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Aws Agentic Ai

Checked
commandcodeai·vlatest·cloud·MIT

Helps with AWS, AI as part of deploying and managing cloud infrastructure workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add commandcodeai/agent-skills --skill aws-agentic-ai -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

commandcodeai

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned commandcodeai/agent-skills at f490dd9016.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
f490dd9016
Manifest
skills/aws-agentic-ai/SKILL.md
Scanned
13 files, 3,280 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to interact with and manage AWS resources through programmatic access. It allows agents to perform tasks like creating EC2 instances, managing S3 buckets, and configuring CloudWatch alarms—all without direct human intervention. The agent can leverage this skill to automate cloud infrastructure management and respond dynamically to changing conditions.

When to use it

  • Automated Infrastructure Provisioning: Automatically spin up new development environments based on predefined templates when a new project is initiated.
  • Dynamic Resource Scaling: Scale EC2 instances in response to real-time traffic patterns detected by CloudWatch metrics.
  • Cost Optimization: Identify and terminate idle resources (e.g., unused EBS volumes) to reduce AWS spending.
  • Incident Response: Automatically remediate common infrastructure issues, like restarting failed services or restoring data from backups.

Key capabilities

  • AWS Resource Management: Create, modify, and delete various AWS resources.
  • Programmatic Access: Interact with AWS APIs through code execution.
  • CloudWatch Integration: Monitor metrics and trigger actions based on CloudWatch alarms.
  • Agentic Automation: Enables AI agents to autonomously manage cloud infrastructure.

Example prompts

  • "Create a new EC2 instance in the us-east-1 region with t2.micro size."
  • "List all S3 buckets in my account and their contents."
  • "Scale the number of running EC2 instances based on CPU utilization from CloudWatch."

Tips & gotchas

  • Requires appropriate AWS credentials configured for the AI agent to execute commands.
  • Be mindful of potential security implications when granting agents access to manage cloud resources; follow the principle of least privilege.