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/nap-decisions.md

NAP Decisions

Use only after completing NAP path selection, eligibility, and applicable evidence collection.

Assess Signals

A primary signal is verified evidence that NAP could address an existing capacity, placement, or stranded-capacity problem. Insufficient resources, a missing compatible node, and stranded or mismatched fixed capacity are primary signals. Persistence or recurrence increases confidence but is not a primary signal by itself.

Require at least one confirmed primary signal. For each signal, identify an allowed NAP node configuration that fits workload requests, DaemonSet overhead, architecture, zones, networking, storage, policy, quota, and budget.

Signal	Evidence	Decision role
Insufficient resources	Matching `FailedScheduling` message reports insufficient CPU/memory or exhausted pod capacity; affected pod requests	Compatible capacity could resolve the shortage
Missing compatible node	Scheduling failure and placement/storage requirements compared with existing capacity	A different configuration is valid only when the restriction is intentional or preferable to safely relaxing it
Stranded or mismatched fixed capacity	Verified bin-packing findings, or pool-floor, node-count, request/utilization, scaling, workload-shape, and cost evidence	Dynamic configurations could serve demand with less stranded capacity
Persistence/recurrence	Seven-day history correlated with event or incident causes	Increases confidence but is not a primary signal alone

Compare the same demand under NAP, CAS tuning, raised pool limits, and an added fixed pool. Prefer NAP only when varying requirements or stranded capacity create a supported cost or operational advantage. Prefer simpler scaling for stable demand that fits an existing pool. Respect availability, reservation, and budget limits.

Consider AKS Automatic for production workloads. If it could meet control and networking boundaries, invoke azure-kubernetes-automatic-readiness for migration compatibility when azure-kubernetes is available.

Do not treat a likely stale placement rule as a primary signal. Route it to bin-packing analysis and preserve it until its owner approves a change.

Readiness and Verdict

After a primary signal, run only triggered VM, quota, subnet, policy, or volume checks from NAP evidence collection. Reuse data; do not perform a broad readiness audit.

Verdict	Rule
Recommend	Eligibility and readiness pass, a primary signal persists or recurs, and NAP clearly beats simpler scaling
Limited trial	Eligibility and readiness pass and NAP has an advantage, but history is limited; agree on trial workloads, capacity limits, and success criteria before enablement
Insufficient evidence	No primary signal or missing cause/fit data
Do not enable	Readiness failed or is unknown, migration is unapproved, only non-NAP causes exist, or simpler scaling suffices

Report the verdict, evidence, affected workloads, timeframe, cost or capacity baseline, expected effect and assumptions, advantage over simpler scaling, readiness gaps, risk, and confidence.

Only after a recommendation and at the user's request, follow NAP enablement and rollback.

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