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amazon-dynamodb

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

76

Quality

96%

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SecuritybySnyk

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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.

The content is a high-signal, executable axiom set with a well-sequenced pipeline, strong validation checkpoints, and clean progressive disclosure to real bundled references. Its main weakness is repetition of the same prohibitions across multiple sections, which inflates length without adding new signal.

Suggestions

Consolidate the repeated inline-arithmetic prohibition and stale-summary freshness checks into a single canonical statement and cross-reference it, rather than restating full versions in Cost estimation, Live validation, and Iterative design loop.

Tighten the teardown protocol (Live validation step 6 a–g) by collapsing the near-duplicate refusal/re-offer branches into one canonical pattern plus a short table of the conditions.

The 'Operating discipline' and 'Verify from evidence' guidance is restated in several places — define it once near the pipeline and reference it, to cut length while preserving the discipline.

DimensionReasoningScore

Conciseness

The body is densely packed with high-signal DynamoDB domain knowledge Claude lacks (notably the 'Facts you MUST NOT contradict' anti-prior-override section), but several prohibitions — inline arithmetic, stale-summary freshness checks, teardown attestation — are restated multiple times across sections and some guardrail paragraphs run long, which is more than minor trimming.

4 / 5

Actionability

Fully executable guidance throughout: a pipeline table mapping each stage to an exact command with flags, concrete benchmark_config.json fields, literal diff examples, and copy-paste-ready script invocations covering the common cases.

5 / 5

Workflow Clarity

A clearly sequenced six-stage pipeline with explicit validation checkpoints (verify summary freshness and config-match before interpreting, skew-vs-starvation gate) and feedback loops (re-run if stale), plus explicit consent gates for the destructive deploy/teardown operations.

5 / 5

Progressive Disclosure

Clear overview body with well-signaled one-level-deep references — the 'Reference architectures' section lists each references/*.md with a 'read this when…' cue, and all six referenced reference files and all eight referenced scripts exist in the bundle; detail is split out rather than inlined.

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.

The description is specific, comprehensive, and clearly distinguishes the skill's niche with explicit what-and-when triggers and natural user-facing keywords. It is long but every clause carries concrete capability or trigger signal rather than fluff.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions — 'enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes... zero-ETL integrations... produces a defensible data-layer design with a monthly cost estimate and optional live validation' — with comprehensive coverage of the DynamoDB data-layer surface.

5 / 5

Completeness

Explicitly answers both what (design/review/debug DynamoDB data layers and produce a defensible design with cost estimate) and when ('Applies whenever a user is designing, reviewing, or refactoring... even when they don't say "axioms" or "design review"'), with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive coverage of natural terms users would say — 'designing, reviewing, or refactoring', 'schemas, access patterns, GSIs', 'transactional outboxes', plus operational pain points like 'hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills', including synonyms.

5 / 5

Distinctiveness Conflict Risk

Clear DynamoDB-specific niche with distinct triggers (SearchVectors, Global Tables, zero-ETL to OpenSearch/Redshift/SageMaker Lakehouse) and minimal overlap risk with other skills; written in third person.

5 / 5

Total

20

/

20

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (707 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
aws/agent-toolkit-for-aws
Reviewed

Table of Contents

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