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/cloud-storage-optimization

Optimize cloud storage across AWS S3, Azure Blob, and GCP Cloud Storage with compression, partitioning, lifecycle policies, and cost management.

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useful-ai-prompts
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Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill cloud-storage-optimization --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/cloud-storage-optimization

Context preview

The summary Claude sees to decide when to auto-load this skill.

Optimize cloud storage across AWS S3, Azure Blob, and GCP Cloud Storage with compression, partitioning, lifecycle policies, and cost management.

SKILL.md

cloud-storage-optimization.SKILL.md
name: cloud-storage-optimization
description: >
  Optimize cloud storage across AWS S3, Azure Blob, and GCP Cloud Storage with
  compression, partitioning, lifecycle policies, and cost management.

Cloud Storage Optimization

Table of Contents

  • [Overview](#overview)
  • [When to Use](#when-to-use)
  • [Quick Start](#quick-start)
  • [Reference Guides](#reference-guides)
  • [Best Practices](#best-practices)

Overview

Optimize cloud storage costs and performance across multiple cloud providers using compression, intelligent tiering, data partitioning, and lifecycle management. Reduce storage costs while maintaining accessibility and compliance requirements.

When to Use

  • Reducing storage costs
  • Optimizing data access patterns
  • Implementing tiered storage strategies
  • Archiving historical data
  • Improving data retrieval performance
  • Managing compliance requirements
  • Organizing large datasets
  • Optimizing data lakes and data warehouses

Quick Start

Minimal working example:

# Enable Intelligent-Tiering
aws s3api put-bucket-intelligent-tiering-configuration \
  --bucket my-bucket \
  --id OptimizedStorage \
  --intelligent-tiering-configuration '{
    "Id": "OptimizedStorage",
    "Filter": {"Prefix": "data/"},
    "Status": "Enabled",
    "Tierings": [
      {
        "Days": 90,
        "AccessTier": "ARCHIVE_ACCESS"
      },
      {
        "Days": 180,
        "AccessTier": "DEEP_ARCHIVE_ACCESS"
      }
    ]
  }'

# Analyze storage usage
aws s3api list-bucket-metrics-configurations --bucket my-bucket

# Enable S3 Select for cost optimization
aws s3api put-bucket-metrics-configuration \
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the `references/` directory:

| Guide | Contents | |---|---| | [AWS S3 Storage Optimization](references/aws-s3-storage-optimization.md) | AWS S3 Storage Optimization | | [Data Compression and Partitioning Strategy](references/data-compression-and-partitioning-strategy.md) | Data Compression and Partitioning Strategy | | [Terraform Multi-Cloud Storage Configuration](references/terraform-multi-cloud-storage-configuration.md) | Terraform Multi-Cloud Storage Configuration | | [Data Lake Partitioning Strategy](references/data-lake-partitioning-strategy.md) | Data Lake Partitioning Strategy |

Best Practices

✅ DO

  • Use Parquet or ORC formats for analytics
  • Implement tiered storage strategy
  • Partition data by time and queryable dimensions
  • Enable versioning for critical data
  • Use compression (gzip, snappy, brotli)
  • Monitor storage costs regularly
  • Implement data lifecycle policies
  • Archive infrequently accessed data

❌ DON'T

  • Store uncompressed data
  • Keep raw logs long-term
  • Ignore storage optimization
  • Use only hot storage tier
  • Store duplicate data
  • Forget to delete old test data
Read more
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