schema-optimization-orchestrator
Automates schema optimization through a multi-phase test harness workflow.
Install
mkdir -p .claude/skills/schema-optimization-orchestrator && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7602" && unzip -o skill.zip -d .claude/skills/schema-optimization-orchestrator && rm skill.zipInstalls to .claude/skills/schema-optimization-orchestrator
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Multi-phase schema optimization workflow orchestrator. Creates session directories,
spawns phase agents sequentially, validates outputs, aggregates results.
Trigger: "run schema optimization", "optimize schema workflow", "execute schema phases"Key capabilities
- →Creates time-stamped, isolated directory structures for each run
- →Executes 5 sequential analysis phases with state passing
- →Validates JSON artifacts against expected schema structure
- →Aggregates final evidence reports into a summary
- →Spawns independent agents for phase-specific logic
How it works
Uses a shell-based orchestrator to cycle through distinct agents, ensuring that each phase produces a validated JSON output before passing the state to the subsequent phase.
Inputs & outputs
When to use schema-optimization-orchestrator
- →Optimize database schema
- →Run automated schema analysis
- →Validate schema migration results
About this skill
Schema Optimization Orchestrator
Runs a multi-phase schema optimization workflow with strict validation and evidence collection.
Workflow Pattern
This is a test harness pattern:
- Creates isolated session directory per run
- Spawns 5 phase agents sequentially
- Each phase reads reference docs, runs scripts, writes reports
- Validates JSON outputs and file artifacts
- Aggregates final summary
Inputs (JSON)
{
"skill_dir": "/absolute/path/to/.claude/skills/schema-optimization",
"input_folder": "/path/to/bigquery/export",
"extraction_type": "bigquery_json",
"session_dir_base": ".claude/skills/schema-optimization/reports/runs"
}
Required:
- skill_dir: Absolute path to this skill directory
- input_folder: Path to data to analyze
- extraction_type: Type of data extraction (e.g., "bigquery_json")
Optional:
- session_dir_base: Where to create run directories (default: reports/runs)
Orchestration Steps
1. Create Session Directory
TIMESTAMP=$(date +%Y-%m-%d_%H%M%S)
SESSION_DIR="${session_dir_base}/${TIMESTAMP}"
mkdir -p "${SESSION_DIR}"
2. Run Phase 1: Initial Schema Analysis
Spawn Phase 1 agent with:
{
"skill_dir": "<skill_dir>",
"session_dir": "<SESSION_DIR>",
"reference_path": "<skill_dir>/references/01-phase-1.md",
"input_folder": "<input_folder>",
"extraction_type": "<extraction_type>"
}
Expected output:
{
"status": "complete",
"report_path": "<SESSION_DIR>/01-initial-schema-analysis.md",
"schema_summary": {
"total_tables": 0,
"total_fields": 0,
"key_findings": []
}
}
Validation:
- JSON parse succeeds
statusis "complete"report_pathfile existsschema_summaryhas required keys
3. Run Phase 2: Field Utilization Analysis
Spawn Phase 2 agent with:
{
"skill_dir": "<skill_dir>",
"session_dir": "<SESSION_DIR>",
"reference_path": "<skill_dir>/references/02-phase-2.md",
"phase1_report_path": "<phase1_report_path>",
"input_folder": "<input_folder>"
}
Expected output:
{
"status": "complete",
"report_path": "<SESSION_DIR>/02-field-utilization-analysis.md",
"utilization_summary": {
"unused_fields": [],
"low_utilization_fields": [],
"recommendations": []
}
}
4. Run Phase 3: Impact Assessment
Spawn Phase 3 agent with:
{
"skill_dir": "<skill_dir>",
"session_dir": "<SESSION_DIR>",
"reference_path": "<skill_dir>/references/03-phase-3.md",
"phase1_report_path": "<phase1_report_path>",
"phase2_report_path": "<phase2_report_path>",
"input_folder": "<input_folder>"
}
Expected output:
{
"status": "complete",
"report_path": "<SESSION_DIR>/03-impact-assessment.md",
"impact_summary": {
"high_risk_changes": [],
"medium_risk_changes": [],
"low_risk_changes": [],
"estimated_savings": {}
}
}
5. Run Phase 4: Verification with Script
Spawn Phase 4 agent with:
{
"skill_dir": "<skill_dir>",
"session_dir": "<SESSION_DIR>",
"reference_path": "<skill_dir>/references/04-phase-4-verify-with-script.md",
"phase2_report_path": "<phase2_report_path>",
"phase3_report_path": "<phase3_report_path>",
"input_folder": "<input_folder>",
"script_path": "<skill_dir>/scripts/analyze_field_utilization.sh",
"output_folder_path": "<input_folder>"
}
Expected output:
{
"status": "complete",
"report_path": "<SESSION_DIR>/04-field-utilization-verification.md",
"verification_summary": {
"files_analyzed": 0,
"conclusions_confirmed": [],
"conclusions_revised": [],
"unexpected_findings": [],
"revised_action_items": []
}
}
6. Run Phase 5: Final Recommendations
Spawn Phase 5 agent with:
{
"skill_dir": "<skill_dir>",
"session_dir": "<SESSION_DIR>",
"reference_path": "<skill_dir>/references/05-phase-5.md",
"phase1_report_path": "<phase1_report_path>",
"phase2_report_path": "<phase2_report_path>",
"phase3_report_path": "<phase3_report_path>",
"phase4_report_path": "<phase4_report_path>"
}
Expected output:
{
"status": "complete",
"report_path": "<SESSION_DIR>/05-final-recommendations.md",
"recommendations_summary": {
"priority_actions": [],
"implementation_plan": [],
"success_metrics": []
}
}
Output (JSON Only)
{
"status": "complete",
"session_dir": "<SESSION_DIR>",
"timestamp": "YYYY-MM-DD_HHMMSS",
"phase_reports": {
"phase1": "<SESSION_DIR>/01-initial-schema-analysis.md",
"phase2": "<SESSION_DIR>/02-field-utilization-analysis.md",
"phase3": "<SESSION_DIR>/03-impact-assessment.md",
"phase4": "<SESSION_DIR>/04-field-utilization-verification.md",
"phase5": "<SESSION_DIR>/05-final-recommendations.md"
},
"final_summary": {
"total_tables": 0,
"total_fields": 0,
"unused_fields": 0,
"optimization_opportunities": 0,
"estimated_savings_pct": 0,
"verification_status": "confirmed"
}
}
Error Handling
If any phase fails:
- Stop execution
- Return error status with phase details
- Preserve partial reports for debugging
{
"status": "error",
"failed_phase": 3,
"error_message": "Phase 3 agent failed validation",
"session_dir": "<SESSION_DIR>",
"completed_phases": ["phase1", "phase2"]
}
Validation Rules
After each phase:
- Parse returned JSON (fail if invalid)
- Check
statusis "complete" (fail if not) - Verify
report_pathexists on disk (fail if not) - Validate phase-specific summary keys (fail if missing)
Implementation Notes
- Use Task tool to spawn phase agents
- Pass exact file paths (no wildcards)
- Session directory must be absolute path
- All reports must be written before returning
- No terminal output except final JSON
Example Usage
User: "Run schema optimization on my BigQuery export"
Claude: [Creates session directory]
Claude: [Spawns Phase 1 agent]
Claude: [Validates Phase 1 output]
Claude: [Spawns Phase 2 agent with Phase 1 report]
Claude: [... continues through Phase 5]
Claude: [Returns final JSON summary]
Files Created Per Run
reports/runs/2025-01-15_143022/
├── 01-initial-schema-analysis.md
├── 02-field-utilization-analysis.md
├── 03-impact-assessment.md
├── 04-field-utilization-verification.md
└── 05-final-recommendations.md
Each file is evidence of work completed.
When not to use it
- →If the input schema data is too small to justify an multi-phase workflow
- →When performing a one-off database change that does not require verification
- →If the environment lacks sufficient write permissions for session directory creation
Prerequisites
Limitations
- →Linear execution path is strictly bound by prior phase completion
- →Failure in one phase stops the entire chain
- →Rigidly coupled to the specific folder structure of the skill directory
How it compares
It creates a formal test harness for schema changes, moving away from ad-hoc analysis to a consistent, auditable multi-phase process.
Compared to similar skills
schema-optimization-orchestrator side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| schema-optimization-orchestrator (this skill) | 1 | 3mo | Review | Intermediate |
| database-design | 6 | 6mo | Review | Intermediate |
| database-schema-designer | 6 | 6mo | No flags | Intermediate |
| database-specialist | 0 | 5mo | No flags | Advanced |
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Example prompts that trigger this skill in your AI assistant.
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