Agent Toolkit for AWS is a collection of AWS-supported MCP servers, skills, plugins, commands, and hooks that help AI coding agents build, deploy, and manage applications on AWS. It is used by developers working with AWS services through agents such as Claude Code, Codex, Cursor, and Kiro. The catalogue entries are the toolkit's own agent extensions for AWS development and operations.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usagegit clone --depth 1 https://github.com/aws/agent-toolkit-for-awsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aws/agent-toolkit-for-aws/aws-sdk-python-usage)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/aws-sdk-python-usage"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/aws-sdk-python-usage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/aws-sdk-python-usage"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/aws-sdk-python-usage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00120 | $0.01919 |
| Opus 5.5 | $0.00048 | $0.00768 |
| Sonnet 5.5 | $0.00024 | $0.00384 |
| Haiku 4.5 | $0.00012 | $0.00192 |
Grade A, and why
aws-sdk-python-usage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- aws-sdk-python-usage — 97% identical, 11 lines differ
- aws-sdk-python-usage — 95% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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()
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 261 lines · 120 tokens per session scan A f4e0bfd87dfc
aws-sdk-python-usage is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,892 stars, last pushed yesterday), licensed Apache-2.0. It adds 120 tokens to every session and 1,919 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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