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 connecting-to-data-sourcegit 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/connecting-to-data-source)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/connecting-to-data-source"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/connecting-to-data-source.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 | $0.00171 | $0.02110 |
| Opus 5 | $0.00086 | $0.01055 |
| Sonnet 5 | $0.00034 | $0.00422 |
| Haiku 4.5 | $0.00017 | $0.00211 |
Grade A, and why
connecting-to-data-source 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 3d 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:
- connecting-to-data-source — 95% identical, 31 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Connect to Data Source
Register an external data source with AWS Glue so downstream skills (ingesting-into-data-lake) can move data from it. A Glue connection stores the network config, driver, and credential reference for one source. Create once per source, reuse across jobs.
Philosophy
A connection is a named pipe, not a pipeline. This skill produces a tested, reusable Glue connection. It does not move data.
Common Tasks
You MUST execute commands using AWS MCP server tools when connected -- they provide validation, sandboxed execution, and audit logging. Fall back to AWS CLI only if MCP is unavailable. You MUST explain each step before executing.
Workflow
1. Verify Dependencies and Context
- You MUST check whether AWS MCP tools or AWS CLI are available and inform the user if missing
- You MUST confirm target AWS region and verify credentials with
aws sts get-caller-identity
2. Classify the Source
Ask the user which source type they want to connect to, or infer from hints:
| User says... | Source type | Connection type | Reference |
|---|---|---|---|
| "Oracle", "SQL Server", "Postgres", "MySQL", "RDS <engine>" | JDBC database | JDBC |
jdbc-setup.md |
| "Redshift", "my cluster", "my data warehouse on AWS" | Redshift | JDBC |
jdbc-setup.md (Redshift section) |
| "Snowflake" | Snowflake | SNOWFLAKE |
snowflake-setup.md |
| "BigQuery", "Google analytics warehouse" | BigQuery | BIGQUERY |
bigquery-setup.md |
If the user names DynamoDB or a local file, stop and tell them: DynamoDB is read directly by Glue without a connection, and local files belong in the ingesting-into-data-lake skill's local-upload workflow.
3. Gather Connection Hints from the User
You MUST ask for hints the user can provide -- do not guess.
For all sources:
- Desired connection name (lowercase, hyphens:
oracle-prod-sales,snowflake-analytics) - Existing Secrets Manager secret, or create one
- Is source reachable from a Glue VPC (same, peered, VPN, Direct Connect)
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.
- 3d ago First seen · 172 lines · 171 tokens per session scan A ddc3867db09d
connecting-to-data-source is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,539 stars, last pushed 2d ago), licensed Apache-2.0. It adds 171 tokens to every session and 2,110 once invoked, about $0.0009 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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