similarity-search-patterns

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

Quality

56%

Does it follow best practices?

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/similarity-search-patterns/SKILL.md

The canonical home for this skill is similarity-search-patterns in rmyndharis/antigravity-skills

Quality

Content

43%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 body is admirably concise and well-structured, but it offers no concrete executable guidance and its only detailed reference points to a file that is absent from the bundle, leaving it shallow and unverifiable.

Suggestions

Replace abstract instructions ("Apply relevant best practices") with concrete, actionable guidance: named libraries (e.g., FAISS, pgvector, HNSW), index-building steps, and example query code.

Add explicit validation checkpoints for the workflow (e.g., verify recall@k against a held-out set, check latency budgets) instead of the generic "validate outcomes" bullet.

Either create resources/implementation-playbook.md so the referenced path resolves, or remove the broken reference and inline the key patterns.

Dimension	Reasoning	Score
Conciseness	The body is a lean, well-sectioned set of bullets with no padding and no over-explanation of concepts Claude already knows; every line earns its place.	5 / 5
Actionability	Instructions are entirely abstract ("Apply relevant best practices and validate outcomes", "Provide actionable steps and verification") with no concrete code, commands, library names, or specific steps, matching the vague/abstract anchor.	1 / 5
Workflow Clarity	A rough sequence is present (clarify, apply, verify) but steps are generic and poorly defined, and validation is only mentioned abstractly ("validate outcomes") without checkpoints or feedback loops.	2 / 5
Progressive Disclosure	The body is well-sectioned and signals a one-level-deep reference (resources/implementation-playbook.md), but that referenced file does not exist in the bundle, so navigation is incomplete.	3 / 5
	Total	11 / 20 Passed

Description

70%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 clear, third-person description that names a specific domain and concrete capabilities with an explicit Use-when clause. It is distinguishable from sibling skills and uses natural trigger terms, though it could add more synonyms and concrete action verbs to reach the top anchor.

Suggestions

Add concrete action verbs (e.g., "build, index, query, and tune vector search") to raise specificity toward comprehensive coverage.

Include additional natural synonyms users say, such as "ANN", "embeddings", and "vector search", to broaden trigger-term coverage.

Tighten the when-clause with file-format or tooling cues (e.g., pgvector, FAISS, indexes) to sharpen distinctiveness.

Dimension	Reasoning	Score
Specificity	"Implement efficient similarity search with vector databases" names the domain plus concrete actions (implementing search, nearest neighbor queries, optimizing retrieval), matching the anchor that lists domain and a couple concrete actions without comprehensive coverage.	3 / 5
Completeness	Explicitly answers both 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, though the when-clause could be slightly more comprehensive.	4 / 5
Trigger Term Quality	Includes natural user-facing terms like "semantic search", "nearest neighbor queries", and "retrieval performance" with good coverage, but omits common synonyms such as "ANN", "embeddings", or "vector search".	4 / 5
Distinctiveness Conflict Risk	"Similarity search with vector databases" carves out a clear niche with distinct triggers and only minor overlap risk with closely related retrieval/database skills.	4 / 5
	Total	15 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/similarity-search-patterns/SKILL.md
Commit: 57c135b

Reviewed: 19 hours ago

Table of Contents

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