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Aws Rds Database

Checked
aj-geddes·vlatest·cloud·MIT

Helps with AWS, database 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 aj-geddes/useful-ai-prompts --skill aws-rds-database -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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/aws-rds-database/SKILL.md
Scanned
6 files, 402 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

The aws-rds-database skill enables AI agents to interact with Amazon RDS instances, allowing them to execute SQL queries, manage database configurations, and automate routine maintenance tasks directly within cloud environments. It serves as a bridge between natural language instructions and complex database operations for PostgreSQL, MySQL, MariaDB, Oracle Database, and SQL Server engines.

When to use it

  • Automating the generation of complex SQL reports or data exports without manual script writing.
  • Performing routine maintenance tasks like schema updates or index creation based on changing business requirements.
  • Querying live production data for real-time analytics or debugging application logic issues.
  • Provisioning new database instances with specific engine versions and parameter groups via natural language commands.

Key capabilities

  • Execute read-only and write operations across supported RDS engines.
  • Manage instance lifecycle events including creation, scaling, and termination.
  • Retrieve detailed performance metrics and monitoring data for optimization insights.
  • Automate backup restoration procedures and point-in-time recovery scenarios.

Example prompts

  • "Create a new PostgreSQL database named 'sales_db' with a user 'analyst' having read-only access to the public schema."
  • "Run a query to sum total sales by region for the last quarter from the orders table in our MySQL instance."
  • "Increase the allocated storage for the production Oracle Database instance to 500GB and apply the latest security patch."

Tips & gotchas

Ensure your AI agent has appropriate IAM permissions attached to the RDS resource before attempting write operations or schema modifications. While this skill streamlines database management, always validate generated SQL queries in a staging environment before applying them to production data to prevent accidental data loss.