Individual skill
castai-cost-tuning
Analyze CAST AI cost, available-savings, and realized-savings reports into a defensible optimization plan. Use when investigating spend, validating savings claims, applying private-price adjustments, or prioritizing workloads and clusters. Trigger with: "tune CAST AI costs", "verify CAST AI savings", "reduce Kubernetes spend with CAST AI".
skills/.curated/castai-cost-tuning/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Last checked
- Nov 12, 2026
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- Repository stars (not skill ratings)
- 2,917
- Repository last updated
- Nov 12, 2026
- License
- MIT
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- saas Declared skill tag
- kubernetes Declared skill tag
- cast-ai Declared skill tag
- finops Declared skill tag
- cost-optimization Declared skill tag
- devops Derived category
- devops-containers Derived category
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SKILL.mdcore · 4,648 bytesSKILL.mdcore · 4,648 bytesSKILL.mdcore · 4,648 bytesSKILL.mdcore · 4,648 bytesreferences/official-docs.mddoc · 968 bytes
Read excerpt
# CAST AI Cost Evidence Review
## Overview
Turn reporting into a prioritized plan without treating modeled savings as invoices. Separate actual spend, available opportunity, realized savings, workload rightsizing, adoption, pricing, and baseline assumptions.
## Prerequisites
- The organization and cluster reports for a declared time range
- Cloud billing or internal allocation evidence for reconciliation
- Current automation adoption, workload SLOs, and pricing-adjustment ownership
## Instructions
