querying-aws-sagemaker-catalog

Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog analytics.

Quality

94%

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Securityby

Passed

No findings from the security scan

Quality

Content

88%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 body is highly actionable with executable commands and SQL, a clear sequenced workflow with validation checkpoints, and a useful troubleshooting table. Minor conciseness gains are possible by deduplicating constraints and splitting some dense reference material.

Suggestions

Dedupe the snapshot_time/workgroup constraints between the Query 'Constraints' block and 'Key Behaviors' to save tokens.

Consider moving the full set of SQL examples or the Security Considerations detail into a reference file with a clearly signaled pointer, improving progressive disclosure.

Trim the 'Works best with the AWS MCP server' note unless it is essential to the core workflow.

Dimension	Reasoning	Score
Conciseness	Efficient and assumes competence without over-explaining basics, but some constraints are restated between the Query 'Constraints' block and the 'Key Behaviors' section, and the MCP-server note adds minor non-essential prose.	4 / 5
Actionability	Provides fully executable AWS CLI commands and copy-paste SQL queries covering the common cases (current state, missing descriptions, growth, time-travel, ownership, metadata-form filter), matching the anchor for fully executable guidance.	5 / 5
Workflow Clarity	Sequenced workflow (configure, enable, verify permissions, query) with explicit MUST checkpoints ('always filter by snapshot_time', 'confirm workgroup and output location') and a troubleshooting table providing error-recovery feedback loops.	5 / 5
Progressive Disclosure	Well-organized into clear sections with one-level-deep external AWS doc references in Additional Resources; no nested references. Scored 4 rather than 5 because all content is inline with no signaling to split dense SQL/security material into reference files.	4 / 5
	Total	18 / 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 specific, complete, and well-differentiated, explicitly covering both capabilities and trigger conditions with natural user phrases. It is a strong example of a third-person, action-oriented skill description.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis') with comprehensive coverage, matching the anchor for multiple specific concrete actions.	5 / 5
Completeness	Clearly answers both 'what' ('Runs SQL analytics... Covers governance queries...') and 'when' ('Applies when querying catalog inventory, finding assets without descriptions...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Explicit trigger phrases ('how many assets, assets without descriptions, who owns this data, catalog governance, data quality audit') are natural terms users would say, with good synonym coverage.	5 / 5
Distinctiveness Conflict Risk	Targets a clear niche (SQL analytics on SageMaker Catalog asset metadata Iceberg tables) with distinct triggers and minimal overlap risk with related data-lake skills.	5 / 5
	Total	20 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aws/agent-toolkit-for-aws
Path: skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog/SKILL.md
Commit: d6ad2e4

Reviewed: about 3 hours ago

Table of Contents

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