Aws Database logo

Aws Database

OrganizationPopular
aws
aws-database

Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill nameaws-database
Stars
2.7K
Forks
311
Bundled files
20
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 20 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Aws Database AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git /tmp/agent-toolkit-for-aws
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit-for-aws/plugins/aws-core/skills/aws-database .claude/skills/aws-database
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Database in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Database on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Database is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS Database

STOP — Do not answer from general knowledge. Before responding to any database question, match the user's request against the sub-skill registry below and follow its procedure. If the procedure says to hand off to a service skill, you MUST load that skill before providing operational guidance. Never skip the routing step.

AWS Databases comprise 15+ fully-managed database engines and offer a high-performance, secure, and reliable foundation to power agentic AI and data-driven applications. Each AWS database is optimized for a specific workload shape or data model — relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory (ElastiCache, MemoryDB). For relational workloads, AWS supports PostgreSQL (Aurora, DSQL, RDS), MySQL (Aurora, RDS), MariaDB (RDS), Oracle (RDS, ODB@AWS), SQL Server (RDS), and Db2 (Db2).

Use this skill as the entry point for any actions or questions related to databases on AWS. It helps match a workload to the right AWS database service, or hand off to a service-specific skill for operational questions or actions.

This skill works with or without the AWS MCP server. When available, the AWS MCP server is recommended for sandboxed execution and audit logging.

Global rules

  1. Match the user's language. Respond in the same language the user writes in. Default to non-technical explanations. Only escalate technical depth when they've shown fluency — by using the terms themselves, stating a technical role, or answering a plain question with a technical answer.

  2. Revise when new information arrives. If the user pushes back or adds new details, re-check the sub-skill registry triggers before responding. Pushback that matches report-issue triggers (e.g., "that's wrong", "it's wrong", "you picked the wrong service") must route to report-issue — do not defend your prior recommendation or ask the user to justify their objection. The goal is the right answer, not consistency with your first response.

  3. Do not rely on training data for facts. AWS databases change frequently. Before stating pricing, quotas, or GA status, verify against the knowledge cards loaded by this skill. If the fact is not in a knowledge card, look it up — in priority order: (a) use the AWS MCP server (aws___read_documentation, aws___search_documentation) if available; (b) fetch the service's llms.txt URL from its knowledge card for a structured documentation index; (c) direct users to AWS documentation. If a user mentions a feature not covered by a knowledge card, look it up rather than guessing.

  4. Verify, don't guess. If you cannot confirm a fact from a knowledge card or documentation, say so. "I'm not sure — check the docs" is better than a confident wrong answer.

How this skill works

  1. Find the sub-skill — Match the user's request against the sub-skill registry below. Match on meaning, not exact wording. If ambiguous, ask: "Are you choosing a database, or do you need help with one you already have?" This matching applies to every user message, not just the first. If a subsequent message matches a different sub-skill's triggers (e.g., the user pushes back on a recommendation and their phrasing matches report-issue), re-route immediately — do not continue the previous sub-skill's flow.

  2. If a sub-skill matches — read references/{sub-skill-id}.md and follow its procedure.

  3. If no sub-skill matches — answer from the knowledge cards in assets/. If the card doesn't cover it, use documentation tools (aws___search_documentation, aws___read_documentation) if available, or fetch the service's llms.txt URL from its knowledge card, or direct the user to the AWS documentation URL listed in the card. This is the path for quick facts: pricing, limits, GA status, feature confirmation, or any question answerable from the card alone. Always offer to load the service skill for deeper guidance.

Sub-skill registry

IDNameTrigger PhrasesWhen to Route HereNext Steps
selectDatabase Selection"which database", "help me choose", "recommend", "what should I use", "starting a new project", "picking a database", "I need a database", "I'm building", "build a", "how should I store", "best way to handle", "need to support", "design for"User hasn't chosen a service yet, is comparing options, or describes a workload/data problem without naming a specific servicehandoff
handoffService Handoff"how do I", "configure", "optimize", "troubleshoot", "set up", "migrate to", "connect to", "scale", "upgrade", "monitor", "backup", "restore", "build", "create", "deploy", "provision", + named serviceUser names a specific AWS database service and has an operational, advisory, or action question
report-issueReport Issue"that's wrong", "incorrect", "bad recommendation", "you should have said", "missing", "skill is wrong", "report this", "file a bug", "report an issue"User reports that the skill gave incorrect or incomplete guidance

Service reference

Load knowledge cards on demand — only when the current turn requires verifying or stating facts about a service. Read assets/{filename} for the relevant service(s). Load only the cards for services being actively considered (typically 2–3 per request).

ServiceKnowledge fileService skill for handoff
Aurora DSQLassets/aurora-dsql.mdaurora-dsql
Aurora MySQLassets/aurora-mysql.mdamazon-aurora-mysql
Aurora PostgreSQLassets/aurora-postgresql.mdamazon-aurora-postgresql
DocumentDBassets/documentdb.mdamazon-documentdb
DynamoDBassets/dynamodb.md
ElastiCacheassets/elasticache.mdamazon-elasticache
Keyspacesassets/keyspaces.mdamazon-keyspaces
MemoryDBassets/memorydb.md
Neptuneassets/neptune.md
ODB @ AWSassets/odb-aws.md
RDS for Db2assets/rds-db2.mdrds-db2
RDS for MariaDBassets/rds-mariadb.mdrds-oss
RDS for MySQLassets/rds-mysql.mdrds-oss
RDS for Oracleassets/rds-oracle.mdrds-oracle
RDS for PostgreSQLassets/rds-postgresql.mdrds-oss
RDS for SQL Serverassets/rds-sqlserver.mdrds-sqlserver
Timestreamassets/timestream.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Database AI skill do?

Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage d...

Why use Aws Database on TypingMind?

Because you install it once and use it with any model. Aws Database is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-core/skills/aws-database. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Database?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Database?

As many as you like. As long as a model supports skills, you can use Aws Database with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Database AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇