizer changes to a live training run
• Compare loss and throughput across matching training steps
• Rehearse checkpoint handoff and rollback procedures

Audit and prepare a GPU Kubernetes cluster for PAIDF Orchestration. Check available GPU capacity, deploy the Airflow workflow controller, choose external or in-cluster model services, and report whether the environment is ready for workflow execution.

• Checking a new GPU cluster before running workflows
• Deploying the Airflow controller
• Choosing external or in-cluster model services

Deploy status across all projects. Shows ECS service versions, Vercel deployments, recent deploys, pending deploys, and CI/CD pipeline state.

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[omh] App idea headed for launch: shape an app idea into decisions, delivery handoff, verification, release, and monitoring status. Use when the user says: idea-to-deploy, idea to deploy, from idea to deploy, plan to deploy, idea to launch, ship this idea, ship this feature, launch this feature.

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Deploy static pages to nexu.space. Use when user says deploy, publish, ship, or go live with a static site/page. Uploads files from workspace to <project-slug>.nexu.space via Wrangler + Cloudflare Pages. Supports first deploy and redeploy.

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Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

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Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

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