「sql」の検索結果

Expert en Apache Kafka (producers, consumers, streams, connectors, schema registry)

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。

Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.

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。

MongoDB, Redis, Cassandra, DynamoDB, and distributed database patterns for scalable 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。

Automate Supabase database queries, table management, project administration, storage, edge functions, and SQL execution via Rube MCP (Composio). Always search tools first for current schemas.

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。

Guidelines for developing with Kysely, a type-safe TypeScript SQL query builder with autocompletion support

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Optimize Entity Framework Core queries by fixing N+1 problems, choosing correct tracking modes, using compiled queries, and avoiding common performance traps. Use when EF Core queries are slow, generating excessive SQL, or causing high database load.

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。

When addressing slow application endpoints, high database CPU usage, or standardizing data access patterns.

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Performs comprehensive FiveM resource security, performance, and compatibility audits. Detects backdoors, RATs, SQL injection, event exploitation, NUI vulnerabilities, dupes, crash/DoS vectors, npm and build-chain supply chain attacks, and malware patterns across ESX, QBCore, QBox, ox_lib, and ND_Core frameworks, on both GTA V Legacy and Enhanced builds. Use whenever a FiveM/cfx resource is being reviewed — even if not explicitly asked — including: audit FiveM script, review FiveM security, optimize FiveM resource, check FiveM performance, FiveM code review, review Lua script security, audit ESX resource, audit QBCore resource, audit QBox resource, check for exploits, FiveM vulnerability scan, GTA V Enhanced migration check, resmon optimization, or asking whether a leaked, cracked, nulled or unknown-origin script is safe to run.

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Comprehensive fullstack development skill for building complete web applications with React, Next.js, Node.js, GraphQL, and PostgreSQL. Includes project scaffolding, code quality analysis, architecture patterns, and complete tech stack guidance. Use when building new projects, analyzing code quality, implementing design patterns, or setting up development workflows.

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Integrate MercadoPago Checkout Pro (redirect-based) into Next.js applications with any PostgreSQL database (Supabase, AWS RDS, Neon, PlanetScale, self-hosted, Prisma, Drizzle, or raw pg). Use when the user needs to: (1) Add MercadoPago payment processing to a Next.js app, (2) Create a checkout flow with MercadoPago, (3) Set up payment webhooks for MercadoPago, (4) Build payment success/failure pages, (5) Create a shopping cart with payment integration, (6) Troubleshoot MercadoPago integration issues (auto_return errors, webhook failures, hydration mismatches, double submissions). Triggers on requests mentioning MercadoPago, Mercado Pago, payment integration with MP, Argentine/Latin American payment processing, or checkout with MercadoPago. Supports all MercadoPago countries: Argentina (ARS), Brazil (BRL), Mexico (MXN), Colombia (COP), Chile (CLP), Peru (PEN), Uruguay (UYU).

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。

Connect to Azure Database for PostgreSQL Flexible Server from Node.js/TypeScript using the pg (node-postgres) package.

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 Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive patterns.

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 Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed 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。

Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.

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 Cosmos DB SDK for Rust (NoSQL API). Use for document CRUD, queries, containers, and globally distributed 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。

Build production-grade Azure Cosmos DB NoSQL services following clean code, security best practices, and TDD principles.

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Uses AWS Athena to query CloudTrail, VPC Flow Logs, S3 access logs, and ALB logs for forensic investigation. Covers CREATE TABLE DDL with partition projection, forensic SQL queries for Tespit etme unauthorized access, data exfiltration, lateral movement, and privilege escalation. Use investigating yaparken AWS security incidents or building cloud-native forensic workflows at scale.

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Provides web exploitation techniques for CTF challenges. Use when the target is primarily an HTTP application, API, browser client, template engine, identity flow, or smart-contract frontend/backend surface, including XSS, SQLi, SSTI, SSRF, XXE, JWT, auth bypass, file upload, req

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Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.

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Database optimization, query tuning, indexing strategies, migration management, and performance monitoring for PostgreSQL with Drizzle ORM. Use when optimizing slow queries, designing schemas, creating migrations, or setting up database monitoring.

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Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.

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Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, production database tables, or other data that PostHog does not collect natively. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, etc.), production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.

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Investigates a session recording by gathering metadata, person profile, same-session events, and linked error tracking issues in one pass. Use when a user provides a recording or session ID and wants to understand what happened — who the user was, what they did, what errors occurred, and whether there are related error tracking issues. Replaces the manual chain of session-recording-get, persons-retrieve, execute-sql, and query-error-tracking-issues-list.

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ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.

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。