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amazon-dynamodb

Designs, costs, and validates DynamoDB data layers from access patterns and design axioms.

2.5k|282|Updated Apr 23, 2026
Search Tags:#benchmarking#data modeling#AWS#dynamodb#cost estimation#noSQL#Vector search
One-click install
npx skills add https://github.com/aws/agent-toolkit-for-aws --skill amazon-dynamodb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: amazon-dynamodb
Source: https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/database-skills/amazon-dynamodb
Command: npx skills add https://github.com/aws/agent-toolkit-for-aws --skill amazon-dynamodb

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires boto3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

DynamoDB designs fail in predictable ways — hot partitions, wrong GSI keys, unbounded Scans, surprise bills — and most mistakes are only discovered after deployment. This Skill produces a defensible DynamoDB data-layer design grounded in explicit access patterns and design axioms, then optionally estimates monthly cost and validates the design against real AWS resources before you commit.

Core Features & Use Cases

  • Axiom-driven data modeling: Enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, and configures Streams, Global Tables, TTL, and vector indexes for similarity search.
  • Cost estimation: Generates a monthly cost report from a JSON data model using scripts/calculate_costs.py, with per-attribute item-size walkthroughs to keep estimates accurate.
  • Live validation: An opt-in pipeline deploys scratch tables, benchmarks them with an in-region Lambda, produces a performance report, and tears everything down with consent gates and spend guardrails.
  • Use Case: A team designing a multi-tenant SaaS backend on DynamoDB uses the Skill to enumerate access patterns, get a schema with per-pattern query plans, receive a monthly cost estimate, and then run a representative benchmark to confirm the design survives hot-key load before production.

Quick Start

Ask the agent to design a DynamoDB data layer for your application by listing the queries it must serve, and it will produce the access-pattern table, schema, and cost estimate.

Frequently Asked Questions about amazon-dynamodb

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a DynamoDB schema from access patterns?▼

Start by enumerating every access pattern with its expected RPS, items returned, item size, consistency, and authorization scope. The Skill then applies design axioms to choose partition and sort keys, GSIs, and table boundaries, producing a schema plus a per-pattern query plan.

How do I estimate monthly DynamoDB costs before deploying?▼

Run scripts/calculate_costs.py against a dynamodb_data_model.json file describing your tables and access patterns. It writes a cost_report.md with a monthly estimate driven by per-pattern RPS and per-attribute item sizes.

Does DynamoDB support vector similarity search?▼

Yes, DynamoDB has native vector indexes queried with the SearchVectors API, storing embeddings as number lists on items. It requires on-demand capacity and boto3/botocore 1.43.64 or later; Query and Scan do not work against vector indexes.

Can I change a GSI key schema or projection after creation?▼

No, GSI key schema and projection are immutable after creation. The supported path is additive migration: create a new GSI with the desired shape, let it populate, cut reads over, then drop the old index.

Does the live validation stage create real AWS resources?▼

Yes, stages 3-6 create real tables, a Lambda, and an IAM role, and they incur real charges. They are strictly opt-in, gated by --yes-deploy flags, a cost_guardrail_usd spend limit, and an explicit teardown confirmation.

Why is my DynamoDB table throttling or running hot partitions?▼

Hot partitions occur when traffic concentrates on few partition keys beyond the per-partition throughput ceiling. The Skill's representative benchmark mode uses zipf key sampling to reproduce this and surfaces key-skew findings in the performance report.

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