'query' 검색 결과

TanStack Query (React Query) v5 best practices for data fetching, caching, mutations, and server state management. Use when building data-driven React applications, setting up query configurations, implementing mutations/optimistic updates, configuring caching strategies, integrating with SSR, or fixing v4→v5 migration errors.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Distinguish the six real reasons a Salesforce query can 'fail', and the protocol for diagnosing before declaring: object doesn't exist, not queryable in edition, permission-denied, field-level errors, namespace prefix missing, API version mismatch. NOT for one object's own query restrictions, required filters or row caps - use apex/soql-object-limits-and-restrictions. NOT for a query that works but is slow - use data/soql-query-optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Snowflake, BigQuery, clustering, partitioning, and materialized views for warehouse performance. Activate on: Snowflake, BigQuery, Redshift, query optimization, clustering, partitioning, materialized view, warehouse cost, query profile. NOT for: dbt model structure (use dbt-analytics-engineer), data modeling (use dimensional-modeler).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run a live investigation as an operator-driven query loop: the operator runs each query on the live system and pastes the result back, and you guide the investigation one labelled query at a time. Use alongside support-assist (which covers what to investigate) whenever someone is pasting query or command output back from a deployment, especially during an incident. Covers labelling, pacing, making each query decide the next step, version-grounding, and keeping queries cheap and correctly classified.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review and implement WordPress query-cache usage on WP 6.9+, especially direct interaction with query cache groups now using salted cache helpers. Covers wp_cache_get_salted, wp_cache_set_salted, wp_cache_get_multiple_salted, wp_cache_get_last_changed, affected query groups like post-queries, term-queries, user-queries, comment-queries, site-queries, and why plugins should usually use WP_Query APIs instead of writing query cache entries directly. Use when optimizing expensive reads, persistent object cache behavior, or cache invalidation bugs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

TanStack Query (React Query) v5 best practices for data fetching, caching, mutations, and server state management. Use when building data-driven React applications, setting up query configurations, implementing mutations/optimistic updates, configuring caching strategies, integrating with SSR, or fixing v4→v5 migration errors.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Snowflake, BigQuery, clustering, partitioning, and materialized views for warehouse performance. Activate on: Snowflake, BigQuery, Redshift, query optimization, clustering, partitioning, materialized view, warehouse cost, query profile. NOT for: dbt model structure (use dbt-analytics-engineer), data modeling (use dimensional-modeler).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Choose and implement ways to manage data in React apps, from values held by individual components to shared data and cached server responses. Covers Redux Toolkit, Zustand, Jotai, and React Query, including migration from legacy Redux.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a library for shared application data
• Managing fetched data and caching
• Migrating legacy Redux code to Redux Toolkit

Refactor state held in Redux or React Context by separating API data, URL settings, shared interface state, and local component state. Move each part to React Query, nuqs, Zustand, or React’s local state hooks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrating away from Redux or React Context
• Removing duplicate storage of API responses
• Sharing filters and pagination through URLs

Turn everyday questions into SQL queries, the commands used to retrieve and summarize database records. Uses your database structure and platform to produce queries with explanations, performance notes, and optional validation scripts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract users who meet specific activity conditions
• Build revenue reports by region and customer tier
• Create queries from database diagrams

Design a shared data platform across Google Cloud, AWS, and Azure for analytics and AI. The skill turns requirements into architecture diagrams, infrastructure code, deployment instructions, and validation checks using Apache Iceberg and BigQuery data agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a data platform across multiple clouds
• Planning data ingestion and AI serving components
• Preparing infrastructure code and deployment instructions

Investigate slow SQL, write complex joins and summaries, and design database structures. Uses execution plans, which show how queries run, to guide index choices and query rewrites, with before-and-after performance measurements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate a slow SQL query
• Write complex joins and grouped calculations
• Design tables, keys, and constraints

Assess GraphQL APIs during penetration tests, from finding endpoints and mapping data structures to checking access controls, query limits, subscriptions, and uploads. Includes example queries, guidance for testing tools, and defensive controls for interpreting findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Map GraphQL endpoints and data structures
• Check access controls across users and roles
• Review query limits and subscription security

Implement and review browser API requests in OpenBot using shared patterns for reading and changing data. Covers cache keys, refreshing stored results after changes, error handling, and how components display request states.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a screen that displays server data
• Implementing create, update, and delete actions
• Investigating stale lists after an update

Answer business questions about counts, trends, comparisons, or rankings using SQL, a language for querying tables. Validate the results, then present a concise answer, a small table, the executed query, and supporting checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Counting records for a defined period
• Checking how a business metric changes over time
• Comparing groups over the same period

Execute use when you need to work with query optimization. This skill provides query performance analysis with comprehensive guidance and automation. Trigger with phrases like "optimize queries", "analyze performance", or "improve query speed".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GCPプロジェクト単位でBigQuery認証を設定するスキル。 gcloud設定プロファイルで複数プロジェクトを安全に分離管理。 「BigQueryに繋ぎたい」「BQ認証」「gcloud認証」「データ分析の認証設定」等のリクエストで発動。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

JeecgBoot 系统主数据查询与管理。Use when user asks to query/create/manage system master data, or says "查询角色", "查询用户", "查询部门", "查询字典", "创建字典", "创建角色", "查岗位", "查职务", "查租户", "查数据源", "查定时任务", "系统主数据", "query roles", "query users", "query depts", "create dict", "create role", "master data". Also triggers when other skills (desform, bpmn, codegen) need to look up or create roles, users, departments, dictionaries, or positions as dependencies — follows "先查后建" (query-first-create-later) principle.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.