This skill should be used when user asks to "query Supabase", "list Supabase tables", "get Supabase schema", "search Supabase records", "check Supabase database", "Supabase auth", "Supabase authentication", "RLS policy", "row level security", "Supabase foreign key", "table relationships", "Supabase join", "Supabase filter", "Supabase pagination", or needs guidance on Supabase database patterns, auth flows, RLS policies, or query best practices.
Generate SQL queries from natural language descriptions
Execute database operations via Supabase MCP (query/write/migration/logs/type generation). Triggers: query/statistics/export/insert/update/delete/fix/backfill/migrate/logs/alerts/type generation. Does not trigger for: pure architecture discussion or code planning. Write operations require confirmation; UPDATE/DELETE without WHERE is refused.
Optimize SQL query performance through EXPLAIN analysis, indexing strategies, and query rewriting for PostgreSQL, MySQL, and SQL Server. Use when debugging slow queries, analyzing execution plans, or improving database performance.
Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
SQL and NoSQL query optimization techniques, indexing strategies, execution plan analysis, JOIN algorithms, cardinality estimation, and database-specific query patterns
When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe," "Product," or "Dataset." For SERP feature types and zero-click patterns, use serp-features. For AI search visibility strategy (not markup), use generative-engine-optimization. For HowTo step sections (placement, copy, vs FAQ), use howto-section-generator.
Optimize slow queries, analyze SQL performance, and collect evidence for expensive workloads.
Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.
Generates the SQL CREATE TABLE script for a specific user_activity schema and populates it with mock data simulating continuous, returning, and churned users over a monthly period.
针对Oracle SQL查询请求，仅输出纯文本SQL语句，不使用Markdown代码块，不提供任何解释，以避免终端显示乱码。
Provides the specific command to create an empty SQLite database file on Debian without entering the interactive shell, suitable for use with ORMs like Hibernate.
Provides the specific command to create an empty SQLite database file on Linux/Debian without entering the interactive shell, intended for use with ORMs like Hibernate.
Formats raw SQL query results for Excel by removing metadata, rounding numbers, and ensuring a professional layout.
Generates C# code to perform a parameterized SQL search and display results in a DataGridView, including input validation and specific connection handling.
Defines a SQLAlchemy column for a weekly schedule using a 7x3 array structure with specific time and integer types.
Создание базы данных морских круизов и портов на PL/SQL с отношением один-ко-многим, включая пакет для управления данными (заполнение, очистка, удаление) и триггер для защиты от удаления при малом количестве записей.
Explains technical concepts, specifically SQL and database topics, using real-life analogies and a concise, beginner-friendly tone with strict code formatting rules.
Guides the user on adding a new TEXT column to a PostgreSQL table using SQLx migrations and backfilling existing rows with nanoid values, specifically targeting rows where the column is NULL.
使用 Pandas 和 SQLAlchemy 将 DataFrame 同步到 MySQL，处理 Merge 后缀，并应用复杂的字段比较逻辑（JSON解析、数值归一化、条件排除、字符串排序）以实现精确的 Upsert。
