Text-to-SQL Skill

Convert natural language questions into SQL queries and execute them against SQL databases.

Phase 1: Project Setup

Step 1: Ask about database connection

Ask user which database type they want to use:

Option A: SQLite (file-based, no credentials needed)

• User provides path to .sqlite or .db file
• Or places file in database/ folder

Option B: Server database (PostgreSQL, MySQL, MariaDB, etc.)

• User creates .env file with connection details
• Supported: PostgreSQL, MySQL, MariaDB, and other SQL databases

Step 2: Initialize project structure

Run the init script OR manually create structure:

Option A: Use init script

python scripts/init_project.py --target /path/to/project

Option B: Manual setup

mkdir -p database output/queries output/reports

Copy from skill folders to project root:

• scripts/*.py → project root (db_extractor.py, query_runner.py, list_databases.py, sql_helper.py)
• assets/example.env → project root
• assets/requirements.txt → project root
• assets/.gitignore → project root

Install dependencies:

pip install -r requirements.txt

Step 3: Configure connection

For SQLite:

# Place database file
cp /path/to/database.sqlite database/

# Extract schema
python db_extractor.py --sqlite database/YOUR_DB.sqlite

For server databases (PostgreSQL, MySQL, etc.):

Copy and edit the template:

cp example.env .env
# Edit .env with actual credentials

The example.env template contains:

DB_TYPE=postgresql  # postgresql, mysql, mariadb
DB_HOST=localhost
DB_PORT=5432        # 5432 for PostgreSQL, 3306 for MySQL
DB_USER=your_username
DB_PASSWORD=your_password
DB_NAME=your_database_name

Then extract schema:

python db_extractor.py --database your_database_name

Step 4: Verify setup

After extraction, these files should exist in output/:

• connection.json - current connection config
• text_to_sql_context.md - schema for LLM queries
• schema_info.json - full schema data
• database_documentation.md - human-readable docs

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Phase 2: Query Workflow

When user asks a data question:

Step 1: Read schema context

Read output/text_to_sql_context.md to understand:

• Available tables and columns
• Data types and relationships
• Enum values for filtering

Step 2: Generate and save SQL

Create SQL file based on user question. See sql_patterns.md for common query patterns.

# Save to output/queries/descriptive_name.sql

Step 3: Execute query

Get run command from output/connection.json, then:

# SQLite example
python query_runner.py --sqlite database/DB.sqlite -f output/queries/query.sql -o result.csv

# MySQL example
python query_runner.py -f output/queries/query.sql -o result.csv

Step 4: Report results

Tell user: "Results saved to output/reports/result.csv"

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Quick Reference

Commands

# List databases
python list_databases.py

# Extract schema (SQLite)
python db_extractor.py --sqlite database/file.sqlite

# Extract schema (MySQL)
python db_extractor.py --database db_name

# Run query (SQLite)
python query_runner.py --sqlite database/file.sqlite "SELECT * FROM table LIMIT 10"
python query_runner.py --sqlite database/file.sqlite -f query.sql -o result.csv

# Run query (MySQL)
python query_runner.py "SELECT * FROM table LIMIT 10"
python query_runner.py -f query.sql -o result.csv

# Output formats
--format csv   # default
--format xlsx  # Excel
--format json  # JSON
--format md    # Markdown

Project Structure

project/
├── .env                    # MySQL credentials (if using MySQL)
├── database/               # SQLite files go here
│   └── your_db.sqlite
├── output/
│   ├── connection.json     # Current DB connection
│   ├── text_to_sql_context.md  # Schema for LLM
│   ├── queries/            # Saved SQL queries
│   └── reports/            # Query results (CSV, XLSX, JSON)
├── db_extractor.py
├── query_runner.py
├── list_databases.py
└── sql_helper.py

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Example Workflow

User: "I have a SQLite database with e-commerce data. Help me analyze it."

Setup:

1. Ask user for SQLite file path
2. Copy file to database/
3. Run python db_extractor.py --sqlite database/file.sqlite
4. Read generated output/text_to_sql_context.md

User: "Show me top 10 sellers by revenue"

Query:

1. Read schema from output/text_to_sql_context.md
2. Generate SQL:

   SELECT seller_id, SUM(price) as revenue
   FROM order_items
   GROUP BY seller_id
   ORDER BY revenue DESC
   LIMIT 10;
   

3. Save to output/queries/top_sellers.sql
4. Execute: python query_runner.py --sqlite database/file.sqlite -f output/queries/top_sellers.sql -o top_sellers.csv
5. Report: "Results saved to output/reports/top_sellers.csv"