What happens if one of the optimization phases fails?

The orchestrator implements strict error handling; it will stop execution, report exactly which phase failed validation, and preserve all completed reports in the session directory for debugging.

How does it ensure the accuracy of its recommendations?

The workflow includes a dedicated verification phase (Phase 4) that runs actual shell scripts against your data to confirm the findings of previous AI-driven analysis phases.

What data sources does this skill work with?

While optimized for BigQuery JSON exports, the orchestrator is designed to handle various extraction types by spawning specialized phase agents that read structured schema documentation.

Does this skill automatically apply changes to my database?

No, it acts as an analysis and reporting tool. It generates a final implementation plan and reports so that developers can review and execute changes safely.

Schema Optimization Orchestrator

Name: Schema Optimization Orchestrator
Author: intent-solutions-io

byintent-solutions-io

Database Management

Orchestrates a multi-phase database schema optimization workflow with automated validation and impact assessment.

About

The Schema Optimization Orchestrator is a specialized Claude Code skill designed to automate complex database refactoring tasks. It follows a rigorous five-phase workflow—spanning initial analysis, utilization tracking, impact assessment, script-based verification, and final recommendation generation. By managing isolated session directories and enforcing strict JSON validation between phases, it provides a reliable 'test harness' pattern for optimizing large-scale data structures like BigQuery exports, ensuring every optimization is backed by evidence and verified for safety.

Key Features

Automated field utilization and low-usage identification
Isolated session management with comprehensive markdown reports
Estimated savings and risk assessment for proposed changes
0 GitHub stars
Sequential five-phase agent orchestration for deep schema analysis
Script-based verification phase to validate findings against raw data

Use Cases

Identifying and removing unused fields in BigQuery exports to reduce storage and compute costs
Performing pre-migration schema audits to streamline data warehouse transitions
Generating evidence-based documentation for database refactoring and governance projects

About

Key Features

Automated field utilization and low-usage identification
Isolated session management with comprehensive markdown reports
Estimated savings and risk assessment for proposed changes
0 GitHub stars
Sequential five-phase agent orchestration for deep schema analysis
Script-based verification phase to validate findings against raw data

Use Cases

Identifying and removing unused fields in BigQuery exports to reduce storage and compute costs
Performing pre-migration schema audits to streamline data warehouse transitions
Generating evidence-based documentation for database refactoring and governance projects