aws-cost-optimizer

An AWS cloud-cost review workflow for finding unnecessary spending in hosted computing resources. AWS is Amazon’s cloud platform, where services and storage are billed based on use.

In plain words
What is it for?
Use it to review AWS bills, right-size compute, remove unused resources, compare Savings Plans or Reserved Instances, modernize storage, and improve cost-allocation tags.
Why use it?
It helps identify idle or oversized resources and makes cost-cutting decisions easier to compare, including their risks and rollback options.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/jayrha/agentskills/aws-cost-optimizer
Any agent
npx skills add JayRHa/AgentSkills --skill aws-cost-optimizer
Clone the repo
git clone --depth 1 https://github.com/JayRHa/AgentSkills

Made for: Claude Code, Codex.

Per session 116 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,155 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00116 $0.02155
Opus 5 $0.00058 $0.01077
Sonnet 5 $0.00023 $0.00431
Haiku 4.5 $0.00012 $0.00215

Measured 2d ago against content hash e1f042c3fcf4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

aws-cost-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/cost_analysis.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

aws-cost-optimizer/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AWS Cost Optimizer

Overview

Keywords: AWS cost, FinOps, right-sizing, idle resources, orphaned resources, Savings Plans, Reserved Instances, RI, Spot, cost allocation tags, Cost Explorer, CUR, unblended cost, amortized cost, NAT gateway, gp2 to gp3, S3 lifecycle, Graviton, Compute Optimizer, anomaly detection.

This skill drives a repeatable cost-reduction workflow across the five highest-leverage levers: eliminate (idle/orphaned), right-size (over-provisioned), commit (Savings Plans / RIs), modernize (newer/cheaper SKUs and storage tiers), and attribute (tagging + showback). Always quantify monthly savings, rank by effort-vs-impact, and never recommend a change without stating its risk and rollback.

Use the bundled assets:

  • references/cost-levers.md — the full catalog of cost-reduction levers per service with detection signals and typical savings.
  • references/savings-plans-vs-ri.md — decision framework and break-even math for commitment purchases.
  • references/tagging-strategy.md — cost-allocation tag taxonomy and enforcement patterns.
  • scripts/cost_analysis.py — stdlib-only analyzer that scores resources for waste and ranks recommendations from JSON inventory.
  • templates/cost-optimization-report.md — fill-in deliverable for stakeholders.
  • examples/right-sizing-walkthrough.md — a concrete end-to-end example.

Workflow

  1. Establish the baseline. Pull the last 1–3 months from Cost Explorer or the Cost & Usage Report (CUR). Always reason in amortized cost (commitments spread over their term), not unblended, so commitments aren't double-counted. Break spend down by service, by linked account, and by tag. Record the top 10 cost drivers — typically EC2, RDS, S3, data transfer, and NAT gateways.

  2. Eliminate waste first (zero risk, fast). Idle and orphaned resources are pure savings with no performance trade-off. Scan for: unattached EBS volumes, unassociated Elastic IPs, idle/empty load balancers, stopped instances still holding EBS, old EBS/RDS snapshots, idle NAT gateways, dev/test resources running 24/7, and forgotten dev environments. See references/cost-levers.md "Eliminate" section.

Read the full file on GitHub · 102 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 102 lines · 116 tokens per session scan A e1f042c3fcf4

Subscribe to this mod's changes

aws-cost-optimizer is a skill published in the GitHub repository JayRHa/AgentSkills (4 stars, last pushed 1mo ago), licensed MIT. It adds 116 tokens to every session and 2,155 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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