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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, 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 — 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.

73

Quality

92%

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%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The skill body is highly actionable, with a clear multi-stage workflow, explicit validation checkpoints, and well-structured one-level-deep references to real bundle files. Its one weakness is conciseness: several operational-discipline and refusal-condition passages are restated multiple times and could be consolidated without losing signal.

Suggestions

Consolidate the repeated restatements of the announce/act/verify operating discipline and the teardown refusal conditions into a single authoritative location, cross-referencing it instead of re-explaining it in Live validation, Iterative design loop, and Security considerations.

Tighten the stale-data and unit-cost-vs-load caveat paragraphs, which re-explain the same 'byte-identical numbers across runs' tell and the '~1 rps cannot exercise throttling' point several times in the Live validation section.

Consider moving the per-axiom 'Do NOT say ...' phrasings out of inline prose into a compact reference table under Facts you MUST NOT contradict to cut token weight while preserving the override signal.

DimensionReasoningScore

Conciseness

The document is mostly high-signal — exact transaction bounds, the four-fact disclosure, teardown protocol, and DynamoDB-specific facts Claude cannot reliably recall — but it restates the same discipline multiple times (announce/act/verify, the teardown refusal conditions, the stale-data tell, the unit-cost-vs-load caveat) and could be tightened; it sits at the mostly-efficient anchor rather than the lean every-token-earns-its-place anchor.

2 / 3

Actionability

Provides fully executable commands with `${SKILL_DIR}` substitution, concrete JSON field names, file names, a complete Python dedupe handler, and literal diff blocks — copy-paste ready guidance rather than pseudocode or abstract direction.

3 / 3

Workflow Clarity

The six-stage pipeline is clearly sequenced with explicit validation checkpoints (verify perf_summary.json freshness via benchmark_completed_at and config-match, the skew-vs-starvation gate, announce/act/verify) and feedback loops for destructive and batch operations (two-phase attested teardown, re-run-on-stale), matching the clear-sequence-with-validation anchor.

3 / 3

Progressive Disclosure

The body is an overview that signals one-level-deep references to real bundle files (all 5 references/*.md and 8 scripts/*.py verified to exist), with a Reference architectures section routing each file to a when-to-read context; schemas and format docs are split out rather than inlined, giving clear navigation.

3 / 3

Total

11

/

12

Passed

Description

100%

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, trigger-rich, complete (both what and when), and distinctively scoped to DynamoDB data-layer work; it uses third-person voice throughout with no over-claims. It is a strong, reference-quality description.

DimensionReasoningScore

Specificity

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

3 / 3

Completeness

Explicitly answers both what ('Designs, reviews, and debugs DynamoDB data layers ...') and when ('Applies whenever a user is designing, reviewing, or refactoring ... Also applies when debugging hot partitions ...'), with an explicit 'Use when'-equivalent trigger clause, so it is not capped at 2.

3 / 3

Trigger Term Quality

Strong coverage of natural terms users would say — 'DynamoDB', 'schemas', 'access patterns', 'GSIs', 'Streams', 'hot partitions', 'throttling', 'unbounded Scans', 'surprise bills' — plus debugging phrasings, matching the good-coverage anchor rather than the jargon-only level 1 or partial level 2.

3 / 3

Distinctiveness Conflict Risk

The niche is clearly DynamoDB data-layer design with distinct, narrowly-scoped triggers, making it unlikely to fire for unrelated skills; matches the clear-niche anchor.

3 / 3

Total

12

/

12

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 (636 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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