'data framework' 검색 결과

Search current library documentation and code examples with Context7 CLI, a command-line tool. Find API usage and configuration guidance, select available version-specific documentation, and prioritize examples in the programming language you use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking current API usage and configuration
• Finding documentation for a requested version
• Prioritizing code examples in a chosen language

Assess scientific claims by examining study design, statistics, bias, and evidence quality. Use structured appraisal frameworks to identify supported conclusions, explain uncertainties, and propose practical improvements with concerns traced to specific evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether a paper's conclusions match its evidence
• Review study design before collecting data
• Identify statistical problems and potential biases

Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning analytical validation studies and acceptance criteria
• Evaluating calibration, accuracy, and precision
• Estimating and confirming detection and quantitation limits

Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Restrict which tools an agent can call
• Check suspicious inputs before execution
• Require human approval for sensitive operations

Build MCP servers, which expose tools and data to AI agents, with mcp-use. The material covers server functions, reusable prompts, and interactive React widgets, but this skill is deprecated and points to mcp-app-builder as its replacement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning an MCP server's tools and data resources
• Implementing reusable prompts and response formats
• Adding interactive React widgets to an MCP server

Guides development of MCP servers that let AI access external actions and data using mcp-use. Covers project setup, tools, reusable prompts, interactive React interfaces, authentication, validation, and deployment through task-specific reference guides.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting an MCP server project with mcp-use
• Adding tools and read-only data resources
• Building interactive React widgets

Research private companies through six perspectives, including business, finances, competition, and technology. Cross-check evidence, separate facts from inferences, and produce a valuation range, possible exit routes, and a map of missing information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess a private company's business and valuation range
• Compare conflicting revenue or funding reports
• Check company claims against hiring and patent signals

Assess REST, GraphQL, and gRPC APIs for authentication, access control, input validation, and business logic weaknesses. Use OWASP API Security Top 10, a framework of common API risks, to organize testing and document findings with reproduction steps and remediation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing API access across user roles
• Checking token handling and request rate limits
• Assessing GraphQL access control and data exposure

Analyze malware communication with command servers to understand periodic check-ins, message formats, encoded data, and backup destinations. Produces a structured analysis report and network detection rules based on captured traffic and malware code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating malware communication in captured traffic
• Measuring periodic check-in intervals and timing variation
• Mapping command servers and backup destinations

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Design and implement machine learning systems for production use. Cover data preparation, training, prediction services, model comparisons, monitoring, and retraining, with attention to reliability, response time, resource costs, and both technical and business evaluation metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a real-time recommendation service
• Comparing model versions with A/B testing
• Detecting changes in data and model performance

Create and render Table v9 tables in Preact, including headers and cells. Get guidance on reusable components, Query data integration, and virtualization, which renders only part of a table at a time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a basic table in Preact
• Reuse table features and components
• Connect server data through TanStack Query

Build and render Table v9 data tables in Lit using TableController and FlexRender. Follow the basic setup, keep table inputs stable, and find related guidance for reusable components, reactive state, and rendering only visible rows or columns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a basic data table in Lit
• Rendering table headers and cells
• Sharing table conventions and reusable UI

Create and render TanStack Table v9 tables in Ember using tracked data and dedicated rendering components. Covers table construction, template access, lifecycle cleanup, and guidance for reusing defaults and UI across tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a basic table in Ember
• Connecting tracked data updates to a table
• Rendering cells and headers in templates

Migrate Solid tables from Table v8 to v9, covering creation, rendering, and reactive state reads. Helps review controlled updates, changing data, and enabled table features alongside the shared core migration checklist.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Upgrading a Solid table from v8 to v9
• Replacing createSolidTable with createTable
• Fixing stale state after migration

Create and render Table v9 tables in Svelte 5. Covers basic setup and directs work on reusable components, TanStack Query data integration, and TanStack Virtual rendering, which limits displayed rows or columns for large tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a basic Table v9 table in Svelte 5
• Organize reusable table components
• Connect tables to TanStack Query data

Create unit test scaffolds, sample data, and mocks from code or specifications. Analyze coverage reports to prioritize missing tests, and guide test-driven development through failing tests, minimal implementation, and refactoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create unit tests from existing code
• Prioritize gaps in test coverage
• Turn acceptance criteria into tests

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

Develop cryptocurrency trading bots with Hummingbot documentation and code examples. Explore exchange connections, price and order book data, and strategies such as market making, which places buy and sell orders, or arbitrage across markets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Developing a cryptocurrency trading bot
• Exploring market making and arbitrage strategies
• Checking exchange connection settings

Set up reinforcement learning, training guided by rewards, for language models with slime. The skill covers Megatron-LM training, SGLang response generation, data preparation, asynchronous execution, and custom workflows for agents that use tools across multiple turns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare GRPO training for GLM or Qwen3 models
• Overlap response generation and model training
• Train agents with multi-turn tool use

Examine education and career choices through decision frameworks drawn from 张雪峰’s public statements. The skill considers family circumstances, researches current employment data for concrete questions, and responds in a direct conversational style.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare university majors using typical graduate outcomes
• Consider career options in light of family finances
• Discuss whether graduate study fits personal circumstances

Integrate charts and architecture diagrams into React or Next.js apps. Plan which parts run in the browser, separate rendering from application state, and address loading performance, mobile layouts, accessibility, and shareable views.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Integrating D3 charts into React components
• Choosing browser or server rendering in Next.js
• Sharing chart filters and selections through URLs

Design and review how a system is divided into modules, cohesive parts with clear responsibilities. Evaluate data ownership, communication rules, dependencies, and failure handling, and decide when to split or merge parts without assuming a particular technology.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Define responsibilities and boundaries for a new module
• Review dependencies between system modules
• Clarify data ownership and communication rules

Evaluate AI agents by preparing test cases, scoring responses and tool use, investigating failures, and comparing results after changes. Supports built-in checks and custom scoring rules, including evaluations performed by a language model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create test cases for an AI agent
• Check response quality and tool use
• Investigate failed evaluation cases