dct-infer

name

dct-infer

description

Use this skill when the user wants to generate SQL CREATE TABLE statements from data files, infer schema from CSV/JSON/Parquet, create database schemas from existing data, or get column types from a file. Triggers include "generate schema", "create table from csv", "infer types", "what's the schema", "get column types", "sql ddl", or when preparing data for SQL databases like DuckDB, PostgreSQL, or similar.

DCT Infer - Generate SQL Schema

Create DuckDB-compatible CREATE TABLE statements by analyzing data file contents.

When to Use

Use this skill when you need to:

• Create database tables from existing data files
• Document the schema of a dataset
• Generate DDL for ETL pipelines
• Understand column types in a file
• Prepare data for SQL-based analysis

Installation

which dct || go build -o dct && chmod +x ./dct

Usage

dct infer <file> [flags]

Flags

• -t, --table <name>: Table name (default: "default")
• -n, --lines <number>: Number of lines to analyze for type inference (useful for large files)
• -o, --output <file>: Output to file instead of stdout

Examples

Basic schema inference:

dct infer data.csv

With custom table name:

dct infer data.parquet -t events

Save schema to file:

dct infer large.ndjson -n 1000 -t users -o schema.sql

Infer from specific number of rows:

dct infer bigfile.csv -n 500 -t transactions

Output Format

DuckDB-compatible CREATE TABLE statement:

create table users (
    "id" bigint,
    "name" varchar,
    "email" varchar,
    "created_at" timestamp,
    "is_active" boolean
)

Supported Data Types

The inferred schema uses DuckDB types:

• bigint - 64-bit integers
• integer - 32-bit integers
• double - Floating point numbers
• varchar - String/text data
• timestamp - Date and time
• date - Date only
• time - Time only
• boolean - True/false values
• array(...) - Array columns
• row(...) - Struct/nested columns

Best Practices

• Use -n flag for large files to speed up inference
• Column names are quoted to handle special characters
• Output is compatible with DuckDB and similar SQL databases
• For Parquet files, types are read directly from metadata
• For CSV/JSON, types are inferred from sample data

Integration Examples

With DuckDB

# Create table directly
dct infer data.csv -t my_table | duckdb mydb.duckdb

# Or save and execute
dct infer data.csv -t my_table -o schema.sql
duckdb mydb.duckdb < schema.sql

In Scripts

#!/bin/bash
for file in *.csv; do
    dct infer "$file" -t "$(basename "$file" .csv)" > "${file%.csv}.sql"
done

Related Skills

• dct-peek: Preview data before inferring schema
• dct-profile: Check data quality before creating tables