Base

LibreOffice Base

Overview

LibreOffice Base skill for creating, managing, and automating database workflows using the native ODB (OpenDocument Database) format.

When to Use This Skill

Use this skill when:

• Creating new databases in ODB format
• Connecting to external databases (MySQL, PostgreSQL, etc.)
• Automating database operations and reports
• Creating forms and reports
• Building database applications

Core Capabilities

1. Database Creation

• Create new ODB databases from scratch
• Design tables, views, and relationships
• Create embedded HSQLDB/Firebird databases
• Connect to external databases

2. Data Operations

• Import data from CSV, spreadsheets
• Export data to various formats
• Query execution and management
• Batch data processing

3. Form and Report Automation

• Create data entry forms
• Design custom reports
• Automate report generation
• Build form templates

4. Query and SQL

• Visual query design
• SQL query execution
• Query optimization
• Result set manipulation

5. Integration

• Command-line automation
• Python scripting with UNO
• JDBC/ODBC connectivity

Workflows

Creating a New Database

Method 1: Command-Line

soffice --base

Method 2: Python with UNO

import uno

def create_database():
    local_ctx = uno.getComponentContext()
    resolver = local_ctx.ServiceManager.createInstanceWithContext(
        "com.sun.star.bridge.UnoUrlResolver", local_ctx
    )
    ctx = resolver.resolve(
        "uno:socket,host=localhost,port=8100;urp;StarOffice.ComponentContext"
    )
    smgr = ctx.ServiceManager
    doc = smgr.createInstanceWithContext("com.sun.star.sdb.DatabaseDocument", ctx)
    doc.storeToURL("file:///path/to/database.odb", ())
    doc.close(True)

Connecting to External Database

import uno

def connect_to_mysql(host, port, database, user, password):
    local_ctx = uno.getComponentContext()
    resolver = local_ctx.ServiceManager.createInstanceWithContext(
        "com.sun.star.bridge.UnoUrlResolver", local_ctx
    )
    ctx = resolver.resolve(
        "uno:socket,host=localhost,port=8100;urp;StarOffice.ComponentContext"
    )
    smgr = ctx.ServiceManager
    
    doc = smgr.createInstanceWithContext("com.sun.star.sdb.DatabaseDocument", ctx)
    datasource = doc.getDataSource()
    datasource.URL = f"sdbc:mysql:jdbc:mysql://{host}:{port}/{database}"
    datasource.Properties["UserName"] = user
    datasource.Properties["Password"] = password
    
    doc.storeToURL("file:///path/to/connected.odb", ())
    return doc

Database Connection Reference

Supported Database Types

• HSQLDB (embedded)
• Firebird (embedded)
• MySQL/MariaDB
• PostgreSQL
• SQLite
• ODBC data sources
• JDBC data sources

Connection Strings

# MySQL
sdbc:mysql:jdbc:mysql://localhost:3306/database

# PostgreSQL
sdbc:postgresql://localhost:5432/database

# SQLite
sdbc:sqlite:file:///path/to/database.db

# ODBC
sdbc:odbc:DSN_NAME

Command-Line Reference

soffice --headless
soffice --base  # Base

Python Libraries

pip install pyodbc    # ODBC connectivity
pip install sqlalchemy # SQL toolkit

Best Practices

1. Use parameterized queries
2. Create indexes for performance
3. Backup databases regularly
4. Use transactions for data integrity
5. Store ODB source files in version control
6. Document database schema
7. Use appropriate data types
8. Handle connection errors gracefully

Troubleshooting

Cannot open socket

killall soffice.bin
soffice --headless --accept="socket,host=localhost,port=8100;urp;"

Connection Issues

• Verify database server is running
• Check connection string format
• Ensure JDBC/ODBC drivers are installed
• Verify network connectivity

Resources

• LibreOffice Base Guide
• UNO API Reference
• HSQLDB Documentation
• Firebird Documentation

Related Skills

• writer
• calc
• impress
• draw
• workflow-automation

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache data schemas, transformation rules, and query patterns. BM25 excels at finding specific column names, table references, and SQL patterns.

# Check for prior data engineering context before starting
python3 execution/memory_manager.py auto --query "data processing patterns and pipeline configurations for Base"

Storing Results

After completing work, store data engineering decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Data pipeline: ETL from PostgreSQL to Qdrant, 50K records/batch, incremental sync via updated_at" \
  --type technical --project <project> \
  --tags base data

Multi-Agent Collaboration

Share data schema changes with backend and frontend agents so they update their models accordingly.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Data pipeline implemented — ETL processing with validation, deduplication, and error recovery" \
  --project <project>