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 ingesting-into-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/ingesting-into-data-lake)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/ingesting-into-data-lake"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/ingesting-into-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/ingesting-into-data-lake"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/ingesting-into-data-lake.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk warn
- 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 Data Exfiltration · line 172 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
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 | $0.00228 | $0.02635 |
| Opus 5.5 | $0.00091 | $0.01054 |
| Sonnet 5.5 | $0.00046 | $0.00527 |
| Haiku 4.5 | $0.00023 | $0.00264 |
Grade A, and why
ingesting-into-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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ingesting-into-data-lake — 95% identical, 39 lines differ
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest into Data Lake
Move data from a source into a queryable table in the data lake. This skill assumes the source connection (if one is needed) already exists. For Glue connection setup or troubleshooting, delegate to connecting-to-data-source.
Philosophy
Default to S3 Tables unless the environment says otherwise. S3 Tables is the recommended target for new data lake work. If the user's catalog inventory shows they haven't adopted S3 Tables, recommend standard Iceberg on their existing general-purpose bucket instead of forcing them to change posture.
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 - For SageMaker Unified Studio project roles, note that target tables and connections may be scoped to the project. See the caller ARN detection pattern in
querying-data-lake.
2. Classify the Source
| User says... | Source type | Reference |
|---|---|---|
| "upload my file", "local CSV", "move to S3" | Local file | local-upload.md |
| "load from S3", "import CSV/JSON/Parquet from s3://" | S3 files | s3-files.md |
| "import from Oracle/Postgres/MySQL/SQL Server/Redshift/RDS/Aurora" | JDBC | jdbc-ingest.md |
| "pull from Snowflake", "Snowflake table to S3" | Snowflake | snowflake-ingest.md |
| "import from BigQuery", "GCP analytics to S3" | BigQuery | bigquery-ingest.md |
| "export DynamoDB", "DynamoDB to data lake" | DynamoDB | dynamodb-ingest.md |
| "migrate Glue table", "convert Hive to Iceberg" | Catalog migration | catalog-migration.md |
What ships with it
25 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.
- references/athena-loading.md 2.8 KB
- references/bigquery-ingest.md 3.7 KB
- references/catalog-migration.md 6.9 KB
- references/ctas-patterns.md 3.0 KB
- references/data-quality-validation.md 12 KB
- references/dynamodb-ingest.md 7.2 KB
- references/error-handling.md 12 KB
- references/format-specific-loading.md 13 KB
- references/glue-etl-migration.md 4.5 KB
- references/glue-job-config.md 9.4 KB
- references/glue-job-scripts.md 9.6 KB
- references/iceberg-catalog-config-and-usage.md 7.9 KB
- references/incremental-loading.md 13 KB
- references/jdbc-ingest.md 5.7 KB
- references/jdbc-performance.md 10 KB
- references/jdbc-schema-discovery.md 13 KB
- references/local-upload.md 4.5 KB
- references/migration-troubleshooting.md 2.6 KB
- references/migration-validation.md 2.7 KB
- references/s3-files.md 5.7 KB
- references/schema-evolution.md 9.3 KB
- references/snowflake-ingest.md 3.6 KB
- references/testing-and-scheduling.md 14 KB
- references/type-transformations.md 7.8 KB
- references/upload-options.md 1.0 KB
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 · 185 lines · 228 tokens per session scan A 0de68643aa09
ingesting-into-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 228 tokens to every session and 2,635 once invoked, about $0.0009 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.
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