1.5万 件 ・ 関連度順
概要と使いどころ
TanStack Query (React Query) v5 (@tanstack/react-query) の API リファレンス。 useQuery, useQueries, useInfiniteQuery, useMutation, useSuspenseQuery, QueryClient, QueryClientProvider, queryOptions, HydrationBoundary, invalidateQueries, prefetchQuery, staleTime, gcTime, placeholderData, optimistic updates, SSR / hydration, persistQueryClient, eslint-plugin-query。 kubb（フック生成側）とは別。
Register a custom Query type for JetEngine's Query Builder — extend \Jet_Engine\Query_Builder\Queries\Base_Query for the runtime query class, extend \Jet_Engine\Query_Builder\Query_Editor\Base_Query for the editor component, then hook BOTH register actions — jet-engine/query-builder/queries/register (factory) for the runtime, jet-engine/query-builder/query-editor/register for the editor UI. Five abstract methods on Base_Query — _get_items(), get_items_total_count(), get_items_page_count(), get_items_pages_count(), get_current_items_page(). Built-in cache via get_cached_data() / update_query_cache(). Custom queries participate in JE 3.8+ MCP tool exposure and the frontend query inspector automatically. Use when scaffolding a custom query type (HPOS WC orders, third-party API source, custom DB table) for JetEngine listings, dynamic widgets, or REST API endpoints.
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LaravelのWebアプリやAPIを、役割の分かれた構成で実装・レビューします。データ取得、入力検証、API応答、キューやキャッシュを整理し、保守しやすい設計を支援します。
Turns a legal question, a set of draft questions, or source material such as a statute, jury instruction, rubric, contract checklist, or coding manual into a battery of narrow questions that TypeSafe's Jev, a fast classifier that returns probabilities rather than prose, can answer reliably across many documents. The skill breaks conclusions such as negligence, felony murder, or standing into element-level questions, writes the governing rule into each question, and delivers the questions alone, in Jev's request format or in plain English. Built for legal work that asks the same questions of many texts: coding statutes across jurisdictions, coding opinions or case files for empirical research, checking contracts against a review checklist, screening intake narratives, and grading student work against a rubric. Use when asked to write Jev questions, build a classifier for legal documents, or turn a legal test into yes-or-no questions. Running the questions requires Jev access; writing them does not.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).
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Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when creating or managing queues, configuring queue membership, setting up case or lead queues, creating public groups, or using groups in sharing rules and manual sharing. Trigger keywords: queue, public group, queue membership, queue email, group sharing, case queue, lead queue. NOT for the rule that decides which queue a record lands in — use admin/assignment-rules. NOT for Omni-Channel routing configuration — use admin/omni-channel-routing-setup.
日本語の概要は準備中です。原文の説明を表示しています。
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.
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Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues. Supports visibility timeout, message encoding, and batch operations. Triggers: "queue storage", "@azure/storage-queue", "QueueServiceClient", "QueueClient", "send message", "receive message", "dequeue", "visibility timeout".
日本語の概要は準備中です。原文の説明を表示しています。
Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources. Triggers: "LogsQueryClient java", "MetricsQueryClient java", "kusto query java", "log analytics java", "azure monitor query java". Note: This package is deprecated. Migrate to azure-monitor-query-logs and azure-monitor-query-metrics.
日本語の概要は準備中です。原文の説明を表示しています。
Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.
日本語の概要は準備中です。原文の説明を表示しています。
Systemic questioning techniques: Circular questions, scaling questions, miracle question, and exception questions for reflection and perspective expansion.
日本語の概要は準備中です。原文の説明を表示しています。
Reviews one employee software request (tool, purpose, users, data handled, cost, urgency) against the approved-tools list and the policies the user provides and returns a draft review for the reviewer: match state on the list with the row quoted, approved tools the list itself describes as covering the same need, policy clauses that bear on the request with quoted text, questions for the requester and the reviewer, and one suggested decision with its basis. Never approves, procures, installs or adds a tool to the list. Use when the user asks to "review this software request", "is this tool on the approved list", "check this request against our software policy", "do we already have something approved that does this" or "prepare the decision note for this tool request". Do not use for a full vendor security assessment, use vendor-risk-screening-brief instead; for sorting mixed requests, use request-intake-triage. Drafts for human review; never approves, authorises or signs off.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Search the Unique AI Platform knowledge base using the unique-cli search command, with automatic per-turn citation tracking so cited facts render as clickable reference chips and `<sup>N</sup>` footnotes on the Unique platform. Use whenever the user asks to find, search, or query documents or content on Unique, including filtering by folder or metadata. Also covers `unique-cli read <cont_id>` for reading the full indexed text of a document when its content ID is already known. NOTE: This search uses combined vector + full-text indexing. Excel (.xlsx/.xls), CSV (.csv), and image files are NOT full-text indexed, so they will not appear in search results. To locate these file types, use the unique-cli-file-management skill instead (browse folders with `unique-cli ls` to find them by name).
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when the user asks to "set up Cloudflare Queues", "create a message queue", "implement queue consumer", "process background jobs", "configure queue retry logic", "publish messages to queue", "implement dead letter queue", or encountering "queue timeout", "message retry", "throughput exceeded", "queue backlog" errors.
日本語の概要は準備中です。原文の説明を表示しています。
Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.
日本語の概要は準備中です。原文の説明を表示しています。
Integrating TanStack Router with TanStack Query: queryClient in router context, ensureQueryData/prefetchQuery in loaders, useSuspenseQuery in components, defaultPreloadStaleTime: 0, setupRouterSsrQueryIntegration for SSR dehydration/hydration and streaming, per-request QueryClient isolation.
日本語の概要は準備中です。原文の説明を表示しています。
Systemic and solution-focused methods: miracle question, scaling, exception exploration, circular questions, hypothetical questions, worsening questions, coping questions. Merged from solution-focused-therapy and systemic-questioning.
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Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). Review which AskUserQuestion prompts fire across gstack skills, set per-question preferences (never-ask / always-ask / ask-only-for-one-way), inspect the dual-track profile (what you declared vs what your behavior suggests), and enable/disable question tuning. Conversational interface — no CLI syntax required. Use when asked to "tune questions", "stop asking me that", "too many questions", "show my profile", "what questions have I been asked", "show my vibe", "developer profile", or "turn off question tuning". (gstack) Proactively suggest when the user says the same gstack question has come up before, or when they explicitly override a recommendation for the Nth time.
日本語の概要は準備中です。原文の説明を表示しています。
自分だけでは判断できない課題を、詳しい相手に渡す質問票へまとめます。相手の役割と知りたい事実を確認し、回答欄付きのMarkdown文書を作るスキル。
遺伝子情報や塩基・アミノ酸配列をggetで検索し、配列の類似性や遺伝子群の特徴を調べながら、結果と検索条件を再確認できる記録にまとめるスキル。