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similarity-search-patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

64

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

65%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 skill is highly actionable with complete executable code across four vector databases, but it is token-heavy with all implementations inlined and lacks a sequenced workflow with validation checkpoints for its batch and destructive operations.

Suggestions

Add explicit validation/verification checkpoints (e.g., confirm upsert counts, verify recall after indexing, dry-run before delete_by_filter) for the batch upsert and delete operations to lift workflow_clarity above the batch-operation cap of 3.

Split the four vendor implementations into one-level-deep reference files (e.g., references/pinecone.md, references/qdrant.md) and keep SKILL.md as an overview with clearly signaled links, improving progressive_disclosure and token efficiency.

Condense the duplicated boilerplate across vendor classes or present a decision table first so the body guides selection before the full code.

DimensionReasoningScore

Conciseness

The body is mostly efficient and free of explanatory padding about basic concepts, but four full vendor implementation classes (~460 lines of code) is heavy; the same patterns could be condensed or moved to reference files to better respect the token budget.

3 / 5

Actionability

Provides fully executable, copy-paste-ready Python classes for Pinecone, Qdrant, pgvector, and Weaviate covering upsert, search, hybrid search, filtering, and reranking, which covers the common cases concretely.

5 / 5

Workflow Clarity

Content is organized by vendor rather than as a sequenced workflow, and batch/destructive operations (bulk upsert, delete, delete_by_filter) lack explicit validation or verification checkpoints, capping this dimension at 3 per the rubric's batch-operation rule.

3 / 5

Progressive Disclosure

No bundle files exist, yet four large vendor implementations are inlined in SKILL.md; content that clearly belongs in separate reference files is kept inline with no signaled one-level-deep references, fitting the 'could be better organized / inline content that should be separate' anchor.

3 / 5

Total

14

/

20

Passed

Description

91%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 clear, specific, and explicitly answers both 'what' and 'when' with natural trigger terms. It is slightly broad in framing, creating minor overlap risk with general search/retrieval skills.

DimensionReasoningScore

Specificity

Names the domain (similarity search with vector databases) and lists several concrete actions via the 'Use when' clause ('building semantic search, implementing nearest neighbor queries, optimizing retrieval performance'), though the actions are phrased as triggers rather than as capabilities the skill itself performs.

4 / 5

Completeness

Explicitly answers 'what' ('Implement efficient similarity search with vector databases') and 'when' ('Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Covers natural terms users would say ('semantic search', 'nearest neighbor queries', 'retrieval performance', 'vector databases') including synonyms and common variations, aligning with the comprehensive coverage anchor.

5 / 5

Distinctiveness Conflict Risk

The 'similarity search with vector databases' niche is fairly distinct with clear triggers, but broad retrieval/search framing creates minor overlap risk with adjacent RAG or search-skills.

4 / 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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (559 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

foryourhealth111-pixel/Vibe-Skills

Path

bundled/skills/similarity-search-patterns/SKILL.md

Reviewed

Table of Contents

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