creating-data-lake-table

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).

Quality

96%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a well-structured, highly actionable 8-step workflow with explicit validation checkpoints, decision routing, and appropriately offloaded reference files. Its only weakness is minor verbosity in the overview and constraint phrasing.

Suggestions

Tighten the Overview to a single purpose statement and drop the generic "Queryable via Athena..." sentence since later steps already cover Athena usage.

Consolidate repeated "You MUST" constraint phrasing where several consecutive bullets restate the same obligation, to reduce token cost without losing force.

Dimension	Reasoning	Score
Conciseness	The body is largely efficient — commands, decision tables, and constraint blocks — but the Overview line ("Amazon S3 Tables provides managed Iceberg tables...") and some repeated "You MUST" phrasing add minor padding that could be trimmed, placing it just below the lean score-5 anchor.	4 / 5
Actionability	It provides copy-paste-ready `aws s3tables` and `aws glue` commands with complete JSON payloads and a concrete schema/partition metadata example covering the common cases, matching the score-5 anchor.	5 / 5
Workflow Clarity	Steps 1-8 are clearly sequenced with explicit validation checkpoints (sts get-caller-identity, list buckets/namespaces, get-table + DESCRIBE), a Decision Guide with STOP/delegate conditions, and a troubleshooting table, satisfying the score-5 anchor including feedback loops for the destructive create operation.	5 / 5
Progressive Disclosure	The body is a clear overview with four well-signaled one-level-deep references (best-practices.md, athena-ddl-path.md, table-creation-glue-etl.md, access-control.md), all verified to exist and summarized in Additional Resources, matching the score-5 anchor.	5 / 5
	Total	19 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is exemplary: it states concrete capabilities, lists natural trigger terms, explicitly covers when to use it, and draws clear boundaries against four sibling skills to avoid misrouting. No vague fluff or over-claims are present.

Dimension	Reasoning	Score
Specificity	"Sets up table bucket, namespace, table, schema, Glue catalog registration, partitioning, IAM access control" lists seven concrete actions with comprehensive coverage of the table-creation workflow, matching the score-5 anchor.	5 / 5
Completeness	It explicitly answers "what" (Create managed Iceberg tables using Amazon S3 Tables...) and "when" (Triggers on: ...) with concrete trigger phrases, matching the score-5 anchor; third-person voice is used throughout.	5 / 5
Trigger Term Quality	"Triggers on: create table, data lake table, analytics table, structured data storage, S3 Tables, Iceberg, Athena table, partitioning strategy, access permissions" provides comprehensive natural-term coverage with synonyms (no file extensions apply to this domain).	5 / 5
Distinctiveness Conflict Risk	"Do NOT use for: importing files (use ingesting-into-data-lake), vector storage..., querying existing tables..., locating existing table..." explicitly delineates four sibling skills, giving a clear niche with minimal conflict risk.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: aws/agent-toolkit-for-aws
Path: plugins/aws-data-analytics/skills/creating-data-lake-table/SKILL.md
Commit: 88a5fb3

Reviewed: ~18 hours ago

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