itsmostafa/aws-agent-skills/dynamodb

Name: dynamodb
Author: itsmostafa

Trust tier 2

community

LLM review pending

Source check pending

Checking that the SKILL.md file is still in the repository.

AWS DynamoDB NoSQL database for scalable data storage. Use when designing table schemas, writing queries, configuring indexes, managing capacity, implementing single-table design, or troubleshooting performance issues.

Install

npx vskill@latest install itsmostafa/aws-agent-skills/dynamodb

SKILL.md

AWS DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service providing fast, predictable performance at any scale. It supports key-value and document data structures.

Core Concepts
Common Patterns
CLI Reference
Best Practices
Troubleshooting
References

Core Concepts

Keys

Key Type	Description
Partition Key (PK)	Required. Determines data distribution
Sort Key (SK)	Optional. Enables range queries within partition
Composite Key	PK + SK combination

Secondary Indexes

Index Type	Description
GSI (Global Secondary Index)	Different PK/SK, separate throughput, eventually consistent
LSI (Local Secondary Index)	Same PK, different SK, shares table throughput, strongly consistent option

Capacity Modes

Mode	Use Case
On-Demand	Unpredictable traffic, pay-per-request
Provisioned	Predictable traffic, lower cost, can use auto-scaling

Common Patterns

Create a Table

AWS CLI:

bash

aws dynamodb create-table \
  --table-name Users \
  --attribute-definitions \
    AttributeName=PK,AttributeType=S \
    AttributeName=SK,AttributeType=S \
  --key-schema \
    AttributeName=PK,KeyType=HASH \
    AttributeName=SK,KeyType=RANGE \
  --billing-mode PAY_PER_REQUEST

boto3:

python

import boto3
dynamodb = boto3.resource('dynamodb')
table = dynamodb.create_table(
    TableName='Users',
    KeySchema=[
        {'AttributeName': 'PK', 'KeyType': 'HASH'},
        {'AttributeName': 'SK', 'KeyType': 'RANGE'}
    ],
    AttributeDefinitions=[
        {'AttributeName': 'PK', 'AttributeType': 'S'},
        {'AttributeName': 'SK', 'AttributeType': 'S'}
    ],
    BillingMode='PAY_PER_REQUEST'
)
table.wait_until_exists()

Basic CRUD Operations

python

import boto3
from boto3.dynamodb.conditions import Key, Attr
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('Users')
# Put item
table.put_item(
    Item={
        'PK': 'USER#123',
        'SK': 'PROFILE',
        'name': 'John Doe',
        'email': 'john@example.com',
        'created_at': '2024-01-15T10:30:00Z'
    }
)
# Get item
response = table.get_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'}
)
item = response.get('Item')
# Update item
table.update_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'},
    UpdateExpression='SET #name = :name, updated_at = :updated',
    ExpressionAttributeNames={'#name': 'name'},
    ExpressionAttributeValues={
        ':name': 'John Smith',
        ':updated': '2024-01-16T10:30:00Z'
    }
)
# Delete item
table.delete_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'}
)

Query Operations

python

# Query by partition key
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123')
)
# Query with sort key condition
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123') & Key('SK').begins_with('ORDER#')
)
# Query with filter
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123'),
    FilterExpression=Attr('status').eq('active')
)
# Query with projection
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123'),
    ProjectionExpression='PK, SK, #name, email',
    ExpressionAttributeNames={'#name': 'name'}
)
# Paginated query
paginator = dynamodb.meta.client.get_paginator('query')
for page in paginator.paginate(
    TableName='Users',
    KeyConditionExpression='PK = :pk',
    ExpressionAttributeValues={':pk': {'S': 'USER#123'}}
):
    for item in page['Items']:
        print(item)

Batch Operations

python

# Batch write (up to 25 items)
with table.batch_writer() as batch:
    for i in range(100):
        batch.put_item(Item={
            'PK': f'USER#{i}',
            'SK': 'PROFILE',
            'name': f'User {i}'
        })
# Batch get (up to 100 items)
dynamodb = boto3.resource('dynamodb')
response = dynamodb.batch_get_item(
    RequestItems={
        'Users': {
            'Keys': [
                {'PK': 'USER#1', 'SK': 'PROFILE'},
                {'PK': 'USER#2', 'SK': 'PROFILE'}
            ]
        }
    }
)

Create GSI

bash

aws dynamodb update-table \
  --table-name Users \
  --attribute-definitions AttributeName=email,AttributeType=S \
  --global-secondary-index-updates '[
    {
      "Create": {
        "IndexName": "email-index",
        "KeySchema": [{"AttributeName": "email", "KeyType": "HASH"}],
        "Projection": {"ProjectionType": "ALL"}
      }
    }
  ]'

Conditional Writes

python

from botocore.exceptions import ClientError
# Only put if item doesn't exist
try:
    table.put_item(
        Item={'PK': 'USER#123', 'SK': 'PROFILE', 'name': 'John'},
        ConditionExpression='attribute_not_exists(PK)'
    )
except ClientError as e:
    if e.response['Error']['Code'] == 'ConditionalCheckFailedException':
        print("Item already exists")
# Optimistic locking with version
table.update_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'},
    UpdateExpression='SET #name = :name, version = version + :inc',
    ConditionExpression='version = :current_version',
    ExpressionAttributeNames={'#name': 'name'},
    ExpressionAttributeValues={
        ':name': 'New Name',
        ':inc': 1,
        ':current_version': 5
    }
)

CLI Reference

Table Operations

Command	Description
`aws dynamodb create-table`	Create table
`aws dynamodb describe-table`	Get table info
`aws dynamodb update-table`	Modify table/indexes
`aws dynamodb delete-table`	Delete table
`aws dynamodb list-tables`	List all tables

Item Operations

Command	Description
`aws dynamodb put-item`	Create/replace item
`aws dynamodb get-item`	Read single item
`aws dynamodb update-item`	Update item attributes
`aws dynamodb delete-item`	Delete item
`aws dynamodb query`	Query by key
`aws dynamodb scan`	Full table scan

Batch Operations

Command	Description
`aws dynamodb batch-write-item`	Batch write (25 max)
`aws dynamodb batch-get-item`	Batch read (100 max)
`aws dynamodb transact-write-items`	Transaction write
`aws dynamodb transact-get-items`	Transaction read

Best Practices

Data Modeling

Design for access patterns — know your queries before designing
Use composite keys — PK for grouping, SK for sorting/filtering
Prefer query over scan — scans are expensive
Use sparse indexes — only items with index attributes are indexed
Consider single-table design for related entities

Performance

Distribute partition keys evenly — avoid hot partitions
Use batch operations to reduce API calls
Enable DAX for read-heavy workloads
Use projections to reduce data transfer

Cost Optimization

Use on-demand for variable workloads
Use provisioned + auto-scaling for predictable workloads
Set TTL for expiring data
Archive to S3 for cold data

Troubleshooting

Throttling

Symptom: ProvisionedThroughputExceededException

Causes:

Hot partition (uneven key distribution)
Burst traffic exceeding capacity
GSI throttling affecting base table

Solutions:

python

# Use exponential backoff
import time
from botocore.config import Config
config = Config(
    retries={
        'max_attempts': 10,
        'mode': 'adaptive'
    }
)
dynamodb = boto3.resource('dynamodb', config=config)

Hot Partitions

Debug:

bash

# Check consumed capacity by partition
aws cloudwatch get-metric-statistics \
  --namespace AWS/DynamoDB \
  --metric-name ConsumedReadCapacityUnits \
  --dimensions Name=TableName,Value=Users \
  --start-time $(date -d '1 hour ago' -u +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period 60 \
  --statistics Sum

Solutions:

Add randomness to partition keys
Use write sharding
Distribute access across partitions

Query Returns No Items

Debug checklist:

Verify key values exactly match (case-sensitive)
Check key types (S, N, B)
Confirm table/index name
Review filter expressions (they apply AFTER read)

Scan Performance

Issue: Scans are slow and expensive

Solutions:

Use parallel scan for large tables
Create GSI for the access pattern
Use filter expressions to reduce returned data

python

# Parallel scan
import concurrent.futures
def scan_segment(segment, total_segments):
    return table.scan(
        Segment=segment,
        TotalSegments=total_segments
    )
with concurrent.futures.ThreadPoolExecutor() as executor:
    results = list(executor.map(
        lambda s: scan_segment(s, 4),
        range(4)
    ))

References

Security

Security scanPassed, 100 of 100Pattern checks on the skill's files.
Model reviewNot runA model review is scheduled for the next rescan.

See the full security report

Quality evaluation

Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

SKILL.md in the repositoryCheckingskills/dynamodb/SKILL.md

Popularity

Repository stars: 1,050
Repository forks: 437
7-day trend score: 35

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 22 Mar 2026, 7 months ago.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

Claude Code
Cursor
GitHub Copilot
Windsurf
Codex
Gemini CLI
Cline
Amp
Roo Code
Goose
Continue
Kilo Code
Junie
Trae
OpenHands
Qwen Code
AI Agent
Tabnine Chat
Sourcegraph Cody
Amazon Q
Firebase Genkit
Sweep
Devin
Phind
Mentat
Claude Desktop
Aider
GPT Pilot
Plandex
bolt.new
v0
Replit Agent
Anse
TypingMind
BetterChatGPT
LibreChat
Warp AI
Pieces
Fig AI

Embed badge

[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/itsmostafa/aws-agent-skills/dynamodb/badge)](https://verified-skill.com/skills/itsmostafa/aws-agent-skills/dynamodb)

Something wrong with this skill or its page? Tell us.

Install

SKILL.md

AWS DynamoDB

Table of Contents

Core Concepts

Keys

Secondary Indexes

Capacity Modes

Common Patterns

Create a Table

Basic CRUD Operations

Query Operations

Batch Operations

Create GSI

Conditional Writes

CLI Reference

Table Operations

Item Operations

Batch Operations

Best Practices

Data Modeling

Performance

Cost Optimization

Troubleshooting

Throttling

Hot Partitions

Query Returns No Items

Scan Performance

References

Security

Quality evaluation

Source

Popularity

Works with