'deployment' 검색 결과

Tune cognee ingestion speed, query response time, and LLM costs by adjusting concurrency, text chunk sizes, request limits, and model choices. Estimate ingestion costs before processing and assess options for background work and deployment scaling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimating costs before importing documents
• Adjusting ingestion speed and request limits
• Reducing query response time

Guide website and web app development through implementation, builds, testing, and visual checks in a real browser. The workflow covers responsive layouts, accessibility, reusable components, and screenshots, with production checks after deployment when applicable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a website and verify it in a real browser
• Check layouts across desktop and tablet screens
• Extend existing components for consistent pages

Deploy web applications to Vercel for production or preview, then check build results, deployment status, and the live URL. The skill also covers linking projects, managing domains, and adding or downloading environment variables through the command line.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publish an existing web app to production
• Create a preview deployment for review
• Check deployment status and live URL responses

Deploy FastGPT on your own server using its official installation script and Docker Compose, which runs its services together. Check service health and access, investigate deployment problems, and receive the access URL, initial login details, and next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up FastGPT on your own server
• Checking service health after deployment
• Investigating port conflicts or container startup failures

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an object detection pipeline
• Clean images and convert annotation formats
• Optimize a trained model for deployment

Coordinate a game release from candidate preparation through testing, release approval, and deployment. Assign work to role-based agents and collect quality results, patch notes, launch messaging, and a post-release monitoring plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a release candidate and checklist
• Collect testing and release readiness results
• Decide whether a release should proceed

Match Google Cloud data residency and access requirements to available data boundary frameworks and cloud controls. Compare ranked recommendations in a table, see requirement gaps, and receive deployment guidance with built-in frameworks prioritized.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding frameworks for data residency requirements
• Comparing framework coverage and major requirement gaps
• Planning deployment for existing or new projects

Design and implement Google Cloud serverless web apps with separate presentation, application, and data layers. Produces architecture diagrams, Terraform infrastructure code, deployment instructions, and validation scripts for private networking and controlled access between layers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a serverless web app with separate application layers
• Generate Terraform for private Cloud Run and Cloud SQL connections
• Plan regional data residency requirements

Helps individual developers get started on Google Cloud by checking local tools and authentication, selecting or creating a project, and linking billing. It verifies the setup and directs users to specialized guides for their first deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a first Google Cloud project
• Reusing an existing project for development
• Checking authentication and billing setup

Design a Google Cloud system that combines multiple services, starting with a discussion of business and technical requirements. The skill produces service recommendations, architecture diagrams, deployment guidance, and validation plans, with user approval at each stage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Gather requirements for a new cloud workload
• Compare and select services for a cloud migration
• Create a diagram and explain system data flows

Design and deploy a Google Cloud global external Application Load Balancer with caching and web attack protection. Matches settings to workload types, discovers existing backends, and generates Terraform or gcloud deployment scripts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design global web delivery across multiple backends
• Choose caching settings for static content and APIs
• Configure Cloud Armor protection for each workload

Design an enterprise search system that answers questions using retrieved company content, an approach called RAG. Gather requirements and produce architecture diagrams, deployment guidance, and validation plans using GKE, AlloyDB, and open models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing conversational search for internal documents
• Planning RAG with a vector-enabled SQL database
• Hosting open models on GKE

Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrating an existing Cloud Run AI service to GKE
• Planning a self-hosted replacement for Gemini API inference
• Sizing GPUs and preparing deployment configuration

Create or update Kubernetes YAML configuration files for GKE Autopilot and Standard. Translates application requirements into manifests covering container permissions, CPU and memory allocation, health checks, storage, external routing, and GPU resources for AI model serving.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating GKE application deployment manifests
• Reviewing container security and resource settings
• Configuring external routes with GKE Gateway API

Helps AI agents recover from repeated failures by reviewing previous attempts, checking original sources, testing different hypotheses, and verifying results. It combines structured troubleshooting with performance review rhetoric modeled on large technology companies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Breaking out of repeated failed attempts
• Investigating configuration or deployment failures
• Verifying an agent's claimed completion

Save a page's SEO settings and compare them with later versions to detect changes after updates or deployments. Review differences in titles, headings, indexing controls, structured data, and available performance metrics, with severity levels and stored comparison history.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare SEO settings before and after deployment
• Check for unintended title or heading changes
• Investigate page changes after a traffic drop

Analyze Solidity smart contracts with Slither and Mythril before Ethereum deployment. Combine code inspection with execution path analysis, review potential vulnerabilities, remove duplicate findings, and produce an audit report with severity ratings and suggested fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking Solidity contracts before deployment
• Investigating reentrancy and access control weaknesses
• Consolidating findings from Slither and Mythril

Manage NodeGroup resources in a KubeSphere environment, including creation, updates, and deletion. Bind or unbind nodes, namespaces, and workspaces, verify changes, and investigate synchronization problems or deployment settings using the bundled API script.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting and updating NodeGroup resources
• Binding nodes, namespaces, and workspaces
• Investigating synchronization failures

Set up and troubleshoot automated build, test, and deployment workflows in KubeSphere DevOps. Covers Jenkins integration, project management, multi-cluster installation, and ArgoCD integration for deployments managed through Git.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up build, test, and deployment pipelines
• Configure Jenkins integration
• Install DevOps across multiple clusters

Coordinate Stripe pricing changes, Supabase database updates, and Vercel deployments through the Composio CLI, a command-line tool. Follow an ordered release workflow with post-deployment checks, Slack announcements, and guidance for reversing changes if verification fails.

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• Launch billing, database, and frontend changes together
• Promote a preview build alongside pricing changes
• Script a recurring release process

Diagnose unreachable or unhealthy OpenShell gateways and sandbox startup failures. Identify the underlying execution platform, then examine logs, authentication, certificates, and configuration with the matching tools to narrow down the cause.

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• Investigate gateway connection and health check failures
• Find why a sandbox remains pending or fails to start
• Check Docker or Podman runtime and image problems

Update a QM source fork or its deployed package dependency and prepare a pull request. The workflow preserves local customizations, resolves merge conflicts, checks configuration compatibility, and records validation results for review.

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• Merge upstream QM changes into a customized source fork
• Upgrade a deployment’s pinned QM package version
• Prepare an update PR with conflict resolutions and check results

Deploy the QM package from an organization-owned deployment repository to Fly.io or AWS. Set up an administrator, choose a model provider and sign-in method, configure connectors, and optionally activate Slack, with acceptance checks before handoff.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploy QM to Fly.io or AWS
• Set up an initial QM administrator
• Configure a model provider and sign-in method