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aws-database

Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.

68

Quality

83%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

80%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 routing skill: concise, actionable, with a clear match-to-sub-skill workflow and excellent progressive disclosure through verified one-level-deep references. The main gap is the absence of an explicit validation/retry checkpoint, though the operations here are advisory rather than destructive.

DimensionReasoningScore

Conciseness

Mostly lean routing guidance that assumes Claude's competence and avoids explaining basic database concepts, with only minor restatement of the service catalog that could be trimmed.

4 / 5

Actionability

Provides concrete, executable routing steps — explicit file paths like references/{sub-skill-id}.md and assets/{filename}, a lookup table of services, and named MCP tools to call — with only minor gaps in the fallback lookup ordering.

4 / 5

Workflow Clarity

Clear sequenced routing flow with explicit re-routing on pushback and a defined fallback chain for facts not in cards; lacks an explicit validate-and-retry checkpoint but the routing decision itself is low-risk.

4 / 5

Progressive Disclosure

A clean overview body pointing one level deep to verified reference files (references/select.md, handoff.md, report-issue.md) and on-demand asset cards, all clearly signaled in tables with easy navigation.

5 / 5

Total

17

/

20

Passed

Description

86%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 highly specific and complete, explicitly covering both what the skill does and when it activates, with concrete service families and natural trigger phrases. Minor room to add more everyday synonyms to push trigger quality to a perfect score.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — choosing, comparing, recommending, getting started with, and operating databases — and enumerates the full set of service families and data models, giving comprehensive coverage.

5 / 5

Completeness

Clearly answers both 'what' (routes database tasks to service-specific skills, supersedes training data) and 'when' (explicit 'Activates when a user describes building an application... even if they do not mention database explicitly').

5 / 5

Trigger Term Quality

Strong natural triggers like 'choosing', 'recommending', 'getting started with', and 'building an application on AWS', but missing several common variations and explicit data-model synonyms a user might say.

4 / 5

Distinctiveness Conflict Risk

Scoped clearly to AWS databases with a distinct routing niche, though the breadth of 'any task involving AWS databases' creates minor overlap risk with the individual service skills it routes to.

4 / 5

Total

18

/

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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