Aks Cost Optimization

From the author

Analyze advanced AKS cost efficiency. WHEN: "AKS NAP readiness", "AKS bin packing", "AKS node scaling bottleneck", "AKS blocked scale-down", "AKS storage cost". DO NOT USE AS DIRECT ENTRY FOR: overall AKS cost assessments or rightsizing/VPA/CAS/Spot (azure-kubernetes); spend/anomalies/commitments (cost-analysis/cost-optimization); incidents (aks-troubleshooting).

— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured workflow for analyzing advanced cost efficiency in Azure Kubernetes Service (AKS) clusters. It focuses on technical placement and scheduling factors rather than high-level financial reporting. The skill guides users through evaluating Node Auto-Provisioning (NAP) readiness, bin-packing efficiency, node scaling bottlenecks, and storage costs. It is designed to be used for specific technical investigations, requiring the user to route broader cost or incident-related queries to their respective specialized skills.

What it does

Evaluates cluster bin-packing efficiency and identifies consolidation candidates.
Assesses readiness for adopting Node Auto-Provisioning (NAP).
Identifies root causes for node scaling bottlenecks and blocked scale-down events.
Analyzes storage cost and mapping configurations.
Provides evidence-based recommendations for pool adjustments or placement changes.
Requires explicit user approval before any cluster configuration changes.

When to use it

When investigating inefficient resource utilization or poor bin-packing in an AKS cluster.
When evaluating whether to migrate from fixed node pools or Cluster Autoscaler to NAP.
When troubleshooting persistent node scaling bottlenecks or blocked scale-down operations.
When analyzing storage-related costs within an AKS environment.

Advantages

Provides deep technical insights into scheduling and placement constraints.
Prevents conflicting recommendations by routing to specialized skills for financial or incident data.
Ensures safe operations by requiring manual approval for all proposed changes.
Uses standardized evidence collection for consistent analysis across clusters.

Usage examples

“Analyze why my AKS cluster is failing to scale down nodes.”: The agent will investigate blocked scale-down events, check for PDBs or disruption budgets, and identify if the nodes are managed by NAP or CAS to provide a resolution path.
“Is my cluster ready for Node Auto-Provisioning?”: The agent will perform an eligibility check, verify current networking and identity configurations, and report if NAP is a viable and cost-effective alternative for your workload.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/aks-cost-optimization/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip aks-cost-optimization.zip -d .agents/skills

Documentation by Microsoft

When to Use This Skill

Use for advanced existing-cluster analysis requiring scheduling, placement, or verified storage mapping. Route overall AKS cost assessments to azure-kubernetes, spend/anomalies to cost-analysis, commitments to cost-optimization, and incidents to aks-troubleshooting when those skills are available. When called from an overall assessment, return findings for its ranked report.

MCP Tools

Prefer fitting Azure MCP reads. Never invent tools or use az/kubectl without approved shell access and a target-bound context.

Workflow/Steps

Bind the subscription, cluster, resource group, and kube context.
Route to NAP readiness, bin-packing, node-scaling bottlenecks, or storage optimization.
When invoked for AKS commitment context, use the commitment-demand handoff and return demand evidence without benefit analysis or a transaction.
Keep reads non-mutating; require approval for every change.

Error Handling

Missing capability: analyze supplied evidence or provide a collection plan; mark dependent cost or commitment results unavailable.
Evidence conflict: mark the conclusion unconfirmed.

Files

ATTRIBUTION.mdAdded by AutoGineer989 B
LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md1.8 KiB
references/aks-bin-packing.md3.8 KiB
references/aks-nap.md3.2 KiB
references/aks-savings-plans-reservations.md3.5 KiB
references/aks-storage-optimization.md3.5 KiB
references/bin-packing-evidence.md3.8 KiB
references/nap-decisions.md3.3 KiB
references/nap-enablement-and-rollback.md1.8 KiB
references/nap-evidence-collection.md3.9 KiB
references/node-scaling-bottlenecks.md3.8 KiB
references/self-hosted-karpenter-migration.md2.1 KiB
references/storage-container-insights.md2.9 KiB
references/storage-decisions.md3.3 KiB
references/storage-metrics.md3.7 KiB

references/aks-savings-plans-reservations.md

AKS Commitment-Demand Handoff

Prepare stable, post-optimization AKS compute demand for cost-optimization, which owns Reservation and Savings Plan analysis. Direct commitment requests start there. Use this workflow only when AKS optimization could materially change steady node demand; do not calculate economics or purchase commitments.

Preconditions

Complete applicable rightsizing, autoscaler, NAP, and bin-packing analysis. Confirm the cluster, representative lookback, planned compute changes, and retained Capacity Reservation Group (CRG) requirements.

Collect AKS Demand

Use Azure MCP AKS or an approved az aks show fallback for node resource group, region, provisioning mode, and regular pool metadata.
For manual provisioning, collect each pool's VM size, current/min/max count, autoscaling, priority, CRG association, and upgrade surge. Map it to exact VMSS or VM resource IDs from pool metadata, not names alone.
For NAP, map each observed node or NodeClaim through providerID to exact Azure compute IDs; do not infer demand from the AKS pool list. Retain time-aligned SKU, region, zone, architecture, OS, and karpenter.sh/capacity-type; report unmapped resources as an evidence gap.
Exclude Spot capacity from stable commitment demand. Group non-Spot observations by compatible compute characteristics.
Reuse representative history; otherwise range-query managed Prometheus kube_node_info hourly over the requested 30-60 days, joining node and provider_id to the time-aligned mapping. Fall back to Container Insights only when Prometheus history is unsuitable. Report the requested/observed periods, observed hours, min/p05/p50/p95/max counts, and candidate-baseline hours and coverage. Retention limits the lookback. If neither source provides representative history, stable demand is unknown and no baseline may be produced.
Query associated CRGs through Resource Graph. Keep SKU, region, zone, reserved, associated, unused, and retained quantities separate. Unused CRG capacity remains billable.
Account for approved rightsizing, consolidation, NAP, migration, and reliability changes. Record seasonal, temporary, surge, outage, and planned demand that makes the lookback unrepresentative.

Handoff

Provide cost-optimization with:

Field	Content
AKS scope	Cluster ID, node resource group, region
Compute mapping	Provisioning mode, compatible compute group, exact VMSS/VM resource IDs, SKU, region/zone, architecture, OS, capacity type
Continuous baseline	Candidate non-Spot counts by compatible compute group, hours covered, coverage percentage, p05/p50/p95, lookback
Variability	Peak, seasonal, temporary, surge, outage, and planned-demand exceptions
Planned changes	Rightsizing, consolidation, NAP, migration, SKU/region changes
Capacity reservations	CRG reserved, associated, unused, and retained quantities
Evidence gaps	Missing history, inaccessible pools, or unresolved owner intent

Ask cost-optimization to evaluate utilization, coverage, scope, expiry, and renewal; retrieve Azure recommendations; correlate them with exact AKS mappings and approved changes; disclose broader-scope benefits; and return term, scope, coverage, expected utilization, reported savings/currency, assumptions, risks, and next review date.

This workflow does not query benefit APIs, recommend quantities, or claim expiry/renewal actions. If cost-optimization is unavailable, return the demand evidence and mark commitment analysis unavailable.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download aks-cost-optimization.zip