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amazon-aurora-postgresql

Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.

70

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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.

Well-structured content that acts as a routing overview with concrete, executable guidance, explicit safety validation for destructive operations, and clean one-level-deep progressive disclosure into reference files and scripts.

DimensionReasoningScore

Conciseness

Dense and actionable without explaining Aurora concepts Claude already knows; a few procedural passages could be tightened but every section earns its place.

4 / 5

Actionability

Provides concrete CLI flags, exact resource-tag syntax, copy-paste script commands, and literal two-paragraph refusal templates; minor gaps where routing guidance is descriptive rather than runnable code.

4 / 5

Workflow Clarity

A clear ordered 5-step operating procedure with edge-case handling and explicit validation checkpoints (confirm-before-mutation, Tier 3 refuse-then-alternative loop, error-then-suggest-fix retry) for destructive operations.

5 / 5

Progressive Disclosure

Body is an overview/router pointing one level deep to verified references/{id}-instructions.md files and bundled scripts, with clearly signaled navigation and no nested references.

5 / 5

Total

18

/

20

Passed

Description

87%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.

A strong, specific description that covers both what the skill does and when to trigger it, with clear engine-level disambiguation. It is concise without fluff and lists natural trigger terms.

DimensionReasoningScore

Specificity

Names the domain and lists several concrete actions ('creates, modifies, and advises') plus specific sub-domains (serverless, express, pgvector, Babelfish), with minor gaps versus a fully exhaustive action list.

4 / 5

Completeness

Explicitly answers both what ('creates, modifies, and advises on Aurora PostgreSQL clusters') and when ('Trigger for Aurora PostgreSQL cluster operations...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Covers natural trigger phrases users would say ('ACU sizing', 'I/O-Optimized storage', 'commitment pricing', 'PostgreSQL upgrade planning', 'express-configuration quick-start'), with a few synonyms missing.

4 / 5

Distinctiveness Conflict Risk

Engine-specific niche with an explicit disambiguation ('For Aurora MySQL, use amazon-aurora-mysql instead') giving minimal conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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
Reviewed

Table of Contents

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