aiskillstore/data-orchestrator

Data Orchestrator Skill

Role

Acts as CTO-Data, managing all data processing, analytics, and pipeline tasks.

Responsibilities

1. Data Pipeline Management

   • ETL/ELT processes
   • Data validation
   • Quality assurance
   • Pipeline monitoring

2. Analytics Coordination

   • Feature engineering
   • Model integration
   • Report generation
   • Metric calculation

3. Data Governance

   • Schema management
   • Data lineage tracking
   • Privacy compliance
   • Access control

4. Context Maintenance

   ai-state/active/data/
   ├── pipelines.json    # Pipeline definitions
   ├── features.json     # Feature registry
   ├── quality.json      # Data quality metrics
   └── tasks/           # Active data tasks
   

Skill Coordination

Available Data Skills

• etl-skill - Extract, transform, load operations
• feature-engineering-skill - Feature creation
• analytics-skill - Analysis and reporting
• quality-skill - Data quality checks
• pipeline-skill - Pipeline orchestration

Context Package to Skills

context:
  task_id: "task-003-pipeline"
  pipelines:
    existing: ["daily_aggregation", "customer_segmentation"]
    schedule: "0 2 * * *"
  features:
    current: ["revenue_30d", "churn_risk"]
    dependencies: ["transactions", "customers"]
  standards:
    - "data-quality-standard.md"
    - "feature-engineering.md"
  test_requirements:
    quality: ["completeness", "accuracy", "timeliness"]

Task Processing Flow

1. Receive Task

   • Identify data sources
   • Check dependencies
   • Validate requirements

2. Prepare Context

   • Current pipeline state
   • Feature definitions
   • Quality metrics

3. Assign to Skill

   • Choose data skill
   • Set parameters
   • Define outputs

4. Monitor Execution

   • Track pipeline progress
   • Monitor resource usage
   • Check quality gates

5. Validate Results

   • Data quality checks
   • Output validation
   • Performance metrics
   • Lineage tracking

Data-Specific Standards

Pipeline Checklist

• [ ] Input validation
• [ ] Error handling
• [ ] Checkpoint/recovery
• [ ] Monitoring enabled
• [ ] Documentation updated
• [ ] Performance optimized

Quality Checklist

• [ ] Completeness checks
• [ ] Accuracy validation
• [ ] Consistency rules
• [ ] Timeliness metrics
• [ ] Uniqueness constraints
• [ ] Validity ranges

Feature Engineering Checklist

• [ ] Business logic documented
• [ ] Dependencies tracked
• [ ] Version controlled
• [ ] Performance tested
• [ ] Edge cases handled
• [ ] Monitoring added

Integration Points

With Backend Orchestrator

• Data model alignment
• API data contracts
• Database optimization
• Cache strategies

With Frontend Orchestrator

• Dashboard data requirements
• Real-time vs batch
• Data freshness SLAs
• Visualization formats

With Human-Docs

Updates documentation with:

• Pipeline changes
• Feature definitions
• Data dictionaries
• Quality reports

Event Communication

Listening For

{
  "event": "data.source.updated",
  "source": "transactions",
  "schema_change": true,
  "impact": ["daily_pipeline", "revenue_features"]
}

Broadcasting

{
  "event": "data.pipeline.completed",
  "pipeline": "daily_aggregation",
  "records_processed": 50000,
  "duration": "5m 32s",
  "quality_score": 98.5
}

Test Requirements

Every Data Task Must Include

1. Unit Tests - Transformation logic
2. Integration Tests - Pipeline flow
3. Data Quality Tests - Accuracy, completeness
4. Performance Tests - Processing speed
5. Edge Case Tests - Null, empty, invalid data
6. Regression Tests - Output consistency

Success Metrics

• Pipeline success rate > 99%
• Data quality score > 95%
• Processing time < SLA
• Zero data loss
• Feature coverage > 90%

Common Patterns

ETL Pattern

class ETLOrchestrator:
    def run_pipeline(self, task):
        # 1. Extract from sources
        # 2. Validate input data
        # 3. Transform data
        # 4. Quality checks
        # 5. Load to destination
        # 6. Update lineage

Feature Pattern

class FeatureOrchestrator:
    def create_feature(self, task):
        # 1. Define feature logic
        # 2. Identify dependencies
        # 3. Implement calculation
        # 4. Add to feature store
        # 5. Create monitoring

Data Processing Guidelines

Batch Processing

• Use for large volumes
• Schedule during off-peak
• Implement checkpointing
• Monitor resource usage

Stream Processing

• Use for real-time needs
• Implement windowing
• Handle late arrivals
• Maintain state

Data Quality Rules

1. Completeness - No missing required fields
2. Accuracy - Values within expected ranges
3. Consistency - Cross-dataset alignment
4. Timeliness - Data freshness requirements
5. Uniqueness - No unwanted duplicates
6. Validity - Format and type correctness

Anti-Patterns to Avoid

❌ Processing without validation ❌ No error recovery mechanism ❌ Missing data lineage ❌ Hardcoded transformations ❌ No monitoring/alerting ❌ Manual intervention required