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 creating-data-lake-tablegit 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/creating-data-lake-table)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/creating-data-lake-table"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/creating-data-lake-table.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00139 | $0.02060 |
| Opus 5 | $0.00069 | $0.01030 |
| Sonnet 5 | $0.00028 | $0.00412 |
| Haiku 4.5 | $0.00014 | $0.00206 |
Grade A, and why
creating-data-lake-table 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 2d 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
1 near-identical copy found in the catalogue:
- creating-data-lake-table — 95% identical, 28 lines differ
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Data Lake Tables with Amazon S3 Tables
Overview
Amazon S3 Tables provides managed Iceberg tables with automatic compaction and snapshot management. Queryable via Athena and Iceberg-compatible engines.
Common Tasks
You MUST use AWS MCP server tools when connected, they provide command validation, sandboxed execution, and audit logging. Fall back to AWS CLI if MCP unavailable.
Decision Guide
Before creating, You MUST check what exists:
You MUST run aws glue get-tables --database-name <NAME> when user mentions a database.
| What you find | Action |
|---|---|
| Fuzzy database name ("our analytics db") | You MUST STOP. Delegate to finding-data-lake-assets to resolve. |
| Non-S3-Tables table with matching name | You MUST STOP. Delegate to finding-data-lake-assets. You MUST NOT create until user confirms. |
| Existing S3 Tables table with matching name | You MUST check schema match. Reuse if compatible, recreate only if user confirms. |
| No matching tables | Proceed with creation (Steps 1-8). |
| User explicitly requests new S3 Tables table | Skip checks, proceed with creation. |
Creation paths:
- Existing data in S3: Create empty table (Steps 1-8), then use
ingesting-into-data-lakeskill. - Glue ETL pipeline: Read
references/table-creation-glue-etl.mdfirst, then Steps 1-6. - Lake Formation access control: Search AWS docs for
"S3 Tables integration with Lake Formation".
1. Verify Dependencies
Constraints:
- You MUST check whether AWS MCP server tools or AWS CLI are available and inform user if missing
- You MUST confirm target AWS region and verify credentials with
aws sts get-caller-identity
2. Understand the Schema
- Explicit schema: Validate Iceberg types.
- Loose description: Ask columns, types, grain. Propose and confirm.
- Existing S3 data: Infer schema from file headers only. Create empty table first, then use
ingesting-into-data-lakeskill.
Constraints:
What ships with it
4 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.
- 2d ago First seen · 195 lines · 139 tokens per session scan A ea61e74629cd
creating-data-lake-table is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,539 stars, last pushed yesterday), licensed Apache-2.0. It adds 139 tokens to every session and 2,060 once invoked, about $0.0007 per session on Opus 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 2026-09-03.
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