Pro Data Analysis Skill

Senior data analyst and SQL engineer specializing in enterprise data warehouses. Translates business questions into fully documented, optimized SQL queries through a systematic 7-stage workflow.

Features

• ✅ Multi-database support: Oracle, MySQL, PostgreSQL, SQL Server
• 🔍 Data Discovery: Search for tables/columns via metadata and comments
• 📊 Query Optimization: Analyze EXPLAIN plans and optimize performance
• ✅ Safety First: All queries are executed with limits and timeouts
• 📝 Full Documentation: Every query includes comments and documentation
• 🤝 Human-in-the-Loop: Checkpoints for user confirmation before proceeding

System Requirements

Python Packages

# Core dependencies
pip install python-dotenv

# Database drivers (choose according to the database you use)
pip install oracledb               # For Oracle
pip install mysql-connector-python # For MySQL
pip install psycopg2-binary        # For PostgreSQL
pip install pyodbc                 # For SQL Server

# Optional (for Excel document search)
pip install openpyxl pandas

SQL Server - ODBC Driver

SQL Server requires an ODBC driver. Install according to your operating system:

Windows:

• Download from Microsoft

macOS:

# Install unixODBC
brew install unixodbc

# Install Microsoft ODBC Driver
brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release
brew install msodbcsql17

Linux (Ubuntu/Debian):

# Install unixODBC
sudo apt-get install unixodbc-dev

# Install Microsoft ODBC Driver 17
curl https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add -
curl https://packages.microsoft.com/config/ubuntu/$(lsb_release -rs)/prod.list | sudo tee /etc/apt/sources.list.d/mssql-release.list
sudo apt-get update
sudo ACCEPT_EULA=Y apt-get install -y msodbcsql17

Configuration

1. Create a .env file

Copy .env.example to .env and fill in your connection details:

cp .env.example .env

2. Configure database connections

Oracle

DWH_TYPE=oracle
DWH_USERNAME=your_username
DWH_PASSWORD=your_password
DWH_DSN=hostname:port/service_name

MySQL

MYSQL_DEV_TYPE=mysql
MYSQL_DEV_USERNAME=your_username
MYSQL_DEV_PASSWORD=your_password
MYSQL_DEV_HOST=localhost
MYSQL_DEV_PORT=3306
MYSQL_DEV_DATABASE=your_database

PostgreSQL

PG_DEV_TYPE=postgresql
PG_DEV_USERNAME=your_username
PG_DEV_PASSWORD=your_password
PG_DEV_HOST=localhost
PG_DEV_PORT=5432
PG_DEV_DATABASE=your_database

SQL Server

MSSQL_DEV_TYPE=sqlserver
MSSQL_DEV_USERNAME=your_username
MSSQL_DEV_PASSWORD=your_password
MSSQL_DEV_HOST=localhost
MSSQL_DEV_PORT=1433
MSSQL_DEV_DATABASE=your_database
# Optional: MSSQL_DEV_DRIVER={ODBC Driver 18 for SQL Server}

Available Scripts

1. Search Schema Metadata

Search for tables and columns by name or comment:

# Search in comments and names
python scripts/search_schema.py --keyword "customer" --db DWH

# Search only in comments
python scripts/search_schema.py --keyword "customer" --search-in comments --db DWH

# Search with regex
python scripts/search_schema.py --keyword "CUST_|CUSTOMER_" --regex --db DWH

# Filter by schema
python scripts/search_schema.py --keyword "revenue" --schema SALES --db DWH

2. Check Table Structure

Check table structure, indexes, partitions, and statistics:

# Oracle
python scripts/check_table.py OWNER TABLE_NAME --db DWH

# SQL Server
python scripts/check_table.py dbo Customers --db MSSQL_DEV

# Output as JSON
python scripts/check_table.py SCHEMA TABLE --db DWH --format json

# Output as Markdown
python scripts/check_table.py SCHEMA TABLE --db DWH --format markdown

3. Run Query Safely

Run SELECT queries with row limits and timeouts:

# Run query from string
python scripts/run_query_safe.py --sql "SELECT * FROM SCHEMA.TABLE" --db DWH

# Run query from file
python scripts/run_query_safe.py --file query.sql --db DWH --limit 50

# Count rows only
python scripts/run_query_safe.py --file query.sql --db DWH --count-only

# Output as JSON
python scripts/run_query_safe.py --file query.sql --db DWH --format json

4. EXPLAIN Plan Analysis

Analyze execution plans for performance optimization:

# Run EXPLAIN on a query
python scripts/explain_query.py --file query.sql --db DWH

# Oracle
python scripts/explain_query.py --sql "SELECT * FROM TABLE" --db DWH

# SQL Server (using SHOWPLAN)
python scripts/explain_query.py --file query.sql --db MSSQL_DEV

# Output as JSON
python scripts/explain_query.py --file query.sql --db DWH --format json

5. Find Relationships

Find foreign keys and join paths:

# Find relationships for one table
python scripts/find_relationships.py --schema SCHEMA --table TABLE_NAME --db DWH

# Find join paths between multiple tables
python scripts/find_relationships.py --schema SCHEMA --tables TABLE1,TABLE2,TABLE3 --db DWH

6. Sample Data

Get sample data and profiling:

# Get 10 sample rows
python scripts/sample_data.py --schema SCHEMA --table TABLE_NAME --db DWH

# Get 50 sample rows
python scripts/sample_data.py --schema SCHEMA --table TABLE_NAME --db DWH --rows 50

# Data profiling (analyze data distribution)
python scripts/sample_data.py --schema SCHEMA --table TABLE_NAME --db DWH --profile

7. Search Documents

Search in Excel documentation (if available):

# Search in folder documents/
python scripts/search_documents.py --keyword "customer" --folder documents/

# Search with regex
python scripts/search_documents.py --keyword "CUST|CUSTOMER" --folder documents/ --regex

7-Stage Workflow

When using this skill with Claude, the workflow proceeds through 7 stages:

1. Requirement Analysis: Analyze business requirements
2. Data Discovery: Find relevant tables/columns
3. Data Mapping: Map data and define join conditions
4. Query Design: Design query with CTEs and comments
5. Query Testing: Test with EXPLAIN and safe execution
6. Optimization: Optimize based on EXPLAIN plan
7. Documentation: Save query and documentation

Checkpoints

The workflow has 4 checkpoints for user confirmation:

• CP1: After Requirement Analysis
• CP2: After Data Discovery (confirm tables/columns)
• CP3: After Data Mapping (confirm joins/filters)
• CP4: Before Query Testing (confirm query logic)

You can skip checkpoints by saying "skip checkpoints" or "auto mode".

Database-Specific Notes

Oracle

• Use ROWNUM for pagination
• Supports CONNECT BY for hierarchical queries
• Partition pruning with WHERE partition_key >= ...

MySQL

• Case-insensitive string comparison (default)
• Use LIMIT for pagination
• GROUP_CONCAT for string aggregation

PostgreSQL

• Case-sensitive string comparison (default)
• Use LIMIT for pagination
• Native JSON/JSONB support

SQL Server

• Use TOP or OFFSET...FETCH NEXT for pagination
• STRING_AGG for string aggregation (SQL Server 2017+)
• Extended properties for table/column comments

References

In the references/ folder, you can find reference documents:

• dialect-differences.md: Differences between Oracle, MySQL, PostgreSQL, SQL Server
• query-patterns.md: Common SQL query patterns
• window-functions.md: Guide to window functions
• optimization.md: Query optimization techniques
• database-design.md: Database design
• dwh-patterns.md: Data warehouse patterns

Troubleshooting

SQL Server Connection Issues

Error: "Can't open lib 'ODBC Driver 17 for SQL Server'"

• Install the ODBC driver (see System Requirements)
• Or specify a different driver: MSSQL_DEV_DRIVER={ODBC Driver 18 for SQL Server}

Error: "Login failed for user"

• Check username/password
• Check SQL Server Authentication mode (Windows Auth vs SQL Auth)
• Ensure user has privileges to access the database

Error: "SSL Security error"

• Add TrustServerCertificate=yes to the connection string
• Or properly configure SSL certificate

Oracle Connection Issues

Error: "TNS:could not resolve the connect identifier"

• Check DSN format: hostname:port/service_name
• Check tnsnames.ora if using alias

MySQL Connection Issues

Error: "Access denied for user"

• Check username/password
• Check host access permissions: GRANT ALL ON db.* TO 'user'@'host'

License

MIT

Version History

• 2.2.0: Added SQL Server support
• 2.1.0: Improved checkpoints and workflow
• 2.0.0: Added PostgreSQL support
• 1.0.0: Initial release with Oracle and MySQL