Cost Optimizer (Cloud Data Platforms)

DevOps & Security

Cost Optimizer (Cloud Data Platforms) is an Agent Skill for devops & security published in the a5c-ai/babysitter repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Analyzes and optimizes costs for cloud data platforms

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QUICK START

How to use this skill

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Prompt to paste

Inspect the Agent Skill at https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/data-engineering-analytics/skills/cost-optimizer/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/cost-optimizer-cloud-data-platforms, but wait for my approval before writing files or running scripts.

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Cost Optimizer (Cloud Data Platforms)

Overview

Analyzes and optimizes costs for cloud data platforms. This skill provides deep expertise in platform-specific cost structures and optimization strategies.

Capabilities

Snowflake credit analysis and optimization
BigQuery slot and on-demand optimization
Redshift node sizing
Storage cost optimization
Query cost estimation
Warehouse scheduling recommendations
Data lifecycle policy recommendations
Reserved capacity planning

Input Schema

{
  "platform": "snowflake|bigquery|redshift|databricks",
  "usageMetrics": "object",
  "billingData": "object",
  "queryHistory": "object"
}

Output Schema

{
  "currentCost": "number",
  "optimizedCost": "number",
  "savings": "percentage",
  "recommendations": [{
    "category": "string",
    "action": "string",
    "impact": "number",
    "effort": "low|medium|high"
  }]
}

Target Processes

Data Warehouse Setup
Query Optimization
Pipeline Migration

Usage Guidelines

Provide platform-specific usage metrics
Include billing data for cost baseline
Share query history for optimization analysis
Prioritize recommendations by impact and effort

Best Practices

Regularly review and optimize warehouse sizes
Implement auto-suspend and auto-resume policies
Use clustering and partitioning to reduce scan costs
Consider reserved capacity for predictable workloads
Monitor and alert on cost anomalies