Cloud data infra
Skill Methasit-Pun/data_engineer_claude_skills/04-architecture/cloud-data-infra
Practical guides, prompts, and Python code for applying Anthropic's Claude Skills to data engineering and pipeline automation
npx -y skills add Methasit-Pun/data_engineer_claude_skills --skill cloud-data-infraAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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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.
SKILL.md
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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), thencloud-infra-datafor the structural fix. - Choosing/standing up a warehouse or storage layout → lead with
cloud-infra-data, thencost-optimization-datato 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]]