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ingesting-into-data-lake

Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

77

Quality

96%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

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.

A well-structured overview skill: lean body, clear multi-step workflow with validation and feedback loops, and exemplary one-level-deep progressive disclosure into substantive reference files. The only gap is that the most executable code lives in references rather than inline.

DimensionReasoningScore

Conciseness

The body is a lean overview of directives, routing tables, gotchas, and a troubleshooting matrix with no padding of concepts Claude already knows; every section earns its place, matching the 'lean and efficient' anchor over the 'minor over-explanation' anchor at 4.

5 / 5

Actionability

Provides concrete executable commands (sts get-caller-identity, glue get-connection), a phrase-to-reference routing table, and a validation checklist, but the bulk of copy-paste PySpark templates and job config is delegated to references, so it sits at 'mostly executable with minor gaps' rather than fully copy-paste-ready in-body.

4 / 5

Workflow Clarity

A clearly sequenced 7-step workflow includes an explicit validation checkpoint (Step 6: row count, null check, sample rows), a stop-and-delegate gate in Step 3, and error-recovery feedback loops via the troubleshooting table, satisfying the anchor for explicit validation with feedback loops; the batch-operation cap does not apply because validation is present.

5 / 5

Progressive Disclosure

The overview points to 25 verified, substantive reference files all exactly one level deep, organized into categorized sections with descriptive link text and a routing table, matching the anchor for a clear overview with well-signaled one-level-deep references and easy navigation.

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.

An excellent description: concrete actions, comprehensive natural trigger phrases, explicit what-and-when structure, and clear negative-boundary guidance that distinguishes it from sibling skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions (import, migrate, convert to Iceberg, export DynamoDB, recurring pipelines) with comprehensive coverage of sources, targets, and load modes, matching the 'comprehensive coverage' anchor rather than the 'minor gaps' anchor at 4.

5 / 5

Completeness

Explicitly answers 'what' (import data into the AWS data lake from named sources, default S3 Tables target, one-time/recurring/migration modes) and 'when' via the concrete 'Triggers on:' phrase list, matching the anchor that requires both with concrete trigger phrases.

5 / 5

Trigger Term Quality

The 'Triggers on:' clause provides comprehensive natural user phrasing with synonyms (import/load/ingest/sync/move) and source-specific terms (pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS), matching the anchor for comprehensive coverage including synonyms.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche (AWS data-lake ingestion) and adds explicit 'Do NOT use for...' boundary guidance redirecting to five sibling skills plus SaaS exclusions, minimizing conflict risk per the anchor.

5 / 5

Total

20

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
aws/agent-toolkit-for-aws
Reviewed

Table of Contents

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