Name: vector-database-engineer
Rating: 56.00000000000001 (1 reviews)
Author: administrakt0r

vector-database-engineer

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

Quality

63%

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Securityby

Passed

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/vector-database-engineer/SKILL.md

The canonical home for this skill is vector-database-engineer in administrakt0r/AI-Agents-Safe-Coding-Skills

Quality

Content

57%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 concise and logically organized with a clear workflow, but it stays at a high advisory level without concrete executable guidance or validation checkpoints, and its one external reference points to a file that is not present in the bundle.

Suggestions

Add concrete, actionable detail to key steps — example index configurations, sample CLI/API commands, or parameter values — so Claude can act rather than just plan.

Insert explicit validation checkpoints into the workflow (e.g. verify recall vs. latency after tuning, re-validate after reindexing) to support database/batch operations.

Create the referenced 'resources/implementation-playbook.md' or remove the dangling reference so progressive disclosure resolves to a real file.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean with short bulleted sections and no padding about what vector databases are, though generic filler lines like 'Apply relevant best practices and validate outcomes' and 'The task is unrelated to vector database engineer' could be trimmed.	4 / 5
Actionability	Steps are largely abstract ('Select appropriate embedding model', 'Configure metadata schema') with only a few concrete specifics (index names 'HNSW, IVF, PQ' and dimension range '384-1536'), lacking executable code, commands, or concrete configuration values needed to act precisely.	3 / 5
Workflow Clarity	An 8-step workflow is clearly sequenced, but it has no explicit validation checkpoints or feedback loops despite involving database/index operations at scale, which caps workflow clarity at 3 per the rubric.	3 / 5
Progressive Disclosure	The body is well-sectioned and points one level deep to 'resources/implementation-playbook.md', but that referenced file does not exist in the bundle, leaving the single progressive-disclosure pointer broken.	3 / 5
	Total	13 / 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.

The description clearly communicates a distinct vector-database niche with good keyword coverage and concrete tool names, but it lacks an explicit 'Use when...' trigger clause, which caps its completeness and keeps the trigger guidance weakly implied rather than explicit.

Suggestions

Add an explicit 'Use when...' clause naming concrete trigger situations, e.g. 'Use when building RAG, semantic search, or similarity-search systems over vector embeddings.'

Include natural synonyms such as 'similarity search' and 'nearest neighbor search' to improve trigger-term coverage.

Reframe 'Expert in'/'Masters' phrasing into concrete actions (e.g. 'select, index, and tune vector databases') to lift specificity.

Dimension	Reasoning	Score
Specificity	Names the domain and lists several concrete tools and application areas ('Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems'), though it describes expertise rather than enumerating concrete actions, keeping it just below a 5.	4 / 5
Completeness	Clearly answers 'what' the skill does, but the 'when' is only weakly implied via the use-case list and there is no explicit 'Use when...' trigger clause, which caps completeness at 3 per the rubric.	3 / 5
Trigger Term Quality	Good coverage of natural user terms ('vector databases', 'embedding', 'semantic search', 'RAG', 'recommendation systems'), but misses common synonyms like 'similarity search' or 'nearest neighbor search' in the description itself.	4 / 5
Distinctiveness Conflict Risk	A clear niche centered on vector databases with distinct, named-engine triggers gives minimal conflict risk with unrelated skills.	5 / 5
	Total	16 / 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/vector-database-engineer/SKILL.md
Commit: 900c504

Reviewed: 6 hours ago

Table of Contents

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