Data catchall
Skill Chipagosfinest/enterprise-team/enterprise-team/skills/data-catchall
Hire a whole company with one plugin. 75 specialized AI agents across Engineering, Product, Infrastructure, Data, Security, Marketing, Sales, Finance, Legal, and People.
npx -y skills add Chipagosfinest/enterprise-team --skill data-catchallAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 18 stars18 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Routes data requests to the correct specialist based on task type: pipelines, analytics, machine learning, or quality assurance. Covers ETL, warehousing, dashboards, ML models, and test automation. Triggers: data pipeline, etl, data warehouse, analytics, dashboard, metrics, kpi, testing, test automation, qa, quality assurance, ci/cd testing, data science, machine learning, sql, dbt, airflow, spark.
SKILL.md
1.8 KB, as published. Nobody here has run it
Data Department
Routes data work to the appropriate specialist role.
Routing Targets
| Role | Handles |
|---|---|
| data-engineer | ETL pipelines, data warehouses, Airflow, dbt, Spark, data infrastructure |
| data-analyst | Dashboards, BI reports, SQL queries, KPIs, metrics, data exploration |
| data-scientist | ML models, statistical analysis, predictive analytics, experiments |
| qa-engineer | Test automation, CI/CD testing, data quality validation, regression testing |
Examples
- "Build an ETL pipeline to sync Stripe data to our warehouse" -> data-engineer
- "Create a dashboard showing monthly revenue by product" -> data-analyst
- "Train a churn prediction model on our user data" -> data-scientist
- "Set up automated data quality checks for the pipeline" -> qa-engineer
- "Migrate our data warehouse from Redshift to BigQuery" -> data-engineer
- "Analyze conversion funnel drop-off rates" -> data-analyst
Workflow
- Identify whether the request is about data infrastructure, analysis, modeling, or testing.
- For requests spanning multiple areas (e.g., "build pipeline + dashboard"), route to the upstream role first (data-engineer before data-analyst).
- For ambiguous data requests, default to data-analyst.
- For ML/AI requests that are more about deployment than modeling, route to ml-developer via engineering-orchestrator.