data-orchestrator

...

Updated Aug 23, 2026

Search Tags:#analytics #etl #data-quality #data-pipeline #orchestrate #data-governance #feature-engineering

Ready to run

npx skills add https://github.com/Brownbull/ayni_core --skill data-orchestrator-brownbull

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-orchestrator
Source: https://github.com/Brownbull/ayni_core/tree/main/.claude/skills/data-orchestrator
Command: npx skills add https://github.com/Brownbull/ayni_core --skill data-orchestrator-brownbull

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinates data pipeline tasks (ETL, analytics, feature engineering) across ingestion, transformation, validation, and governance to ensure reliable data flows and quality.

Core Features & Use Cases

End-to-end orchestration of ETL/ELT, analytics, and governance with pipeline monitoring
Quality and lineage tracking through data dictionaries and quality metrics
Context-driven task routing to the appropriate data skill (etl-skill, analytics-skill, feature-engineering-skill, quality-skill, pipeline-skill)

Quick Start

Instruct the orchestrator to ingest new data, run transformations, apply quality checks, and publish results to the data store.

Frequently Asked Questions about data-orchestrator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I orchestrate end-to-end data pipelines for ETL and analytics?▼

To orchestrate data pipelines end-to-end, you can coordinate tasks across ETL, analytics, and governance. This involves routing context to predefined data skills for ingestion, transformation, quality checks, and lineage tracking across batch and streaming workloads.

What is data pipeline orchestration and when do I need it for data governance?▼

Data pipeline orchestration coordinates end-to-end data processing tasks across ingestion, transformation, and governance. You need it to ensure reliable data flows, maintain context, apply quality checks, and track lineage across batch and streaming analytics workloads.

Can I run data quality checks and track lineage during pipeline orchestration?▼

Yes, data pipeline orchestration supports data quality checks and lineage tracking. It coordinates validation against standard adherence requirements and maintains data dictionaries and quality metrics throughout the ETL and analytics workflow.

How do I route data processing tasks to the correct ETL or feature engineering skill?▼

Data pipeline orchestration uses context-driven task routing to direct processing to the appropriate predefined data skill. It routes ingestion and transformation to ETL skills, validation to quality skills, and applies feature engineering based on the task context.

What's the best way to maintain context across batch and streaming data workloads?▼

The best way to maintain context across batch and streaming data workloads is using a data orchestrator. It coordinates end-to-end processing tasks, ensuring standard adherence and tracking data lineage throughout the analytics and governance lifecycle.

Does data orchestration work with both batch and streaming workloads?▼

Yes, data orchestration works with both batch and streaming workloads. It coordinates end-to-end data processing tasks across ETL, analytics, and governance, applying ingestion, transformation, and quality checks regardless of the workload type.