Instructions

Primary Objective

Export analytical results or filtered datasets to a CSV file with proper formatting, headers, and sorting.

Core Workflow

1. Understand the Export Request

• •Identify the source data to be exported (e.g., query results, filtered lists, analysis outputs)
• •Clarify the desired output format: column names, data types, sorting order
• •Determine the destination file path

2. Prepare Data for Export

• •Structure Data Logically: Organize columns in a meaningful sequence (ID, name, metrics, timestamps)
• •Format Numerical Values: Apply appropriate rounding/precision (typically 2 decimal places for percentages/float values)
• •Handle Missing Data: Decide on representation for null/empty values (empty string or "N/A")
• •Sort Data Meaningfully: Order by primary metric (descending for severity/drop percentages, ascending for IDs/names)

3. Generate CSV File

• •Use filesystem-write_file to create the CSV
• •Include header row with descriptive column names
• •Ensure proper CSV formatting (comma-separated, proper escaping if needed)
• •Write all data rows according to the prepared structure

4. Quality Verification

• •Read back the created file using filesystem-read_file to verify content
• •Check for correct row count and data integrity
• •Confirm proper formatting and sorting

Common Patterns from Trajectory

Pattern A: Exporting Query Results with Calculations

When exporting results that involve calculations (like percentage drops):

1. •Run the analytical query to get the dataset
2. •Perform any necessary calculations/transformations in the query itself
3. •Format numerical results (e.g., ROUND(value, 2))
4. •Sort by the key metric (e.g., ORDER BY drop_percentage DESC)
5. •Export with columns: identifier, name, metrics, calculated values

Pattern B: Multi-Source Data Integration

When combining data from multiple sources (e.g., CSV + database):

1. •Read local CSV files using filesystem-read_file
2. •Query database tables using appropriate services (e.g., google-cloud-bigquery_run_query)
3. •Join/merge datasets using common identifiers
4. •Apply filtering criteria (e.g., WHERE drop_percentage > threshold)
5. •Export the integrated result set

Pattern C: Conditional Logging with Exports

When export triggers additional actions (like critical alerts):

1. •Identify threshold conditions within the exported data
2. •For records meeting critical criteria, create log entries
3. •Use appropriate logging services (e.g., google-cloud-logging_write_log)
4. •Include identifying information (IDs, names) in log messages
5. •Set appropriate severity levels (e.g., "CRITICAL" for urgent notifications)

Best Practices

File Naming

• •Use descriptive names: bad_student.csv, performance_report.csv, export_[date].csv
• •Place in appropriate directories (e.g., /workspace/dumps/workspace/)

Data Presentation

• •Include all relevant context columns (IDs, names, timestamps)
• •Add calculated metrics as separate columns
• •Use clear, human-readable column headers
• •Maintain consistent formatting throughout

Error Handling

• •Verify file write operations succeeded
• •Check that expected row counts match
• •Validate data integrity after export

Related Skills

• •For data analysis before export: data-analyzer
• •For database querying: bigquery-query-executor
• •For log management: logging-manager