'sql' 검색 결과

Review every project in a .NET solution for Oracle dependencies, classify its migration status, and create a saved PostgreSQL migration plan with a project inventory and an order that prioritizes shared libraries.

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• Planning an Oracle-to-PostgreSQL migration across multiple projects
• Identifying Oracle dependencies in a .NET solution
• Deciding migration order based on project dependencies

Create integration tests for .NET database access before migrating from Oracle to PostgreSQL. The tests record expected Oracle behavior with controlled test data and portable checks, providing a baseline for later migration validation.

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• Recording Oracle behavior before migration
• Testing repository methods and stored procedure calls
• Checking empty strings, NULL inputs, and datetime precision

Design PostgreSQL tables by choosing data types, validation rules, and indexes that match actual queries. The skill also guides JSONB usage, partitioning large tables, and planning schema changes with attention to read and write workloads.

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• Designing tables for users and orders
• Reviewing indexes against actual queries
• Choosing between relational columns and JSONB

Design PostgreSQL tables around actual queries and data volumes. Choose data types, validation rules, and indexes, then plan large table partitioning, access rules for individual rows, and schema changes with migration and rollback considerations.

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• Designing tables for users, orders, and related data
• Reviewing data types, constraints, and indexes
• Planning large tables and frequent update workloads

Review an analysis before sharing it with stakeholders. Check calculations, data selection, comparison methods, potential bias, charts, and whether conclusions follow from the evidence, then receive a readiness assessment and specific improvements.

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• Reviewing an analysis before an executive presentation
• Spot-checking SQL results and aggregation logic
• Checking comparisons across different time periods

Design systems that collect, transform, and store data. Choose between scheduled and continuous processing, generate workflow configurations, validate data quality, and analyze performance using guidance for tools including Python, SQL, Spark, Airflow, dbt, and Kafka.

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• Designing data ingestion and transformation pipelines
• Choosing scheduled or real-time data processing
• Checking data freshness, completeness, and uniqueness

Develop time-series and event-data solutions with TimescaleDB, an extension for PostgreSQL. The skill provides organized references for installation, time-based tables, ongoing aggregate calculations, compression, and performance troubleshooting.

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• Learn how to install and use TimescaleDB
• Design tables for time-series and event data
• Implement time-bucket and continuous aggregates

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

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• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

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.

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

Create, edit, or validate Jupyter notebooks for SQL and Python analysis. Organize data sources, assumptions, calculations, and findings into a readable document that others can review and rerun, with execution gaps clearly identified.

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• Preparing an analysis notebook for handoff
• Organizing experiment logs and data-quality checks
• Making existing notebooks easier to read and rerun

Build and review Cloudflare Durable Objects for applications that retain shared state, such as chat rooms, games, and booking systems. Covers SQLite storage, remote method calls, WebSockets, scheduled work, Workers integration, configuration, and Vitest tests.

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• Build shared state for chat rooms, games, and bookings
• Implement remote methods, SQLite storage, WebSockets, and alarms
• Configure Workers bindings and migrations

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.

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

Test web APIs, authentication, and LLM features with six Python scanners. Check cross-site access rules, header injection, database inputs, and signed login tokens, then investigate hidden server behavior through external callbacks and chatbot responses.

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• Checking CORS and response-header behavior
• Testing NoSQL logins and JWT authentication
• Confirming hidden bugs through external callbacks

Find test inputs and bypass references for web security checks, including XSS, SQL injection, and access control flaws. Use the guidance to investigate impact and assess whether a finding is suitable for a vulnerability report.

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• Finding test inputs for web vulnerability checks
• Reviewing WAF bypass techniques and response differences
• Tracing user input into risky browser operations

Review web application code against OWASP Top 10 (2021), a list of common security risks. Receive findings with file locations, severity ratings, concrete fix code, and a prioritized list of remediation tasks.

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• Reviewing a project's OWASP Top 10 security coverage
• Checking code for SQL injection and XSS
• Inspecting authentication and access controls

Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.

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• Consolidating separate agent memory systems
• Migrating SQLite and Markdown data to AgentDB
• Finding stored information with similar meaning

Manage Fabric Warehouse, Lakehouse SQL endpoints, and Mirrored Databases: DDL/DML, COPY INTO, read-only T-SQL, Query Insights diagnostics, and Capacity Metrics CU-spike correlation. Synapse migration target SQL belongs to synapse-migration; Fabric SQL database belongs to sqldb-cli. Triggers: query warehouse, create warehouse table, failed or canceled query, CU spike, Capacity Metrics app, custom SQL pool, Lakehouse table health.

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Expert knowledge for Azure SQL Database development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Hyperscale, serverless, geo-replication, Always Encrypted, or CI/CD/Terraform deployments, and other Azure SQL Database related development tasks. Not for Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Cosmos DB (use azure-cosmos-db), Azure Synapse Analytics (use azure-synapse-analytics).

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Expert knowledge for Azure SQL Managed Instance development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning MI tiers/networking, configuring HA/geo-replication, Entra/TDE security, or app connectivity patterns, and other Azure SQL Managed Instance related development tasks. Not for Azure SQL Database (use azure-sql-database), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Cosmos DB (use azure-cosmos-db), Azure Data Factory (use azure-data-factory).

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ByteHouse AI 查询技能，提供自然语言转 SQL（Text2SQL）、SQL 执行、库表结构查询、多模态向量化和向量检索等能力，覆盖 ByteHouse 云数仓的日常查询、SQL 生成与执行场景。当用户提到 "ByteHouse"、"查表"、"查数据"、"Text2SQL"、"自然语言查询"、"列出数据库"、"列出表"、"执行 SQL"、"生成 SQL"、"多模态检索" 等诉求，或者需要基于 ByteHouse 完成上述任务时，应使用本 Skill。

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You are an expert in Drizzle ORM, the lightweight TypeScript ORM that maps directly to SQL. You help developers write type-safe database queries that look like SQL (not a new query language), generate migrations from schema changes, and deploy to serverless environments with zero overhead — supporting Postgres, MySQL, SQLite, Turso, Neon, PlanetScale, and Cloudflare D1.

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Framework-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.

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Design relational database schemas (and choose when to go NoSQL) that stay maintainable and fast. Use this skill whenever the user mentions tables, DDL, entities and relationships, normalization (1NF/2NF/3NF), primary and foreign keys, UUID vs bigint IDs, indexes (B-tree, composite, covering, partial), EXPLAIN, constraints (CHECK, UNIQUE, exclusion), transactions and isolation levels, migrations (Alembic, Prisma, Flyway, expand-contract, backfilling), or SQL vs NoSQL (MongoDB, DynamoDB, Cassandra, graph databases). Also trigger for "design the database", "model this domain", "which database should I use", or writing ORM models and migration files for PostgreSQL, MySQL, SQLite, or SQL Server.

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T-SQL, stored procedures, and MS SQL Server DBA practices. Use when writing SQL queries, designing schemas, tuning SQL Server performance, managing backups, configuring security, or using SQL Server 2025+ features.

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