Agent skill · data analytics · jeremylongshore
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".
Why this skill is useful
Provides specific API commands and strategies for optimizing Kubernetes costs with CAST AI that the AI wouldn't reliably generate on its own.
What it needs
Requires curl, jq installed locally. Requires castai account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/castai-cost-tuning