← Back to the catalog 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
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[](https://www.skillteca.com.br/skills/data-catchall?utm_source=badge&utm_medium=readme&utm_campaign=badge) Copy snippet Use this skill for any task involving spreadsheet files as primary input or output, such as opening, reading, editing, fixing, creating, or converting .xlsx, .xlsm, .csv, or .tsv files.
Dados e Análise #xlsx by anthropics
Searches claude-mem's persistent cross-session memory database. Use this to answer questions about previous solutions or retrieve work from past sessions.
Dados e Análise #ai by thedotmack
Generates a week-by-week narrative digest of a project's Claude-mem timeline, splitting it into ISO-week files and using subagents to produce weekly chapters. Ideal for "weekly digests" or "narrative chapters" of a project's history.
Dados e Análise #ai by thedotmack
This skill explains how claude-mem captures observations, when memory injection occurs, and where its data is stored.
Dados e Análise #ai by thedotmack
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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.
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