'application' 검색 결과

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Deploy applications to Render by inspecting code and preparing build settings, environment variables, and required services. Generates render.yaml deployment configuration or creates services through MCP, then checks deployment status, logs, and basic health.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publish a web application on Render
• Prepare deployment configuration for multiple services
• Set up databases and scheduled jobs

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Assess applications against a role's written scoring criteria, recording strengths, gaps, and supporting resume quotes. Save a structured review and propose tentative interview times for strong matches, while leaving all advancement and rejection decisions to the hiring manager.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing unscreened applications against written criteria
• Checking the resume evidence behind a candidate's score
• Recording strengths and gaps for hiring manager review

Configure OpenTelemetry to send AI application traces, records of execution steps, to Confident AI. Reuse existing exporters, choose the regional endpoint, and add AI-specific attributes without requiring the deepeval Python package.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Send existing AI application traces to Confident AI
• Configure tracing for a non-Python AI application
• Choose the US or EU endpoint for an API key

Develop patent application drafts from an invention description, covering prior art searches, claims that define protection scope, specifications, and examiner-style review. Compile documents for China, the United States, or Europe, with human checkpoints throughout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning an invention description into application drafts
• Assessing prior art before drafting claims
• Preparing patent drafts for CN, US, or EP

Test local web applications with Python scripts using Playwright, a browser automation tool. Verify interface behavior, investigate problems, capture screenshots, and inspect browser logs, with a helper that manages one or more application servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify buttons, links, and form behavior
• Investigate problems in dynamic web interfaces
• Capture screenshots of local applications

Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a chatbot that remembers conversation history
• Create document question answering with source references
• Build an agent that uses custom tools

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Build Apple platform applications with Swift, including SwiftUI screens and state management. Design reusable interfaces with protocols, implement asynchronous tasks, protect shared data with actors, and check code through builds, performance profiling, and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building SwiftUI screens and state management
• Fixing concurrency issues with async/await and actors
• Designing protocol-based application architecture

Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up application dashboards and alerts
• Investigate production issues using logs, metrics, and traces
• Run load tests before traffic increases

Build Angular 17+ applications with standalone components, routing, and shared application state. Covers reactive data handling with RxJS, performance improvements, and unit and integration tests, with code and explanations of architecture choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building Angular screens with standalone components
• Managing shared application state with NgRx
• Configuring lazy-loaded routes and access guards

Build C# applications with .NET 8+, including ASP.NET Core APIs and Blazor interfaces. Get models, endpoints, services, and configuration, with guidance on database access, asynchronous operations, cancellation, authentication, performance tuning, and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a REST API with ASP.NET Core
• Implement database access with Entity Framework Core
• Create Blazor components and manage application state

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publishing a web application on Cloudflare
• Deploying serverless functions with Workers
• Choosing storage services for an application

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

Build Solana applications using Helius for transaction submission, token and NFT queries, and wallet analysis. It also guides live data streams and webhooks, which notify applications when selected blockchain events occur.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a live wallet activity dashboard
• Query tokens and NFTs held by an address
• Notify an application about transfers or swaps

Operate desktop applications through DeepChat's built-in Computer Use tools. Inspect windows, click controls, enter text, use menus, and arrange windows, checking the visible result after each action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Entering text and using controls in desktop apps
• Opening files or URLs in an application
• Arranging application windows

Test local web applications with Python scripts using Playwright, a browser automation tool. Verify interface behavior, investigate problems, capture screenshots, and inspect browser logs, with a helper that manages one or more application servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify buttons, links, and form behavior
• Investigate problems in dynamic web interfaces
• Capture screenshots of local applications

Connects AI agents to remote Windows desktop applications on Amazon WorkSpaces Applications (AppStream 2.0) through the managed Agent Access MCP server, and guides reliable desktop automation. Covers connecting an agent to the MCP endpoint (SigV4, streaming URL, and Active Directory SAML/Domain Join), BLOCKING vs POLLING connect modes, the computer-use tools (screenshot, click, type, key, scroll), screenshot-budget and action-batching discipline, MCP tool forwarding (forwarded___ tools), session lifecycle and expire-on-delete, and troubleshooting connection errors. Use when building or debugging an agent that drives a remote Windows desktop or GUI application via WorkSpaces Applications / AppStream — including "dcv session not ready", "client_disconnected", 400 signing-region, POLLING/connection_status, SAML assertion, or forwarded tool questions. Not for Amazon WorkSpaces Personal/Core virtual desktops or general AppStream fleet administration unrelated to agent access.

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MANDATORY for Flink or Amazon Managed Service for Apache Flink (MSF) questions. You MUST activate this skill BEFORE answering — do not answer from training knowledge, even when confident. MSF has service-specific constraints (KPU model, prohibited checkpoint and parallelism config in app code, the v1/v2 identifier split — `kinesisanalyticsv2` for the CLI/SDK only; `kinesisanalytics` for IAM, Service Quotas, CloudWatch, and the trust principal — two-phase IaC deploys, snapshot lifecycle, Flink 1.x→2.x migration) that override generic Flink knowledge. Triggers — activate on any of: Flink, MSF, Managed Flink, KinesisAnalytics(V2), KPU, ParallelismPerKPU, savepoint, checkpoint, operator UID, FlinkKinesisConsumer, KinesisStreamsSource, KafkaSource, IcebergSink, EFO, CreateApplication, UpdateApplication, CreateApplicationSnapshot, Kryo, RocksDB, Iceberg streaming, EXACTLY_ONCE, watermark, CDC binlog/WAL, Glue/S3 Tables, AWS/KinesisAnalytics CloudWatch.

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Set up and keep a job application tracker: one master CV, every tailored version, where each was sent, its status, next action and follow-up date, plus a weekly review of what is working. Use when asked to track my job applications, organise my job search, which CV did I send to which company, or what should I follow up on this week. Produces a tracker table (markdown or CSV) with one row per application, a version log linking each to its tailored CV, this week's actions, and a short weekly review of response rates by source.

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Set up signal capture for a Simulink Real-Time application using `slrealtime.Instrument`. Use when choosing which signals a real-time application captures and where the data goes — test-pointing or canvas-badging a signal before build (`TestPoint`, `DataLogging`), constructing an Instrument from an `.mldatx`, calling `addSignal`, `addInstrumentedSignals`, `addFileLogSignals`, `validate`, `connectCallback`, `copy`, or `generateScript`, attaching an instrument to `tg.Instruments` via `addForFileLog`, `addForSDI`, or `addForMATLAB`, setting up live-streaming signals, dynamic file-log workflows, or persisting an instrument as M-code. Selects and routes signals only — not for configuring, building, running, or connecting applications onto targets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert knowledge for Azure Front Door development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring apex domains, rules engine, Private Link origins, TLS/WAF security, or tier migrations, and other Azure Front Door related development tasks. Not for Azure Application Gateway (use azure-application-gateway), Azure Traffic Manager (use azure-traffic-manager), Azure Load Balancer (use azure-load-balancer), Azure Web Application Firewall (use azure-web-application-firewall).

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