AWS Sdk Python Usage
AWS SDK for Python (boto3/botocore) development patterns.
Install
npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -g -a claude-code -y- Repository
- aws/agent-toolkit-for-aws3.4k
- Category
- DevOps
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 1,904 tokens
- Runs scripts
- No, instructions only
What it does
AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.
Do not use emojis in any code, comments, or output when this skill is active.
AWS SDK for Python (boto3)
boto3 is the high-level Python SDK for AWS. It wraps botocore (the low-level SDK) and provides two distinct interfaces: clients (low-level, 1:1 API mapping) and resources (high-level, object-oriented). Understanding which to use and when is essential.
Client vs Resource
Clients map directly to AWS service APIs. Every service has a client. Responses are plain dicts.
Resources provide an object-oriented interface with attributes and actions. Only some services have resources (S3, DynamoDB, EC2, IAM, SQS, SNS, CloudFormation, CloudWatch, Glacier). Resources auto-marshal types (especially useful for DynamoDB).
import boto3
# Client - low-level, all services
s3_client = boto3.client("s3")
response = s3_client.list_buckets()
buckets = response["Buckets"] # plain dicts
# Resource - high-level, select services
s3_resource = boto3.resource("s3")
for bucket in s3_resource.buckets.all():
print(bucket.name) # attribute access, not dict keys
Use clients when you need full API coverage or the service has no resource interface. Use resources when they exist and simplify your code (especially DynamoDB and S3).
Session and Client Creation
import boto3
# Default session implicitly created
client = boto3.client("s3")
resource = boto3.resource("dynamodb")
# Explicit session use when you need to customize how
# clients are created, use an explicit profile, etc.
session = boto3.Session(
profile_name="my-profile",
region_name="us-west-2",
)
client = session.client("s3")
Do not create clients inside loops - reuse a single client instance. Clients are thread safe and can be shared across threads once they're instantiated.

