Results for “sql”

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.

• 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.

• 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.

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

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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Review security measures in Java Spring Boot services, including identity checks, access permissions, input validation, and secret storage. Provides configuration examples and a release checklist covering API request limits, browser protections, logging, and dependency vulnerabilities.

• Adding authentication and access permissions
• Reviewing API input validation and SQL queries
• Checking browser protections such as CSRF and CORS

Plan a practical equipment register for tracking serial numbers, assignees, locations, purchase values, warranties, and returns. On request, create empty CSV templates, SQL table definitions, JSON Schema, or Notion mappings from a shared field list.

• Setting up a laptop and equipment register
• Planning equipment returns when employees leave
• Reviewing fields needed for an asset audit

Design a business change log that records who changed what, when, and why. Get a focused recommendation first, then request empty templates in CSV, SQL table definitions, JSON Schema, or a Notion property mapping.

• Designing a record of expense approvals
• Moving spreadsheet change logs into a structured register
• Preparing log fields for an audit review

Analyzes one .NET project to plan integration tests for an Oracle-to-PostgreSQL migration. Identifies database access methods, prioritizes migration risks, and documents test cases, seed data, and coverage, including empty strings, NULL values, and timezone behavior.

• Identifying database access methods that need integration tests
• Prioritizing tests for Oracle-specific behavior
• Planning checks for empty strings, NULL values, and timezones

Set up and manage AlloyDB for PostgreSQL clusters, instances, and backups. Includes guidance for MCP integration, a way for agents to operate databases, plus references for application connections, Terraform, authentication, and AI search capabilities.

• Create an AlloyDB cluster and primary instance
• Manage AlloyDB backups
• Automate database operations through MCP

Create realistic sample data for testing, development, and demos. Specify fields, record counts, value ranges, and business rules, then receive CSV, JSON, SQL INSERT statements, or a Python generator script with validation and usage instructions.

• Creating test data that follows business rules
• Preparing realistic sample data for demos
• Populating a test database

Design hybrid search on AlloyDB by combining meaning-based search, keywords, and SQL filters. Turn requirements into architecture diagrams, database setup scripts, deployment guidance, and validation plans for search applications on Google Cloud.

• Designing product search with meaning-based matching and attribute filters
• Planning result reranking and AI quality checks
• Preparing AlloyDB and Cloud Run deployment guidance

Write SQL that uses BigQuery’s built-in machine learning and generative AI for forecasting, anomaly detection, identifying drivers of change, searching by meaning, and classifying or summarizing text stored in your data.

• Forecasting future values from time-series data
• Detecting anomalies and investigating drivers of change
• Searching documents by meaning

Track shared work across AI agents using a SQLite database. The skill claims tasks, records progress and evidence for each step, skips completed work, and checks required procedures before marking a task complete.

• Assigning work from a shared queue to multiple agents
• Resuming tasks while skipping completed steps
• Checking required procedures before task completion

Use Chainbase CLI to investigate blockchain records, including token holders, prices, wallet activity, and DeFi positions. Use Tops to discover trending crypto topics, inspect related posts, and track recent Twitter/X mentions of projects or keywords.

• Check major token holders and transfer history
• Review wallet balances and DeFi positions
• Run custom SQL queries on blockchain records

Review database access built with Entity Framework Core to find slow queries, excessive database calls, memory costs, and correctness issues. It traces application code and uses available SQL logs or execution plans to support targeted fixes.

• Reviewing slow EF Core queries and excessive database calls
• Investigating memory use in imports and background jobs
• Checking search filters, pagination, and index coverage

Create and validate mirrord.json settings for isolated database copies used in development and testing. Configure connection sources, copying scope, migrations, and authentication so experiments can run without affecting shared databases.

• Preparing isolated databases for development and testing
• Choosing which schemas and data to copy
• Configuring connection sources and IAM authentication

Translate natural language to SQL, optimize query performance, and interpret EXPLAIN plans for SQLite and PostgreSQL. Triggered when users ask to convert questions into SQL, improve slow queries, tune indexes, analyze execution plans, or mention keywords like NL2SQL, query tuning, or full table scan.

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