atmos-aws-security

atmos-aws-security is a skill for Claude Code, Codex from cloudposse/atmos. It costs 31 tokens per session (744 once invoked), scanned A, original, Apache-2.0.

A workflow for investigating AWS security findings in Atmos infrastructure. Atmos is a tool for organizing reusable infrastructure components and deployment stacks, while Terraform files define the cloud resources.

In plain words
What is it for?
Use it to explain the root cause of an AWS finding, identify the affected Atmos component or stack, write exact Terraform changes, and provide commands for applying the fix.
Why use it?
It connects a security warning to the Terraform configuration that caused it and turns the finding into specific remediation steps and deployment commands.

Skill for Claude CodeCodex

Part of the atmos plugin — 51 skills shipped together

About the project

Atmos is an infrastructure runtime that coordinates tools such as Terraform, OpenTofu, Kubernetes, Helm, Packer, Ansible, and containers through consistent commands and configuration. It is for teams running cloud infrastructure on laptops, in CI, or through AI agents across environments and regions. Its catalogue entries provide skills, agents, commands, and other add-ons for Atmos workflows.

cloudposse/atmos · 1,372 stars · on GitHub · atmos.tools

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/cloudposse/atmos/atmos-aws-security
Any agent
npx skills add cloudposse/atmos --skill atmos-aws-security
Clone the repo
git clone --depth 1 https://github.com/cloudposse/atmos

Made for: Claude Code, Codex.

Or install atmos, the plugin that ships this one along with the rest of its 51 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for atmos-aws-security

README.md
[![agentmods](https://agentmods.dev/badge/skills/cloudposse/atmos/atmos-aws-security.svg)](https://agentmods.dev/skills/cloudposse/atmos/atmos-aws-security)
Your own site
<a href="https://agentmods.dev/skills/cloudposse/atmos/atmos-aws-security"><img src="https://agentmods.dev/badge/skills/cloudposse/atmos/atmos-aws-security.svg" alt="Measured on agentmods" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 744 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 5.1 $0.00031 $0.00744
Opus 5 $0.00015 $0.00372
Sonnet 5 $0.00006 $0.00149
Haiku 4.5 $0.00003 $0.00074

Measured 6d ago against content hash 1ccab9d873d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

atmos-aws-security 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 6d ago.

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.

agent-skills/skills/atmos-aws-security/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Atmos AWS Security Finding Analysis

You are analyzing AWS security findings that have been mapped to Atmos infrastructure components. Your job is to provide consistent, structured remediation guidance that follows an exact format.

Output Format

You MUST return your analysis using these exact section headers. Every section is required. The output is parsed programmatically — do not deviate from the format.

Root Cause

Explain WHY this finding exists in the infrastructure. Reference the specific Terraform resource or stack configuration that caused it. Be specific — name the resource type, the missing attribute, or the misconfigured setting.

Steps

Return an ordered list of remediation steps. Each step should be a concrete action. Use numbered list format:

  1. First step
  2. Second step
  3. Third step

Code Changes

Show the specific Terraform/HCL changes needed. Use the component source code provided in the context. Format as a diff or before/after:

# Before
resource "aws_s3_bucket" "this" {
  bucket = var.bucket_name
}

# After
resource "aws_s3_bucket" "this" {
  bucket = var.bucket_name
}

resource "aws_s3_bucket_versioning" "this" {
  bucket = aws_s3_bucket.this.id
  versioning_configuration {
    status = "Enabled"
  }
}

Stack Changes

Show the specific stack YAML changes needed. Reference the exact vars key to add or modify:

# stacks/deploy/prod/us-east-1.yaml
components:
  terraform:
    s3-bucket:
      vars:
        versioning_enabled: true

Deploy

Provide the exact atmos terraform apply command to deploy the fix:

atmos terraform apply <component> -s <stack>

Risk

Rate the risk of applying this remediation: low, medium, or high.

  • low — Read-only change, no service disruption
  • medium — Config change that may cause brief disruption
  • high — Destructive change (resource replacement, data loss risk)

References

List relevant AWS documentation URLs, CIS benchmark controls, or compliance framework references.

Read the full file on GitHub · 110 lines

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. 6d ago First seen · 110 lines · 31 tokens per session scan A 1ccab9d873d2

Subscribe to this mod's changes

atmos-aws-security is a skill published in the GitHub repository cloudposse/atmos (1,372 stars, last pushed today), licensed Apache-2.0. It adds 31 tokens to every session and 744 once invoked, about $0.0002 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 2026-08-30.

Related

Other skills, from other repositories

flow-nexus-swarm

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform.

ruvnet/ruflo · 20 tokens

ray-train

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

davila7/claude-code-templates · 63 tokens

skypilot-multi-cloud-orchestration

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

davila7/claude-code-templates · 51 tokens

trigger-cost-savings

Analyze Trigger.dev tasks, schedules, and runs for cost optimization opportunities. Use when asked to reduce spend, optimize costs, audit usage, right-size machines, or review task efficiency. Combines static source analysis with live run analysis via the Trigger.dev MCP tools (listruns, getrundetails…

triggerdotdev/trigger.dev · 68 tokens

dstack-prototyping

Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final…

dstackai/dstack · 80 tokens

dstack-presets

Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or serving a model.

dstackai/dstack · 61 tokens