Creating Data Lake Table

What it does

When your agent uses it

• Data lake table
• Analytics table
• Structured data storage
• Partitioning strategy

Example prompts

• “/creating-data-lake-table”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

1. 1Verify Dependencies
2. 2Understand the Schema
3. 3Create Table Bucket
4. 4Create Namespace
5. 5Create Glue Data Catalog Integration
6. 6Configure Access Control
7. 7Create the Table
8. 8Verify and Confirm

What it can do on your machine

Read from SKILL.md and the folder at commit bd49cc8. It shows what the files ask for, not the result of running them.

• Tool permissions

  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.

• Runs code

  Shell commands in SKILL.md call:

  • aws

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md. Its commands use aws, which can reach the network depending on how they are called.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Creating Data Lake Table loads about 2.1k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 163 tokens; SKILL.md has 735 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~163

When it runs · the whole SKILL.md, loaded when a task matches

~2.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~6k

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.

Safety

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.

SKILL.md

The full file from aws/agent-toolkit-for-aws at commit bd49cc8, republished under its Apache-2.0 licence (© aws). 735 words, ~2,073 tokens.

name

creating-data-lake-table

description

Create managed Iceberg tables using Amazon S3 Tables (s3tables API namespace) with automatic compaction and snapshot management. Sets up table bucket, namespace, table, schema, Glue catalog registration, partitioning, IAM access control. Triggers on: create table, data lake table, analytics table, structured data storage, S3 Tables, Iceberg, Athena table, partitioning strategy, access permissions. Do NOT use for: importing files (use ingesting-into-data-lake), vector storage (use storing-and-querying-vectors), querying existing tables (use querying-data-lake), or locating existing table (use finding-data-lake-assets).

metadata.version

1

metadata.argument-hint

'[table-description|schema-spec]'

aws s3tables create-table-bucket --name <BUCKET_NAME> --region <REGION>

aws s3tables create-namespace --table-bucket-arn <ARN> --namespace <NAMESPACE>

aws glue get-catalog --catalog-id s3tablescatalog

aws glue create-catalog --name "s3tablescatalog" --catalog-input '{
  "FederatedCatalog": {
    "Identifier": "arn:aws:s3tables:<REGION>:<ACCOUNT_ID>:bucket/*",
    "ConnectionName": "aws:s3tables"
  },
  "CreateDatabaseDefaultPermissions": [{"Principal": {"DataLakePrincipalIdentifier": "IAM_ALLOWED_PRINCIPALS"}, "Permissions": ["ALL"]}],
  "CreateTableDefaultPermissions": [{"Principal": {"DataLakePrincipalIdentifier": "IAM_ALLOWED_PRINCIPALS"}, "Permissions": ["ALL"]}],
  "AllowFullTableExternalDataAccess": "True"
}'

aws s3tables create-table \
  --table-bucket-arn <ARN> \
  --namespace <NAMESPACE> \
  --name <TABLE_NAME> \
  --format ICEBERG \
  --metadata '<METADATA_JSON>'

{"iceberg":{"schema":{"fields":[
  {"name":"order_date","type":"date","required":true},
  {"name":"customer_id","type":"string","required":true},
  {"name":"amount","type":"double","required":false}
]},
"partitionSpec":{"fields":[
  {"sourceId":1,"fieldId":1000,"transform":"month","name":"order_date_month"}
]}}}

© 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

Files

SKILL.md and 4 other files (references) in plugins/aws-data-analytics/skills/creating-data-lake-table of aws/agent-toolkit-for-aws.

• SKILL.md
• references/access-control.md
• references/athena-ddl-path.md
• references/best-practices.md
• references/table-creation-glue-etl.md

Open the folder on GitHubat commit bd49cc8

Used in 1 other repository

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.

• Kilo-Org/kilo-marketplaceskills/creating-data-lake-table