schema-optimization-orchestrator
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"
Best use case
schema-optimization-orchestrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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"
Teams using schema-optimization-orchestrator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/schema-optimization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How schema-optimization-orchestrator Compares
| Feature / Agent | schema-optimization-orchestrator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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"
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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)
```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
```bash
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:
```json
{
"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:
```json
{
"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
- `status` is "complete"
- `report_path` file exists
- `schema_summary` has required keys
### 3. Run Phase 2: Field Utilization Analysis
Spawn Phase 2 agent with:
```json
{
"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:
```json
{
"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:
```json
{
"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:
```json
{
"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:
```json
{
"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:
```json
{
"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:
```json
{
"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:
```json
{
"status": "complete",
"report_path": "<SESSION_DIR>/05-final-recommendations.md",
"recommendations_summary": {
"priority_actions": [],
"implementation_plan": [],
"success_metrics": []
}
}
```
## Output (JSON Only)
```json
{
"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
```json
{
"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:
1. Parse returned JSON (fail if invalid)
2. Check `status` is "complete" (fail if not)
3. Verify `report_path` exists on disk (fail if not)
4. 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
```
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