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Use when the user asks to write SQL queries, optimize database performance, generate migrations, explore database schemas, or work with ORMs like Prisma, Drizzle, TypeORM, or SQLAlchemy.
Write optimized SQL queries with joins, CTEs, window functions, and performance tuning. Based on Anthropic's Claude Cookbooks.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.
Escreve SQL otimizado para o stack Evolution (PostgreSQL primário) com boas práticas. Use quando precisar traduzir uma necessidade de dados em SQL, construir uma query com múltiplas CTEs, joins e agregações, otimizar uma query contra tabelas grandes, ou obter sintaxe específica para consultas no banco do Evo CRM, Evo AI, ou qualquer serviço do stack Evolution. Dialetos secundários disponíveis: Snowflake, BigQuery, MySQL, DuckDB.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
Write correct, performant, readable SQL across all major data warehouse dialects
Explains, optimises, writes, and documents SQL queries. Use when asked to explain a SQL query, optimise slow SQL, translate SQL to plain English for non-technical stakeholders, write a query from a natural language description, or produce query documentation. Produces plain-English explanations, annotated optimised queries, or a data dictionary covering output shape, assumptions, and known limitations. Works across PostgreSQL, MySQL, BigQuery, Snowflake, and standard SQL.
Write ggsql queries — a grammar of graphics for SQL. Use when the user wants to create, modify, or understand a ggsql visualization query.
Write ggsql queries — a grammar of graphics for SQL. Use when the user wants to create, modify, or understand a ggsql visualization query.
Answer a quantitative business question by writing a SQL query against the data warehouse, validating it, and presenting the result. Use when the user asks "how many...", "what's the trend of...", "compare X vs Y over...", "what's our top N...", or anything that resolves to a query against tabular data. Produces a small result table plus the underlying query.
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.
Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.
Apply sql queries in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql queries or its strengths fit the task.
Turn user CSV files and a question into a typed, joined, repeatable local SQLite analysis with traceable records, browser revisions and verified portable exports.
Design and spec an analytical dashboard — define the question each chart answers, write the SQL queries, spec the layout and refresh cadence. Produces a complete dashboard spec ready to implement. Use when asked to "build a dashboard", "analytics dashboard", "BI dashboard", "weekly product health", or "visualize this data".
Build one company memory from a SQL database, a support ticket export and a folder of documents, linked into one graph by cognee, then answer questions that need several of those sources together. Use when someone wants to connect their company's database, tickets and docs, or asks who owns, decided or is handling something across them. Runs locally; reads the sources, writes only to cognee's memory.
Reviews or writes a multi-table SQL join for grain and fan-out safety — confirms the join key is unique on one side before an aggregate, catches many-to-many row multiplication, spots a LEFT JOIN silently turned INNER by a WHERE on the right table, and states the grain of the result. Use when a query joins two or more tables and then sums, counts, or averages, or when a reported total looks inflated. Do NOT use for single-table queries, index/performance tuning (that is postgres-query-review), or ORM/query-builder API questions.
Expert SQL query writing, optimization, and database schema design with support for PostgreSQL, MySQL, SQLite, and SQL Server. Use when working with databases for: (1) Writing complex SQL queries with joins, subqueries, and window functions, (2) Optimizing slow queries and analyzing execution plans, (3) Designing database schemas with proper normalization, (4) Creating indexes and improving query performance, (5) Writing migrations and handling schema changes, (6) Debugging SQL errors and query
Expert system for generating, validating, and optimizing ClickHouse SQL. Use this when the user needs data, queries, or analysis.