# Cost Optimizer (Cloud Data Platforms)

> Analyzes and optimizes costs for cloud data platforms

- Skill: `majiayu000/cost-optimizer-cloud-data-platforms` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/cost-optimizer-cloud-data-platforms`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/cost-optimizer-cloud-data-platforms/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/cost-optimizer-cloud-data-platforms

---


# 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

```json
{
  "platform": "snowflake|bigquery|redshift|databricks",
  "usageMetrics": "object",
  "billingData": "object",
  "queryHistory": "object"
}
```

## Output Schema

```json
{
  "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

1. Provide platform-specific usage metrics
2. Include billing data for cost baseline
3. Share query history for optimization analysis
4. 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

