Cost Optimizer (Cloud Data Platforms)

Analyzes and optimizes costs for cloud data platforms

509 stars

Best use case

Cost Optimizer (Cloud Data Platforms) is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Analyzes and optimizes costs for cloud data platforms

Teams using Cost Optimizer (Cloud Data Platforms) should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/cost-optimizer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/data-engineering-analytics/skills/cost-optimizer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/cost-optimizer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Cost Optimizer (Cloud Data Platforms) Compares

Feature / AgentCost Optimizer (Cloud Data Platforms)Standard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Analyzes and optimizes costs for cloud data platforms

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

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