cloud-data-infra

name

cloud-data-infra

description

Umbrella skill for where data pipelines run and what they cost — AWS/GCP/Azure infrastructure (S3/GCS/ADLS layout, BigQuery/Redshift/Snowflake selection, IAM, managed-service choice, performance tuning) and cost control (bytes scanned, partition pruning, slot/credit management, storage tiering, budgets and alerts). Use this whenever the topic is cloud data infrastructure or the cloud data bill. This skill ROUTES to the focused sub-skills (cloud-infra-data, cost-optimization-data) and pulls in both when a task spans architecture and cost. Trigger on: deploying a pipeline to cloud, choosing between managed warehouses, bucket/partition layout, IAM for data access, "our BigQuery bill jumped", expensive queries, or slot/credit utilization.

Cloud Data Infrastructure & Cost (Router)

This is a router skill. It groups the two skills covering cloud data infrastructure and its cost. Diagnose which sub-area(s) the task touches, then invoke the matching sub-skill(s) with the Skill tool.

How to route

If the task is about…	Invoke sub-skill
Provisioning and architecture: S3/GCS/ADLS partitioning, BigQuery slots, Redshift Spectrum, Snowflake warehouses, IAM/cross-account access, managed-service selection, cloud performance tuning	cloud-infra-data
The bill: query cost analysis, bytes scanned, partition pruning, slot reservation vs. on-demand, storage tiering, Snowflake credits, cost alerts and budgets	cost-optimization-data

Routing rules

• These two almost always travel together. A "slow BigQuery query" is both a performance (cloud-infra-data) and a cost (cost-optimization-data) problem — invoke both.
• "Our bill jumped" or a named expensive query → lead with cost-optimization-data (it starts by finding where the money goes), then cloud-infra-data for the structural fix.
• Choosing/standing up a warehouse or storage layout → lead with cloud-infra-data, then cost-optimization-data to price the choice.
• Invoke via the Skill tool by name, e.g. Skill(skill="cost-optimization-data"). Combine outputs; don't paraphrase from memory.

Related groups

• Pipelines deployed onto this infra → [[data-pipelines]]
• Query patterns that drive cost → [[data-modeling]]