castai-cost-tuning

v2026.09.24

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".

GitHub
安装命令
npx skhub add jeremylongshore/castai-cost-tuning
Markdown
SKILL.md

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

Step 1: Normalize the question

Use Read to identify whether the request concerns actual spend, a forecast, available savings, realized savings, or workload autoscaler savings. Record cluster scope, currency, time range, and comparison period.

Step 2: Validate the reporting basis

Use Grep across exported reports and runbooks to find baseline source, public versus adjusted prices, data gaps, and adoption thresholds. Cost comparison needs sufficient history; new clusters and low automation adoption can legitimately show incomplete or zero savings.

Step 3: Reconcile cost layers

Compare provisioned resources, requested resources, utilization, lifecycle mix, price per resource, actual cost, and modeled baseline. When private discounts or commitments matter, require reviewed Price adjustments instead of assuming public list prices match the bill.

Step 4: Rank opportunities by constraint

Group opportunities into workload rightsizing, node bin-packing, spot or fallback strategy, architecture choice, idle capacity, and allocation hygiene. For each, include savings confidence, SLO risk, prerequisite, owner, and evidence window.

Step 5: Design a measured experiment

Use Write or Edit to create a canary hypothesis with a single policy change, expected capacity effect, performance guardrail, measurement window, and rollback. Do not combine node, vertical, horizontal, and pricing changes in one experiment.

Step 6: Produce the decision record

State what CAST AI reports, what billing evidence confirms, what remains modeled, and which action is authorized. Preserve before-and-after snapshots without exporting sensitive workload names beyond their approved audience.

Tool Discipline

Use Read for reports, billing extracts, and policy context. Use Grep to reconcile repeated cluster, workload, baseline, and pricing facts. Use Write and Edit only for the analysis, experiment, and decision record; this skill does not enable automation.

Output

  • Scope and reporting-basis statement
  • Reconciled spend and savings table
  • Ranked opportunities with confidence and risk
  • One controlled experiment and rollback threshold

Examples

A report shows high available savings but no realized savings because the cluster remains read-only. Another cluster shows modeled workload savings, but private prices are absent, so the team configures reviewed adjustments before using the number for a commitment.

Error Handling

FailureResponse
Baseline source is unknownLabel savings unverified and obtain the report basis
CAST AI and invoice periods differNormalize the window before comparison
Private pricing is missingUse Price adjustments or disclose list-price limitation
Optimization conflicts with an SLOReject the action regardless of modeled savings

Resources

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版本
最新版本元数据

版本

v2026.09.24

发布时间

2026年9月24日

分类

未分类

许可证

MIT

源路径

skills/.curated/castai-cost-tuning

默认分支

main

最新提交

e5a6c3b

Tree SHA

c2dc8e8