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 querying-data-lakegit 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/querying-data-lake)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/querying-data-lake"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/querying-data-lake/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/querying-data-lake"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/querying-data-lake.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 132 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00105 | $0.01714 |
| Opus 5.5 | $0.00042 | $0.00686 |
| Sonnet 5.5 | $0.00021 | $0.00343 |
| Haiku 4.5 | $0.00011 | $0.00171 |
Grade A, and why
querying-data-lake 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.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Query Data Lake
Execute SQL queries on Amazon Athena across default and federated catalogs (Glue, S3 Tables, Redshift) with workgroup selection, statement classification, and error recovery.
Overview
Executes and manages Athena SQL queries across default and federated catalogs. Selects a workgroup, resolves target assets (delegating fuzzy references to finding-data-lake-assets), classifies statements for safety, and reports cost and data scanned. Use the AWS MCP server for sandboxed execution and audit logging; the same AWS CLI commands work directly when the MCP server is not available.
Constraints for parameter acquisition:
- You MUST accept a single optional argument: SQL text, a named-query name, a workgroup name, a catalog name, or
profile TABLE_NAME - You MUST accept the argument as direct text or a pointer to a file containing SQL
- You MUST ask the user for the target AWS region if not already set
- You MUST confirm the output S3 location before executing any non-trivial query
- You MUST respect the user's decision to abort at any step
Common Tasks
1. Verify Dependencies
Check for required tools and AWS access before running queries.
Constraints:
- You MUST verify AWS MCP server tools are available (
aws___call_aws) and run queries through them when present; fall back to AWS CLI only if the MCP server is unavailable - You MUST NOT fall back to shell or Bash for query execution — results must be captured via the MCP tool or
aws athenaCLI so output location and cost are tracked - You MUST confirm credentials with
aws sts get-caller-identityand inform the user about any missing tools
2. Resolve Workgroup
Check caller identity, list workgroups, auto-select the best one (see workgroup-selection.md).
Constraints:
- You MUST select a workgroup before submitting any query (prevents output-location errors)
- You MUST present the selected workgroup and its output location to the user
- You MUST NOT auto-escalate to a different workgroup on failure without user confirmation
What ships with it
2 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 · 140 lines · 105 tokens per session scan A 2723babdbd4c
querying-data-lake is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,927 stars, last pushed today), licensed Apache-2.0. It adds 105 tokens to every session and 1,714 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
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atlas-mssql
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ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
nornicdb-cypher-queries
Pick fast, predictable Cypher query shapes in NornicDB — point lookups, batch retrieval, pagination, search, traversal, batched UNWIND/MERGE writes, cleanup, multi-tenant isolation. Use when writing or reviewing Cypher whose latency or throughput matters; maps user intent to the executor's hot-path query templates.
volcengine-rds-postgresql
A tool for operating PostgreSQL databases hosted by Volcano Engine's managed database service. PostgreSQL is a relational database used to store structured application data.