aws-sdk-python-usage
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/aws/agent-toolkit-for-aws --skill aws-sdk-python-usage- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads aws-sdk-python-usage automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
Making API Calls
# Client - pass parameters as keyword arguments, get dicts back
response = client.get_object(Bucket="my-bucket", Key="my-key")
data = response["Body"].read()
# Resource - use object methods and attributes
obj = s3_resource.Object("my-bucket", "my-key")
response = obj.get()
data = response["Body"].read()
Parameter names match the exact casing of the AWS API, which is typically PascalCase, not snake_case.
Error Handling
Only catch exceptions when you have something actionable to do - return a fallback value, retry, take a different code path. Catching an exception just to print it and swallow it is wrong: it hides the real error and prevents callers from reacting. Let exceptions propagate by default.
When you do catch, prefer typed exceptions on the client over generic
ClientError with string code matching through the client.exceptions
attribute:
lambda_client = boto3.client("lambda")
def get_function_config(name: str) -> dict | None:
"""Return function configuration, or None if it doesn't exist."""
try:
return lambda_client.get_function_configuration(FunctionName=name)
except lambda_client.exceptions.ResourceNotFoundException:
return None # actionable: convert missing function to None
# Everything else propagates - caller or main() handles it
Use generic ClientError only as a catch-all in a top-level error handler, not
in business logic functions. It lives in botocore, not boto3:
from botocore.exceptions import ClientError
def main() -> int:
try:
result = do_the_work()
print(result)
return 0
except ClientError as e:
print(f"Error: {e}", file=sys.stderr)
return 1
For the full error hierarchy and botocore exceptions, see references/error-handling.md.
Script Structure
When asked to write a script that uses boto3 or botocore, keep if __name__ == "__main__" to a single function call. Argument parsing, error presentation,
and exit codes belong in main(), not scattered across business logic
functions:
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("bucket")
args = parser.parse_args()
try:
do_the_work(args.bucket)
return 0
except ClientError as e:
print(f"Error: {e}", file=sys.stderr)
return 1
if __name__ == "__main__":
sys.exit(main())
Never call sys.exit() from a business logic function -- it makes the function
untestable and unusable as a library. Raise an exception or return an error
value instead, and let main() decide how to present it.
Pagination
Never manually loop with NextToken -- use paginators. When you only need
specific fields, use .search() with a JMESPath expression to extract and
flatten across pages:
paginator = iam.get_paginator("list_users")
for name in paginator.paginate().search("Users[].UserName"):
print(name)
# Filter and project
for arn in paginator.paginate().search("Users[?Path == '/admin/'][].Arn"):
print(arn)
When you need the full response object per item, or need per-page control (e.g. counting pages, batching by page), iterate pages directly:
for page in paginator.paginate():
for user in page.get("Users", []):
process(user)
For more details on pagination, see: references/pagination.md.
Waiters
Wait for a resource to reach a desired state:
waiter = client.get_waiter("bucket_exists")
waiter.wait(
Bucket="my-bucket",
WaiterConfig={"Delay": 5, "MaxAttempts": 20},
)
For more details on waiters, see references/waiters.md.
Client Configuration
Use botocore.config.Config for retries, timeouts, and connection pool
settings, etc.:
from botocore.config import Config
config = Config(
retries={"total_max_attempts": 2, "mode": "adaptive"},
connect_timeout=5,
read_timeout=10,
max_pool_connections=50,
)
client = boto3.client("s3", config=config)
When creating custom configuration for a client, see references/configuration.md.
Logging
Both boto3 and botocore use the standard library logging module. You can
configure logging through the standard logging APIs, or you can use
helpers provided by boto3 and botocore for convenience:
# Quick: log all botocore wire-level details to stderr
boto3.set_stream_logger("") # root logger -- everything
boto3.set_stream_logger("botocore") # just botocore
# Botocore, log all botocore details
import logging
from botocore.session import Session
session = Session()
session.set_stream_logger('botocore', logging.DEBUG)
# OR: Configure logging to a file.
session.set_file_logger(logging.DEBUG, '/tmp/botocore.log')
set_stream_logger(name, level=logging.DEBUG) adds a
StreamHandler to the named logger. This is the idiomatic way to get
request/response debug output from the SDK.
Common Issues
Issue: ClientError import location
Wrong: from boto3.exceptions import ClientError
Right: from botocore.exceptions import ClientError
Service specific customizations
When writing any Python code that uses the following services, you MUST load these additional reference files for best practices and custom high level APIs:
- S3 - you MUST load
references/s3.md. - Dynamodb - you MUST load
references/dynamodb.md.
References
- Client configuration (retries, timeouts, endpoints):
references/configuration.md - Credentials and sessions:
references/credentials.md - Error handling patterns:
references/error-handling.md - Pagination:
references/pagination.md - Waiters:
references/waiters.md - S3 transfers and presigned URLs:
references/s3.md - DynamoDB operations:
references/dynamodb.md
FAQ
What does the aws-sdk-python-usage skill do?
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.
How do I install aws-sdk-python-usage?
Run npx skills add https://github.com/aws/agent-toolkit-for-aws --skill aws-sdk-python-usage in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/aws-sdk-python-usage/ (personal) or .claude/skills/aws-sdk-python-usage/ (project).
Which AI agents can use aws-sdk-python-usage?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is aws-sdk-python-usage safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains aws-sdk-python-usage?
It is published by aws in aws/agent-toolkit-for-aws, and it is an official skill from the maker of the technology it covers. It has 7,868 recorded installs.
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