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connecting-to-data-source

Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).

75

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%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 a well-structured, actionable workflow with explicit validation checkpoints, feedback loops, and clean progressive disclosure into seven real reference files. Its only weakness is mild stylistic verbosity and the create-connection payload being deferred rather than shown inline.

Suggestions

Trim the "Philosophy" section and tighten the two-phase testing prose to push conciseness toward the lean anchor-5.

Show a minimal inline --connection-input JSON skeleton (e.g., for a JDBC source) so the core creation step is fully copy-paste-ready without requiring a reference file.

Consider collapsing the duplicated "Troubleshooting" section and the Step 8 troubleshoot block to reduce redundancy.

DimensionReasoningScore

Conciseness

The body is efficient — tables, command snippets, and bullet lists with no over-explanation of concepts Claude already knows — but the stylistic "Philosophy" section ("A connection is a named pipe, not a pipeline") and a few explanatory sentences around two-phase testing could be trimmed, keeping it just below the lean anchor-5.

4 / 5

Actionability

Concrete, copy-paste-ready AWS CLI commands appear throughout (aws sts get-caller-identity, aws glue get-connections, aws glue test-connection) with standard templated parameters; the one minor gap is that the core create-connection command's --connection-input payload is a "<JSON>" placeholder rather than shown inline, though it is justifiably deferred to per-source reference files.

4 / 5

Workflow Clarity

An 8-step numbered workflow with explicit validation checkpoints — Step 1 dependency/credential verification and Step 7 two-phase testing (Phase A API check + Phase B engine-level verification) — plus a feedback loop (test failure routes to Step 8 troubleshoot) and MUST constraints on risky operations (no credential rotation without confirmation), satisfying the validation requirement that would otherwise cap this score.

5 / 5

Progressive Disclosure

SKILL.md is a clear overview with seven one-level-deep reference files, all of which exist and are signaled inline at each relevant step and consolidated in a References section; content is appropriately split (per-source setup details live in separate files, the workflow and gotchas stay in the overview), giving easy navigation.

5 / 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 exemplary: it states concrete capabilities, lists natural trigger phrases, explicitly marks when to use the skill, and disambiguates it from sibling skills with a clear exclusion clause. No vague fluff or over-claims are present.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions — "Create and troubleshoot", "Gathers connection hints", "discovers existing connections", "registers credentials", "configures VPC", and "tests" — giving comprehensive coverage of what the skill does.

5 / 5

Completeness

Explicitly answers both "what" (create/troubleshoot Glue connections and the enumerated sub-actions) and "when" (a dedicated "Triggers on:" clause with concrete trigger phrases), matching the anchor-5 pattern exactly.

5 / 5

Trigger Term Quality

The "Triggers on:" clause lists natural user phrases ("connect to database", "set up Glue connection", "register data source", "connection timeout", "test connection") plus concrete source names/synonyms (Snowflake, BigQuery, RDS), matching the comprehensive-coverage anchor.

5 / 5

Distinctiveness Conflict Risk

A clear niche (Glue connections to data sources) with an explicit "Do NOT use for..." clause routing to sibling skills (ingesting-into-data-lake, creating-data-lake-table, querying-data-lake, exploring-data-catalog) and excluding SaaS, minimizing conflict risk.

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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