'deployment' 검색 결과

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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

Reduce LLM generation delays with speculative decoding, which proposes tokens ahead of verification. This skill compares small draft models, Medusa, and Lookahead Decoding, with implementation examples, tuning guidance, and deployment using vLLM.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing response delays in chatbots
• Speeding up code generation
• Choosing a small model to draft candidate tokens

Build Next.js 14+ applications with App Router, server-rendered components, and form actions. Produces routes, page layouts, data fetching, loading and error states, and SEO metadata, while supporting performance optimization and deployment preparation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building pages and layouts with App Router
• Implementing form submissions with Server Actions
• Adding loading states, error handling, and SEO metadata

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a Python environment for SageMaker deployment
• Separate AWS automation dependencies from system Python
• Check installed AWS package versions

Capture production errors from Convex server functions and browser interfaces in your own deployment. Redact sensitive information before storage, inspect recent failures, monitor new events, and optionally route recurring errors into a workflow that opens fix pull requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Collect server and browser errors in one deployment
• Inspect production failures with sensitive details redacted
• Monitor newly recorded errors

Review 72 hours of Convex deployment health events and trace read limits or conflicting writes to the responsible code. Receive prioritized performance, cost, and reliability findings with runtime evidence, source locations, and concrete fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating read limits and large table scans
• Tracing conflicting writes to shared records
• Reviewing deployment performance and cost issues

Review smart contract security through five steps covering Slither scans, special features, diagrams, documented safety conditions, and manual checks. Get project-specific findings, testing setup guidance, and a prioritized action plan for development or deployment reviews.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review smart contract security before deployment
• Triage Slither findings and false positives
• Visualize inheritance and access permissions

Deploy a runnable prototype to the hosting service you choose, such as Sites or Vercel. Use the available deployment tool, return a working shareable URL, and identify any remaining manual steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publishing a prototype for others to try
• Deploying a prototype to Sites
• Deploying a prototype to Vercel

Prepare and deploy Expo apps to iOS and Android stores or the web using EAS, Expo's build and deployment services. Covers production builds, store submissions, beta distribution, preview deployments, and automated release workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare Expo apps for iOS and Android store submission
• Distribute an iOS beta through TestFlight
• Deploy a web app and create a PR preview

Create and edit EAS workflow YAML files to automate builds and deployments for Expo projects. Check required settings, job dependencies, and dynamic values against the latest schema, which defines the accepted configuration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an automated build workflow for an Expo project
• Edit EAS deployment automation
• Check workflow settings and job dependencies

Build remote MCP servers, which let AI agents call external tools, on Cloudflare Workers. This skill covers server code, tool definitions, OAuth authentication, local testing, deployment, and connecting a Codex client.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a remote MCP server
• Adding OAuth authentication to MCP tools
• Exposing external APIs as tools for AI agents

Deploy an application or website to Vercel and receive its deployment link. Create a preview by default, request production explicitly, or use the included fallback script when the command line tool has no saved credentials.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a preview link for a website
• Deploying a project from a specified folder
• Deploying without saved CLI credentials

Deploy applications to Render by inspecting the code and preparing service settings. Generate a render.yaml configuration or create services through MCP, an agent tool connection, then check deployment status, logs, and basic service health.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publish a web application or static site on Render
• Prepare deployment settings for apps with databases or workers
• Generate and validate render.yaml

Coordinate Cloud Claw workflows from launching an agent deployment to managing its virtual machine. It routes work to focused skills for setup, starting and stopping machines, viewing logs, renewing usage, and accessing dashboards through local altllm commands.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Launch an agent deployment and manage it afterward
• Inspect, start, or stop an existing virtual machine
• Review logs and access a deployment dashboard

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Inspect production AI agents in FailproofAI Cloud using the fp CLI. Review errors, run histories, quality scores, and usage, then manage issues, audits, user access, and scoped API keys for the team's deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failed agent runs
• Checking for drops in quality scores
• Running audits and triaging findings

Guides deployment of Open Harness on Vercel, from collecting credentials and setting environment variables to checking the first session. Separates a basic setup from a fuller setup with GitHub repository access.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a personal copy of Open Harness
• Identifying credentials needed for a basic deployment
• Adding GitHub repository access

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Build Next.js 14+ applications with App Router, covering pages, server-side processing, data fetching, and caching. The skill also guides performance improvements, search engine metadata, and production deployment to Vercel or a self-hosted environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building pages and layouts with App Router
• Implementing form updates with Server Actions
• Configuring data fetching and cache revalidation

Provides GitHub Actions and GitLab CI templates for automating tests, builds, and releases. It adapts configurations to project types and covers caching, parallel jobs, credential handling, and controls for production deployments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up CI/CD for a frontend or backend project
• Automating tests and releases for a library
• Running checks only for changed parts of a monorepo

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create workflows for automated testing and deployment
• Investigate failed runs and suggest fixes
• Coordinate code reviews and security checks

Manage Flow Nexus accounts, isolated cloud environments for running code, and application deployment. The skill also covers publishing templates, tracking credits and payments, submitting coding challenges, managing stored files, and checking execution logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a cloud environment and run code
• Publish an app or deploy a template
• Check credit balances and configure automatic refills