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storing-and-querying-vectors

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).

72

Quality

88%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

76%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.

A well-structured, actionable skill body with concrete AWS CLI commands, a clear sequenced workflow, and clean progressive disclosure into two reference files. Main weakness is the absence of explicit post-operation verification for batch/destructive operations.

Suggestions

Add an explicit post-put-vectors verification step (e.g., a small query or head-vector call) to confirm ingestion succeeded before proceeding.

Trim the overview marketing prose ('cost-effective... subsecond latency... as low as 100ms') since it does not guide action.

Provide a concrete retry-with-backoff snippet rather than only stating the constraint, since 429 handling is a batch-operation feedback loop.

DimensionReasoningScore

Conciseness

Mostly lean and actionable with concrete commands, but the overview paragraph and embedding-parsing prose ('The response key is model-dependent...') could be trimmed slightly. Not a 5 due to minor over-explanation.

4 / 5

Actionability

Copy-paste ready aws s3vectors and bedrock-runtime commands with concrete parameters cover the common cases (bucket, index, put, query, embeddings), plus a troubleshooting table for error recovery.

5 / 5

Workflow Clarity

Clear 6-step sequence with a pre-flight checklist, confirm-with-user checkpoints, and a 429 retry-with-backoff feedback loop; capped below 5 because there is no explicit post-operation verification (e.g., confirm vectors stored) for the batch/destructive operations.

4 / 5

Progressive Disclosure

SKILL.md is a concise overview with well-signaled, one-level-deep references (limits-and-patterns.md, metadata-filtering.md) listed both inline and in Additional Resources; content is appropriately split.

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.

A strong, third-person description that concretely states capabilities, enumerates natural trigger terms, and includes explicit negative-boundary guidance to avoid mis-triggering. It cleanly answers what, when, and when-not.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Store and query vector embeddings', 'create S3 vector bucket, vector index, store embeddings') with comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both what ('Store and query vector embeddings using Amazon S3 Vectors') and when ('Triggers on: ...') with concrete trigger phrases, plus a 'Do NOT use for' boundary.

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms including synonyms ('semantic search', 'similarity search', 'vector database', 'RAG vector storage') that users would naturally say.

5 / 5

Distinctiveness Conflict Risk

Clear niche (S3 Vectors / s3vectors namespace) with explicit redirects for overlapping cases (querying-data-lake, OpenSearch), 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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