「querying」の検索結果

Zero-config Postgres for Netlify apps via @netlify/database — querying data from Functions/Edge Functions, writing schema migrations, setting up Drizzle ORM, local dev with netlify dev, database branches for deploy previews, and migrating an existing Postgres project onto Netlify. Use when adding a database, building a contact form or CRUD API, writing SQL migrations, wiring up Drizzle, running netlify database commands, testing with a local Postgres, or switching from Neon/Supabase/RDS to Netlify Database.

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。

BDB local-first long-term memory engine (memB). Use when querying, remembering, or adapting preferences, code architectures, and developer patterns across tasks.

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。

Fetch current, hourly, and daily weather forecasts and display required attribution using WeatherKit. Use when integrating weather data, showing forecasts, handling weather alerts, displaying Apple Weather attribution, or querying historical weather statistics in iOS apps.

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。

Implement, review, or improve data persistence using SwiftData. Use when defining @Model classes with @Attribute, @Relationship, @Transient, @Unique, or @Index; when querying with @Query, #Predicate, FetchDescriptor, or SortDescriptor; when configuring ModelContainer and ModelContext for SwiftUI or background work with @ModelActor; when planning schema migrations with VersionedSchema and SchemaMigrationPlan; when setting up CloudKit sync with ModelConfiguration; or when coexisting with or migrating from Core Data.

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。

Access Apple Card, Apple Cash, and Wallet financial data using FinanceKit. Use when querying transaction history, reading account balances, accessing Wallet orders, requesting financial data authorization, or building personal finance features that integrate with Apple's financial services.

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 Python. Use for querying Log Analytics workspaces and Azure Monitor 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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Deploys and configures osquery for real-time endpoint monitoring using SQL-based queries to inspect running processes, open ports, installed software, and system configuration. Use when building visibility into endpoint state, threat hunting across fleet, or implementing compliance monitoring. Activates for requests involving osquery deployment, endpoint visibility, fleet management, or SQL-based endpoint querying.

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。

Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.

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。

Monitors Certificate Transparency (CT) logs to detect unauthorized certificate issuance, discover subdomains via CT data, and alert on suspicious certificate activity for owned domains. Uses the crt.sh API and direct CT log querying based on RFC 6962 to build continuous monitoring pipelines that catch rogue certificates, track CA behavior, and map the external attack surface. Activates for requests involving certificate transparency monitoring, CT log auditing, subdomain discovery via certificates, or certificate issuance alerting.

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。

Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.

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。

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

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。

Querying Stellar chain data via Stellar RPC (preferred) and Horizon (legacy). Covers RPC JSON-RPC methods, Horizon REST endpoints, streaming, pagination, historical queries, Hubble/Galexie for deep history, and the RPC/Horizon migration story. Use when reading balances, transactions, operations, ledgers, contract events, or building any indexer/analytics workflow.

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 Python. Use for querying Log Analytics workspaces and Azure Monitor 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 when the user asks about the pwdev-prd audit trail — 'auditoria do prd', 'eventos', 'decisões registradas', 'audit report', 'exportar relatório de auditoria' — querying the shared SQLite database read-only (summary, events, decisions, artifacts, stats, export, query).

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 Python. Use for querying Log Analytics workspaces and Azure Monitor 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。

Generates DDD Specification for PHP 8.5. Creates reusable business rule objects for validation, filtering, and querying with composite pattern support. Includes unit tests.

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。

Manage knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic memory.

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 Python. Use for querying Log Analytics workspaces and Azure Monitor 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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Python JSON parsing best practices covering performance optimization (orjson/msgspec), handling large files (streaming/JSONL), security (injection prevention), and advanced querying (JSONPath/JMESPath).

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。

Look up Microsoft API references, find working code samples, and verify SDK code is correct. Use when working with Azure SDKs, .NET libraries, or Microsoft APIs—to find the right method, check parameters, get working examples, or troubleshoot errors. Catches hallucinated methods, wrong signatures, and deprecated patterns by querying official docs.

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。

INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.

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。

Deploys and configures osquery for real-time endpoint monitoring using SQL-based queries to inspect running processes, open ports, installed software, and system configuration. Use when building visibility into endpoint state, threat hunting across fleet, or implementing compliance monitoring. Activates for requests involving osquery deployment, endpoint visibility, fleet management, or SQL-based endpoint querying.

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。