AmoxSQL

Sequence cleaning steps in a chain — trim/case/replace, null handling, deduplication, and type casting — in the right order. Use when raw ingested data needs cleaning before joins or aggregation.

Place Assert and Schema Validation nodes as gates that halt a chain when data is wrong, so bad data never reaches the output. Use when the pipeline must guarantee correctness before exporting or loading downstream.

Choose the right sink format, compression, and destination for a chain's output, and decide between exporting a file vs creating a table. Use at the end of a pipeline when deciding how to persist results.

Best practices for loading files into a chain — single file, folder globs, type detection, and union of many files. Use when the pipeline starts from CSV/Parquet/JSON/Excel files or a folder of files.

Decide between Merge (stack rows / UNION) and Join (match on a key) when a chain has multiple inputs, and set keys and join type correctly. Use when a pipeline combines two or more upstream tables.

Descompone un objetivo de procesamiento en un flujo de nodos Chains (source → transform → sink). Use when the user wants to build a data pipeline / chain, or describes an end-to-end "load X, clean it, summarize, export" goal.

Framework de razonamiento para convertir resultados de análisis en una narrativa visual clara y convincente

Cohort retention analysis — how groups of users or customers behave over time after an initial event

Detect nulls, duplicates, outliers, and integrity issues in a table, prioritized by downstream impact. Use when auditing data before a business analysis or when the user suspects problems in the data.

First look at a dataset — profiles structure, data quality, and key distributions to build a mental model before any specific analysis. Use when the user wants to understand a new table or asks for an overview without a defined goal.

Root-cause analysis — identify which dimensions explain a metric spike, drop, or anomaly

Diagnose and fix slow queries using EXPLAIN, rewriting joins, adding filters, and DuckDB-specific optimizations

Trend analysis over time — growth rates, seasonality, anomalies, and period comparisons. Use when the question involves how a metric evolves over a date/time dimension.

Multi-step structured analysis with visible progress tracking — enables create_plan and update_plan tools