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Applies data-quality-standard.md (95% minimum).","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fbrownbull\u002Fdata-orchestrator\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"data orchestrator","orchestrator","data","feature","analytics","data-quality","coordinates","ingestion","use","quality","pipeline","engineering","feature engineering","data ingestion","etl","data pipeline","tasks","2026-11-10 10:12:30",false,"---\r\nname: data-orchestrator\r\ndescription: Coordinates data pipeline tasks (ETL, analytics, feature engineering). Use when implementing data ingestion, transformations, quality checks, or analytics. Applies data-quality-standard.md (95% minimum).\r\n---\r\n\r\n# Data Orchestrator Skill\r\n\r\n## Role\r\nActs as CTO-Data, managing all data processing, analytics, and pipeline tasks.\r\n\r\n## Responsibilities\r\n\r\n1. **Data Pipeline Management**\r\n   - ETL\u002FELT processes\r\n   - Data validation\r\n   - Quality assurance\r\n   - Pipeline monitoring\r\n\r\n2. **Analytics Coordination**\r\n   - Feature engineering\r\n   - Model integration\r\n   - Report generation\r\n   - Metric calculation\r\n\r\n3. **Data Governance**\r\n   - Schema management\r\n   - Data lineage tracking\r\n   - Privacy compliance\r\n   - Access control\r\n\r\n4. **Context Maintenance**\r\n   ```\r\n   ai-state\u002Factive\u002Fdata\u002F\r\n   ├── pipelines.json    # Pipeline definitions\r\n   ├── features.json     # Feature registry\r\n   ├── quality.json      # Data quality metrics\r\n   └── tasks\u002F           # Active data tasks\r\n   ```\r\n\r\n## Skill Coordination\r\n\r\n### Available Data Skills\r\n- `etl-skill` - Extract, transform, load operations\r\n- `feature-engineering-skill` - Feature creation\r\n- `analytics-skill` - Analysis and reporting\r\n- `quality-skill` - Data quality checks\r\n- `pipeline-skill` - Pipeline orchestration\r\n\r\n### Context Package to Skills\r\n```yaml\r\ncontext:\r\n  task_id: \"task-003-pipeline\"\r\n  pipelines:\r\n    existing: [\"daily_aggregation\", \"customer_segmentation\"]\r\n    schedule: \"0 2 * * *\"\r\n  features:\r\n    current: [\"revenue_30d\", \"churn_risk\"]\r\n    dependencies: [\"transactions\", \"customers\"]\r\n  standards:\r\n    - \"data-quality-standard.md\"\r\n    - \"feature-engineering.md\"\r\n  test_requirements:\r\n    quality: [\"completeness\", \"accuracy\", \"timeliness\"]\r\n```\r\n\r\n## Task Processing Flow\r\n\r\n1. **Receive Task**\r\n   - Identify data sources\r\n   - Check dependencies\r\n   - Validate requirements\r\n\r\n2. **Prepare Context**\r\n   - Current pipeline state\r\n   - Feature definitions\r\n   - Quality metrics\r\n\r\n3. **Assign to Skill**\r\n   - Choose data skill\r\n   - Set parameters\r\n   - Define outputs\r\n\r\n4. **Monitor Execution**\r\n   - Track pipeline progress\r\n   - Monitor resource usage\r\n   - Check quality gates\r\n\r\n5. **Validate Results**\r\n   - Data quality checks\r\n   - Output validation\r\n   - Performance metrics\r\n   - Lineage tracking\r\n\r\n## Data-Specific Standards\r\n\r\n### Pipeline Checklist\r\n- [ ] Input validation\r\n- [ ] Error handling\r\n- [ ] Checkpoint\u002Frecovery\r\n- [ ] Monitoring enabled\r\n- [ ] Documentation updated\r\n- [ ] Performance optimized\r\n\r\n### Quality Checklist\r\n- [ ] Completeness checks\r\n- [ ] Accuracy validation\r\n- [ ] Consistency rules\r\n- [ ] Timeliness metrics\r\n- [ ] Uniqueness constraints\r\n- [ ] Validity ranges\r\n\r\n### Feature Engineering Checklist\r\n- [ ] Business logic documented\r\n- [ ] Dependencies tracked\r\n- [ ] Version controlled\r\n- [ ] Performance tested\r\n- [ ] Edge cases handled\r\n- [ ] Monitoring added\r\n\r\n## Integration Points\r\n\r\n### With Backend Orchestrator\r\n- Data model alignment\r\n- API data contracts\r\n- Database optimization\r\n- Cache strategies\r\n\r\n### With Frontend Orchestrator\r\n- Dashboard data requirements\r\n- Real-time vs batch\r\n- Data freshness SLAs\r\n- Visualization formats\r\n\r\n### With Human-Docs\r\nUpdates documentation with:\r\n- Pipeline changes\r\n- Feature definitions\r\n- Data dictionaries\r\n- Quality reports\r\n\r\n## Event Communication\r\n\r\n### Listening For\r\n```json\r\n{\r\n  \"event\": \"data.source.updated\",\r\n  \"source\": \"transactions\",\r\n  \"schema_change\": true,\r\n  \"impact\": [\"daily_pipeline\", \"revenue_features\"]\r\n}\r\n```\r\n\r\n### Broadcasting\r\n```json\r\n{\r\n  \"event\": \"data.pipeline.completed\",\r\n  \"pipeline\": \"daily_aggregation\",\r\n  \"records_processed\": 50000,\r\n  \"duration\": \"5m 32s\",\r\n  \"quality_score\": 98.5\r\n}\r\n```\r\n\r\n## Test Requirements\r\n\r\n### Every Data Task Must Include\r\n1. **Unit Tests** - Transformation logic\r\n2. **Integration Tests** - Pipeline flow\r\n3. **Data Quality Tests** - Accuracy, completeness\r\n4. **Performance Tests** - Processing speed\r\n5. **Edge Case Tests** - Null, empty, invalid data\r\n6. **Regression Tests** - Output consistency\r\n\r\n## Success Metrics\r\n\r\n- Pipeline success rate > 99%\r\n- Data quality score > 95%\r\n- Processing time \u003C SLA\r\n- Zero data loss\r\n- Feature coverage > 90%\r\n\r\n## Common Patterns\r\n\r\n### ETL Pattern\r\n```python\r\nclass ETLOrchestrator:\r\n    def run_pipeline(self, task):\r\n        # 1. Extract from sources\r\n        # 2. Validate input data\r\n        # 3. Transform data\r\n        # 4. Quality checks\r\n        # 5. Load to destination\r\n        # 6. Update lineage\r\n```\r\n\r\n### Feature Pattern\r\n```python\r\nclass FeatureOrchestrator:\r\n    def create_feature(self, task):\r\n        # 1. Define feature logic\r\n        # 2. Identify dependencies\r\n        # 3. Implement calculation\r\n        # 4. Add to feature store\r\n        # 5. Create monitoring\r\n```\r\n\r\n## Data Processing Guidelines\r\n\r\n### Batch Processing\r\n- Use for large volumes\r\n- Schedule during off-peak\r\n- Implement checkpointing\r\n- Monitor resource usage\r\n\r\n### Stream Processing\r\n- Use for real-time needs\r\n- Implement windowing\r\n- Handle late arrivals\r\n- Maintain state\r\n\r\n### Data Quality Rules\r\n1. **Completeness** - No missing required fields\r\n2. **Accuracy** - Values within expected ranges\r\n3. **Consistency** - Cross-dataset alignment\r\n4. **Timeliness** - Data freshness requirements\r\n5. **Uniqueness** - No unwanted duplicates\r\n6. **Validity** - Format and type correctness\r\n\r\n## Anti-Patterns to Avoid\r\n\r\n❌ Processing without validation\r\n❌ No error recovery mechanism\r\n❌ Missing data lineage\r\n❌ Hardcoded transformations\r\n❌ No monitoring\u002Falerting\r\n❌ Manual intervention required","# Data Orchestrator Skill\r\n\r\n## Role\r\nActs as CTO-Data, managing all data processing, analytics, and pipeline tasks.\r\n\r\n## Responsibilities\r\n\r\n1. **Data Pipeline Management**\r\n   - ETL\u002FELT processes\r\n   - Data validation\r\n   - Quality assurance\r\n   - Pipeline monitoring\r\n\r\n2. **Analytics Coordination**\r\n   - Feature engineering\r\n   - Model integration\r\n   - Report generation\r\n   - Metric calculation\r\n\r\n3. **Data Governance**\r\n   - Schema management\r\n   - Data lineage tracking\r\n   - Privacy compliance\r\n   - Access control\r\n\r\n4. **Context Maintenance**\r\n   ```\r\n   ai-state\u002Factive\u002Fdata\u002F\r\n   ├── pipelines.json    # Pipeline definitions\r\n   ├── features.json     # Feature registry\r\n   ├── quality.json      # Data quality metrics\r\n   └── tasks\u002F           # Active data tasks\r\n   ```\r\n\r\n## Skill Coordination\r\n\r\n### Available Data Skills\r\n- `etl-skill` - Extract, transform, load operations\r\n- `feature-engineering-skill` - Feature creation\r\n- `analytics-skill` - Analysis and reporting\r\n- `quality-skill` - Data quality checks\r\n- `pipeline-skill` - Pipeline orchestration\r\n\r\n### Context Package to Skills\r\n```yaml\r\ncontext:\r\n  task_id: \"task-003-pipeline\"\r\n  pipelines:\r\n    existing: [\"daily_aggregation\", \"customer_segmentation\"]\r\n    schedule: \"0 2 * * *\"\r\n  features:\r\n    current: [\"revenue_30d\", \"churn_risk\"]\r\n    dependencies: [\"transactions\", \"customers\"]\r\n  standards:\r\n    - \"data-quality-standard.md\"\r\n    - \"feature-engineering.md\"\r\n  test_requirements:\r\n    quality: [\"completeness\", \"accuracy\", \"timeliness\"]\r\n```\r\n\r\n## Task Processing Flow\r\n\r\n1. **Receive Task**\r\n   - Identify data sources\r\n   - Check dependencies\r\n   - Validate requirements\r\n\r\n2. **Prepare Context**\r\n   - Current pipeline state\r\n   - Feature definitions\r\n   - Quality metrics\r\n\r\n3. **Assign to Skill**\r\n   - Choose data skill\r\n   - Set parameters\r\n   - Define outputs\r\n\r\n4. **Monitor Execution**\r\n   - Track pipeline progress\r\n   - Monitor resource usage\r\n   - Check quality gates\r\n\r\n5. **Validate Results**\r\n   - Data quality checks\r\n   - Output validation\r\n   - Performance metrics\r\n   - Lineage tracking\r\n\r\n## Data-Specific Standards\r\n\r\n### Pipeline Checklist\r\n- [ ] Input validation\r\n- [ ] Error handling\r\n- [ ] Checkpoint\u002Frecovery\r\n- [ ] Monitoring enabled\r\n- [ ] Documentation updated\r\n- [ ] Performance optimized\r\n\r\n### Quality Checklist\r\n- [ ] Completeness checks\r\n- [ ] Accuracy validation\r\n- [ ] Consistency rules\r\n- [ ] Timeliness metrics\r\n- [ ] Uniqueness constraints\r\n- [ ] Validity ranges\r\n\r\n### Feature Engineering Checklist\r\n- [ ] Business logic documented\r\n- [ ] Dependencies tracked\r\n- [ ] Version controlled\r\n- [ ] Performance tested\r\n- [ ] Edge cases handled\r\n- [ ] Monitoring added\r\n\r\n## Integration Points\r\n\r\n### With Backend Orchestrator\r\n- Data model alignment\r\n- API data contracts\r\n- Database optimization\r\n- Cache strategies\r\n\r\n### With Frontend Orchestrator\r\n- Dashboard data requirements\r\n- Real-time vs batch\r\n- Data freshness SLAs\r\n- Visualization formats\r\n\r\n### With Human-Docs\r\nUpdates documentation with:\r\n- Pipeline changes\r\n- Feature definitions\r\n- Data dictionaries\r\n- Quality reports\r\n\r\n## Event Communication\r\n\r\n### Listening For\r\n```json\r\n{\r\n  \"event\": \"data.source.updated\",\r\n  \"source\": \"transactions\",\r\n  \"schema_change\": true,\r\n  \"impact\": [\"daily_pipeline\", \"revenue_features\"]\r\n}\r\n```\r\n\r\n### Broadcasting\r\n```json\r\n{\r\n  \"event\": \"data.pipeline.completed\",\r\n  \"pipeline\": \"daily_aggregation\",\r\n  \"records_processed\": 50000,\r\n  \"duration\": \"5m 32s\",\r\n  \"quality_score\": 98.5\r\n}\r\n```\r\n\r\n## Test Requirements\r\n\r\n### Every Data Task Must Include\r\n1. **Unit Tests** - Transformation logic\r\n2. **Integration Tests** - Pipeline flow\r\n3. **Data Quality Tests** - Accuracy, completeness\r\n4. **Performance Tests** - Processing speed\r\n5. **Edge Case Tests** - Null, empty, invalid data\r\n6. **Regression Tests** - Output consistency\r\n\r\n## Success Metrics\r\n\r\n- Pipeline success rate > 99%\r\n- Data quality score > 95%\r\n- Processing time \u003C SLA\r\n- Zero data loss\r\n- Feature coverage > 90%\r\n\r\n## Common Patterns\r\n\r\n### ETL Pattern\r\n```python\r\nclass ETLOrchestrator:\r\n    def run_pipeline(self, task):\r\n        # 1. Extract from sources\r\n        # 2. Validate input data\r\n        # 3. Transform data\r\n        # 4. Quality checks\r\n        # 5. Load to destination\r\n        # 6. Update lineage\r\n```\r\n\r\n### Feature Pattern\r\n```python\r\nclass FeatureOrchestrator:\r\n    def create_feature(self, task):\r\n        # 1. Define feature logic\r\n        # 2. Identify dependencies\r\n        # 3. Implement calculation\r\n        # 4. Add to feature store\r\n        # 5. Create monitoring\r\n```\r\n\r\n## Data Processing Guidelines\r\n\r\n### Batch Processing\r\n- Use for large volumes\r\n- Schedule during off-peak\r\n- Implement checkpointing\r\n- Monitor resource usage\r\n\r\n### Stream Processing\r\n- Use for real-time needs\r\n- Implement windowing\r\n- Handle late arrivals\r\n- Maintain state\r\n\r\n### Data Quality Rules\r\n1. **Completeness** - No missing required fields\r\n2. **Accuracy** - Values within expected ranges\r\n3. **Consistency** - Cross-dataset alignment\r\n4. **Timeliness** - Data freshness requirements\r\n5. **Uniqueness** - No unwanted duplicates\r\n6. **Validity** - Format and type correctness\r\n\r\n## Anti-Patterns to Avoid\r\n\r\n❌ Processing without validation\r\n❌ No error recovery mechanism\r\n❌ Missing data lineage\r\n❌ Hardcoded transformations\r\n❌ No monitoring\u002Falerting\r\n❌ Manual intervention 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