'selection' 검색 결과

Build reusable React components that save settings in browser storage and support keyboard selection. The patterns cover settings dialogs and command palettes, with guidance on TypeScript types, storage keys, and managing keyboard focus.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Keeping app settings across page reloads
• Building command selection with arrow keys and Enter
• Creating reusable settings dialogs

Create or modify Excel workbooks, then independently check the saved file with Python. Verify required sheets, columns, populated cells, row counts, and sample selections against the requested structure before reporting the work as complete.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating Excel workbooks from reference data
• Identifying the correct workbook before editing
• Checking required sheets, columns, and row counts

Plan and run fuzz testing, which feeds varied inputs to software to uncover bugs. The skill guides tool selection, test harness creation, input collection, coverage measurement, and crash triage for parsers, protocols, kernels, and other targets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning fuzz tests for a file parser
• Preparing test harnesses and seed inputs
• Investigating stalled test coverage

Guides development of apps for viewing USD, a 3D scene format. Selects browser streaming or local implementation references, covering rendering, camera controls, selection, interface layout, and validation while keeping users’ scene files unchanged.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a browser viewer for USD scenes
• Develop a local workstation viewer
• Add camera controls and object selection

Compare a listed company with relevant peers using valuation multiples, ratios linking price or company value to financial measures. Produce reports or editable workbooks covering peer selection, implied prices, data quality, and valuation assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Select comparable companies and explain peer choices
• Assess valuation premiums and discounts
• Build an Excel or Sheets valuation workbook

Refactor large macOS SwiftUI views into focused files and components. Clarify window state, selection, commands, and AppKit integration while preserving existing behavior and keeping the main layout stable as users change selections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Split oversized SwiftUI views
• Organize state for each window
• Extract toolbar and command logic

Build custom browser charts with D3, including precise labels, annotations, zooming, and range selection. Create SVG graphics that adapt to mobile screens, use meaningful contextual backgrounds, and support export when needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating SVG charts with direct labels and annotations
• Adding zoom and range selection to scatterplots
• Plotting data over a soccer pitch or floor plan

Edit an image or selected region in iPolloWork Image Studio using the chosen model. Preserve pixels outside the editing mask and the original dimensions, then save a separate workspace image with a preview and reusable file path.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Editing only a masked region of an image
• Editing from a captured Image Studio selection
• Saving an edited version while keeping the original

Use border-beam to add moving edge highlights or breathing glows to React controls and panels. Connect effects to loading, selection, and focus states while retaining clear text, static indicators, keyboard focus, and support for reduced motion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a breathing glow to a processing panel
• Highlighting a selected card or current step
• Adding a moving bottom edge to an input

Integrate Jupiter services into Solana applications, including token swaps, prices, limit orders, and scheduled purchases. The skill guides API selection, authentication, transaction signing, and error handling, while explaining service-specific constraints and on-chain alternatives.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding token swap quotes and execution to an app
• Implementing limit orders and scheduled purchases
• Retrieving token prices and portfolio positions

Select technologies for AI and machine learning features in .NET 8+ applications. Get task-specific plans or implementation guidance for structured predictions, text generation, document retrieval, tool-using agents, and running pretrained models locally or in production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing libraries for classification or prediction in .NET
• Adding text generation or summarization to an application
• Designing document retrieval with source-backed AI answers

Run Weave Router locally with Codex to reproduce routing bugs and check response conversion, model selection, and routing markers. Temporary configuration keeps everyday Codex settings intact, with real providers or repeatable mock responses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reproduce a Codex routing bug locally
• Verify a router fix from request to response
• Check forced model selection and routing markers

Choose the AI model for each agent launch using saved preferences, session instructions, agent settings, and task type. Save global or per-agent choices for future sessions and include the selected model in launch acknowledgments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose models automatically by task type
• Save a default model for all agents
• Assign a persistent model preference to one agent

Validate a Kiln release candidate with standard code checks and selected tests against live APIs. Diagnose failures, inspect outdated model selections and coverage gaps, and produce a report with recommended actions without changing code or tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validate a release candidate before publication
• Diagnose failures in live API tests
• Check test model selections for outdated versions

Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a test for group comparisons or relationships
• Checking normality, variance, and outliers before analysis
• Estimating the sample size needed for a study

Compare qualitative research designs for studying experiences, stories, cultures, or developing theory. Receive three options matched to the research question and available resources, plus a plan for participant selection, data collection, analysis, and quality checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing qualitative research designs
• Planning participant selection and sample size
• Designing interview and observation procedures

Apply machine learning methods to estimate intervention effects and explore how effects vary across groups. Provides implementation examples, diagnostic checks, and reporting guidance for Double Machine Learning, causal forests, and variable selection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate average effects with many control variables
• Explore differences in effects across groups
• Select control variables using LASSO

Analyze strategic interactions in markets, including firm entry, pricing, auctions, and bargaining. Guides equilibrium selection and computation, explains how to handle multiple possible outcomes, and helps assess whether data can identify the model's parameters.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing and computing an equilibrium
• Planning an analysis of firm entry or price competition
• Handling multiple equilibria in empirical models

Estimate effects by comparing observations just above and below an eligibility threshold using regression discontinuity design. Covers plots, comparison range selection, sharp and fuzzy designs, validity checks, and sensitivity analysis for reporting research findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate a policy effect near an eligibility threshold
• Analyze treatment with imperfect compliance
• Check manipulation and covariate balance at a cutoff

Use RQL, a language that chains search and editing steps, to inspect and change Unity Editor content. Find objects, summarize data, update properties, and manage scenes, prefabs, assets, selection, and Play Mode.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find scene objects and summarize their properties
• Update lighting or object settings in bulk
• Create and manage scenes, prefabs, and assets

Organize agent skills around domain routers that map requests to specific commands. It guides router selection, automatic activation alongside related skills, and the separation between broad activation rules and precise command matching.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing command selection across multiple tools
• Activating domain skills together with their router
• Reviewing activation rules and routing patterns

Build or extend a Tutti app with a web interface, server, and shared data contracts. The skill guides local agent execution, dynamic agent selection, app tools, localization checks, packaging, and release workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a Tutti app with local agent execution
• Restructuring an existing web and server app for Tutti
• Adding agent selection and app-specific tools

Reviews machine learning preprocessing, features, and data splits for data leakage: information unavailable when making a real prediction. Identifies risky statistics, future data, and validation setups, then suggests corrections using only appropriate training information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing normalization and missing value imputation
• Checking feature selection and target encoding
• Finding future information in time series features

Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.