AWS CLI Beast
giuseppe-trisciuoglio/developer-kit
Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch.
AWS SDK for Python (boto3/botocore) development patterns. An agent skill from aws/agent-toolkit-for-aws.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-sdk-python-usage --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/core-skills/aws-sdk-python-usage .claude/skills/aws-sdk-python-usage && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "aws-sdk-python-usage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-sdk-python-usage into .claude/skills/aws-sdk-python-usage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-sdk-python-usage", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-sdk-python-usageType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-sdk-python-usage --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/core-skills/aws-sdk-python-usage .agents/skills/aws-sdk-python-usage && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "aws-sdk-python-usage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-sdk-python-usage into .agents/skills/aws-sdk-python-usage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-sdk-python-usage", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-sdk-python-usage --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/core-skills/aws-sdk-python-usage .cursor/skills/aws-sdk-python-usage && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "aws-sdk-python-usage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-sdk-python-usage into .cursor/skills/aws-sdk-python-usage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-sdk-python-usage", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws/agent-toolkit-for-aws.git --path skills/core-skills/aws-sdk-python-usage--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-sdk-python-usage --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/core-skills/aws-sdk-python-usage .gemini/skills/aws-sdk-python-usage && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "aws-sdk-python-usage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-sdk-python-usage into .gemini/skills/aws-sdk-python-usage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-sdk-python-usage", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws/agent-toolkit-for-aws aws-sdk-python-usageInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/core-skills/aws-sdk-python-usage .github/skills/aws-sdk-python-usage && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "aws-sdk-python-usage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-sdk-python-usage into .github/skills/aws-sdk-python-usage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-sdk-python-usage", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws/agent-toolkit-for-aws aws-sdk-python-usage --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/core-skills/aws-sdk-python-usage .opencode/skills/aws-sdk-python-usage && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "aws-sdk-python-usage" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/core-skills/aws-sdk-python-usage into .opencode/skills/aws-sdk-python-usage/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-sdk-python-usage", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
aws-sdk-python-usageAWS SDK for Python (boto3/botocore) development patterns. An agent skill from aws/agent-toolkit-for-aws.
AWS SDK Python Usage is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. 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…
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/configuration.md`, `references/credentials.md` and `references/dynamodb.md`).
It sits in Databases, covering File uploads and storage and NoSQL databases. It works with Amazon Web Services, Python and Amazon DynamoDB. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit bd49cc8. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
AWS SDK Python Usage loads about 2.1k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 139 tokens; SKILL.md has 585 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from aws/agent-toolkit-for-aws at commit bd49cc8, republished under its Apache-2.0 licence (© aws). 585 words, ~2,051 tokens.
.claude/skills/aws-sdk-python-usage/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.Do not use emojis in any code, comments, or output when this skill is active.
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.
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 keysUse 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).
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.
# 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.
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 itUse 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 1For the full error hierarchy and botocore exceptions, see references/error-handling.md.
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.
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.
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.
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.
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.
Wrong: from boto3.exceptions import ClientError
Right: from botocore.exceptions import ClientError
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:
references/s3.md.references/dynamodb.md.references/configuration.mdreferences/credentials.mdreferences/error-handling.mdreferences/pagination.mdreferences/waiters.mdreferences/s3.mdreferences/dynamodb.md© aws, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 7 other files (references) in skills/core-skills/aws-sdk-python-usage of aws/agent-toolkit-for-aws.
Open the folder on GitHubat commit bd49cc8
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in aws/agent-toolkit-for-aws, which our catalogue first saw on October 7, 2026.
AWS SDK Python Usage next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AWS SDK Python Usage this skillaws/agent-toolkit-for-aws | 2.8k | 1 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| AWS CLI Beastgiuseppe-trisciuoglio/developer-kit | 355 | — | ~1.7k | Automated safety check: Notes | MIT | |
| AWSkid-sid/claude-spellbook | 189 | — | ~4.9k | Automated safety check: Warn | MIT | |
| AWS Dynamodbalinaqi/maggy | 707 | — | ~4.6k | Automated safety check: Pass | MIT | |
| AWS Patternsvibeeval/vibecosystem | 531 | — | ~1.5k | Automated safety check: Pass | MIT | |
| AWS Cloud Patternsrohitg00/awesome-claude-code-toolkit | 2.7k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 |
giuseppe-trisciuoglio/developer-kit
Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch.
kid-sid/claude-spellbook
A skill your agent uses when writing boto3 or AWS SDK v3 code — configuring IAM auth, reading/writing S3, designing DynamoDB access patterns, writing Lambda handlers, processing SQS batches, or…
alinaqi/maggy
AWS DynamoDB single-table design, GSI patterns, SDK v3 TypeScript/Python
vibeeval/vibecosystem
Lambda best practices, S3 event patterns, SQS/SNS fanout, and DynamoDB access patterns for serverless AWS architectures.
rohitg00/awesome-claude-code-toolkit
AWS cloud patterns for Lambda, ECS, S3, DynamoDB, and Infrastructure as Code with CDK/Terraform
awslabs/agent-plugins
Build and deploy full-stack web and mobile apps with AWS Amplify Gen2 (TypeScript code-first).
aws/agent-toolkit-for-aws
Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow.
aws/agent-toolkit-for-aws
A skill your agent uses to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal.
aws/agent-toolkit-for-aws
Migrates vibe-coded web applications to AWS. An agent skill from aws/agent-toolkit-for-aws.
aws/agent-toolkit-for-aws
Deploy an event-driven workflow that routes S3 uploads to either Lambda or Fargate via Step Functions based on file size.
aws/agent-toolkit-for-aws
Deploys, queries, and debugs AWS Marketplace usage-based (PAYG) metering — the pipeline (ResolveCustomer, BatchMeterUsage, EventBridge via SAM) and querying/debugging metering records, statuses…
aws/agent-toolkit-for-aws
A skill your agent uses when THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits.
Works with
Categories
AWS SDK for Python (boto3/botocore) development patterns. An agent skill from aws/agent-toolkit-for-aws. AWS SDK Python Usage is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. AWS SDK for Python (boto3/botocore) development patterns.
AWS SDK Python Usage fits situations like: writing Python code that uses AWS services via boto3; Python code imports boto3; the user asks about AWS operations in Python.
Run `npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a claude-code`. Or copy the skill folder (skills/core-skills/aws-sdk-python-usage in aws/agent-toolkit-for-aws) into .claude/skills/aws-sdk-python-usage in your project. Claude Code loads it when a task matches its description.
Run `npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a codex`. Or copy the skill folder (skills/core-skills/aws-sdk-python-usage in aws/agent-toolkit-for-aws) into .agents/skills/aws-sdk-python-usage in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws/agent-toolkit-for-aws --skill aws-sdk-python-usage -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-sdk-python-usage, .gemini/skills/aws-sdk-python-usage, .github/skills/aws-sdk-python-usage and .opencode/skills/aws-sdk-python-usage in your project.
SKILL.md names no scripts, command-line tools or credentials: AWS SDK Python Usage is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
AWS SDK Python Usage is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.1k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with AWS SDK Python Usage: AWS CLI Beast (giuseppe-trisciuoglio/developer-kit, 355 stars), AWS (kid-sid/claude-spellbook, 189 stars), AWS Dynamodb (alinaqi/maggy, 707 stars) and AWS Patterns (vibeeval/vibecosystem, 531 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,816 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 7, 2026.
Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.