'database' 검색 결과

Build a Java MCP server, a service that lets AI agents call Java code, APIs, or databases. The skill prepares a Maven or Gradle project, implements tools, adds tests, and configures client connections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Expose existing Java code to AI agents
• Connect agents to APIs or databases
• Build an MCP server in Spring Boot

Set up and manage AlloyDB for PostgreSQL clusters, instances, and backups. Includes guidance for MCP integration, a way for agents to operate databases, plus references for application connections, Terraform, authentication, and AI search capabilities.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an AlloyDB cluster and primary instance
• Manage AlloyDB backups
• Automate database operations through MCP

Design and improve server-side applications using patterns for Node.js, Express, and Next.js API routes. Code examples cover API structure, database access, caching, authentication, error handling, and background jobs to guide implementation choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize API endpoints and application logic
• Reduce unnecessary database queries
• Add authentication and permission checks

Organize Node.js, Express, and TypeScript APIs into layers with clear responsibilities. This guide covers request handling, business logic, database access, input validation, error tracking, configuration, and testing to support consistent implementation and refactoring across services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding an API endpoint with clearly separated responsibilities
• Organizing database access through Prisma
• Adding input validation and error tracking

Write complex SQL, improve slow queries, and design database schemas for transaction processing and analytics. Uses schema details and query execution plans to guide indexing, validate results, and assess performance with realistic data volumes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate and improve slow analytical queries
• Design a database schema for a SaaS application
• Write customer retention and cohort analysis queries

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Build and operate LLM applications with guidance on RAG, which uses retrieved documents to inform answers. Covers document indexing, vector database selection, prompt improvement, response evaluation, caching, and estimates of model usage costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an assistant that answers from documents
• Choosing a vector database for document search
• Comparing answer quality after prompt changes

Import text, files, URLs, repositories, and database content into cognee memory. This skill explains input handling, storage organization, extraction options, and cost estimates, while helping diagnose unsupported arguments and document update errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Importing documents, URLs, or database content
• Organizing stored information by project or topic
• Estimating tokens and cost before importing

Operate cognee from the terminal to store, retrieve, update, and remove information. Covers dataset and configuration management, ingestion cost estimates, session history access, and commands for inspecting or applying database migrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Storing documents and asking questions from the terminal
• Estimating token usage and cost before importing data
• Managing datasets and saved configuration

Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect cognee to Qdrant or another database
• Import data from Slack or Gmail
• Configure monitoring with Keywords AI

Manage cognee database migrations, from checking migration status and recovering blocked writes to creating schema or data updates. It also guides dataset backups, restores, and transfers using cognee’s portable COGX archive format.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recover writes blocked by a failed migration
• Create migrations for database structure or stored data
• Back up and restore datasets with COGX

Prepare a deployment checklist covering tests, approvals, database changes, release steps, and monitoring. Tailor it to the release and document rollback triggers, the conditions for reverting a change, before production deployment begins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare checks for an upcoming production release
• Review a release with database migrations
• Verify feature flags and CI status before deployment

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

Design a RAG pipeline that retrieves documents to support AI answers. Analyze document splitting, select embedding models and vector databases, and evaluate retrieval quality using test queries and known relevant documents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a RAG system for internal documents
• Choosing how to split documents for search
• Comparing embedding models and vector databases

Design relational database tables from requirements and generate relationship diagrams, migration code, type definitions, and test data. Also covers indexes, audit history, and row-level access rules for applications serving multiple organizations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing tables for a new feature
• Reviewing table relationships and indexes
• Separating data by organization

Design and review system architecture, compare databases and structural approaches, and document technical decisions. Included tools generate architecture diagrams, inspect dependencies, and assess project structure to identify circular relationships, excessive coupling, and opportunities for improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing monolith and microservices approaches
• Choosing a database for project requirements
• Generating diagrams from an existing project

Finds and loads relevant Google skills for choosing or setting up services, databases, storage, authentication, and other platform components. It supplies guidance for comparing Google options, understanding trade-offs, and recognizing when another choice fits better.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare platforms for running a service
• Find guidance for database or authentication setup
• Locate a skill for a named Google product or API

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.

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

Verify Kortix changes with focused tests and real inputs and outputs. This skill guides test placement, API and CLI workflows, browser checks, database verification, and reporting of results, including skipped cases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify a bug fix with focused tests
• Check API and CLI behavior through real inputs and outputs
• Test database query conditions

Handle operational requests from Slack threads across a database, Stripe, Linear, and GitHub. The skill guides lookups, issue creation, and PR reviews while defining platform access limits and human approval requirements for consequential changes.

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• Checking customer accounts and billing status from Slack
• Creating Linear issues for reported problems
• Reviewing a PR and opening a proposed fix

Improve PostgreSQL queries and database design by examining execution plans and choosing indexes for actual access patterns. Get guidance on large tables, JSONB data, connection pooling, and maintenance while accounting for write overhead and memory use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow queries with execution plans
• Choosing indexes for search patterns
• Designing partitions for time-series data

Organize Notion workspaces with clear page structures, connected databases, and reusable templates. Build team wikis and task views, reduce duplicated content, and use the Notion API to automate page creation, database queries, and content updates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organizing a team wiki so information is easier to find
• Creating templates for meeting notes and project briefs
• Connecting project databases with filtered views

Set up Wren Engine through guided steps covering environment checks, installation, database connection, and project creation. Prepare data definitions and validate the setup, then suggest initial questions to help the user begin exploring their data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Installing Wren Engine for the first time
• Connecting a new database to Wren
• Creating a project and preparing data definitions

Generate Drizzle database migrations on a fresh Neon branch and review their production safety. Check table sizes, locking order, execution time, and data transfer needs, with load testing for changes affecting busy or large tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate migrations after changing a database schema
• Review locking risks before production deployment
• Rehearse large table changes under load