Coordinate a virtual data team by delegating technical work to specialized sub-agents.
npx skills add https://github.com/raphaelVRA/company_agents --skill data-team
Please help me install this Agent Skill. Skill: data-team Source: https://github.com/raphaelVRA/company_agents/tree/main/data-team Command: npx skills add https://github.com/raphaelVRA/company_agents --skill data-team
Transform this session into an autonomous Chief Data Officer (CDO) leading a complete virtual data team. You delegate all data engineering, analysis, modeling, and visualization work to specialized sub-agents. You use adversarial review for ML models (two approaches compared before recommendation). You NEVER write SQL, code, or analysis yourself — you strategize, delegate, validate, and decide.
Invoke /data-team to assemble the team and begin execution.
High-intent search queries and answers about installing and using this skill.
You can orchestrate end-to-end data team delivery by delegating data engineering, analytics, modeling, and dashboard tasks to specialized sub-agents. This skill coordinates parallel execution while maintaining executive governance and data quality gates for auditable delivery.
Adversarial review is a validation process where two machine learning modeling approaches are compared against each other before a final recommendation. This skill enforces this review to evaluate explicit trade-offs and ensure robust model selection.
Yes, you can manage dbt workflows and data quality governance by delegating operational ETL and modeling tasks to sub-agents. The skill enforces data quality gates and generates governance artifacts while you strategize and validate outputs.
No, you never write SQL, code, or analysis yourself when managing this virtual data team. You act as a Chief Data Officer, strategizing and delegating all technical work to specialized sub-agents while retaining executive decision rights.
You assemble a cross-functional data team by invoking the skill to define explicit team composition for your specific data problem. It then coordinates specialized sub-agents for parallel execution across engineering, modeling, and visualization.
The primary limitation is that you must define explicit team composition and structured execution protocols upfront. Delivery depends on the sub-agents' capabilities, meaning you must actively validate outputs and enforce governance artifacts rather than expecting fully autonomous execution.
drbsclOrchestrate agent teams to execute user-defined tasks with pre-execution reviews.
sakamoto-family-smileTransform notebook experiments into production ML pipelines with data contracts and governance.
Patasse97Design and orchestrate data pipelines with Python, SQL, Spark, Airflow, and dbt.
ClyptAIDispatch independent tasks to specialized agents with isolated context.
