Agent skill · data analytics · aiskillstore
data-orchestrator
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).
Why this skill is useful
Provides specific workflows and standards for managing data pipelines, quality checks, and analytics that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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 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 …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/data-orchestrator