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vector-database-engineer

Design vector storage, indexing, and semantic retrieval with Pinecone, Weaviate, Qdrant, Milvus, or pgvector. Use when implementing RAG, similarity search, or recommendation systems.

Quality

69%

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tessl review fix ./skills/vector-database-engineer/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

38%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 body is a generic, high-level overview padded with filler: it duplicates the frontmatter description, includes a tautological 'Do not use' section, and offers 'Instructions' that apply to any skill. It provides no executable guidance — no code, commands, or concrete configuration values — and its workflow lacks validation checkpoints. Section organization is reasonable, but the body adds little beyond what Claude already knows about vector databases.

Suggestions

Remove token-wasting filler: the duplicated intro description, the 'Use PROACTIVELY' line, the 'Use this skill when' section (already covered by the frontmatter description), the tautological 'Do not use this skill when' section, and the generic 'Instructions' section.

Add executable guidance for the lowest-scoring dimension: a code example creating an index or collection (e.g., Qdrant create_collection or pgvector CREATE EXTENSION plus an HNSW index DDL), a hybrid search query snippet, and concrete HNSW/IVF parameter values (m, ef_construction, nlist) instead of only naming index types.

Insert validation checkpoints into the workflow: measure recall@k against a labeled ground-truth set before and after index or tuning changes, and benchmark p99 latency after each configuration change before proceeding.

DimensionReasoningScore

Conciseness

Several padded sections waste tokens: the intro paragraph ("Expert in vector databases... Masters Pinecone, Weaviate...") duplicates the frontmatter description, "Do not use this skill when — The task is unrelated to vector database engineer" is tautological, and "Instructions" ("Clarify goals, constraints, and required inputs") is generic filler applicable to any skill. Not 1 because it never extensively explains basic concepts; not 3 because the padding is more than "some unnecessary explanation".

2 / 5

Actionability

Guidance is high-level ("Implement hybrid search if needed", "Optimize for latency/recall tradeoffs") with only scattered specifics ("384-1536", "HNSW, IVF, PQ") and no code, commands, or parameter values to execute. Not 1 because a few concrete details exist; not 3 because nothing approaches executable or pseudocode-level guidance.

2 / 5

Workflow Clarity

The 8-step workflow (analyze data → select embedding model → ... → set up monitoring) is coherently sequenced, but validation checkpoints are entirely absent — "Test recall vs latency tradeoffs" appears only as a best practice, not a workflow step. Not 4 because checkpoints are missing altogether rather than having minor gaps.

3 / 5

Progressive Disclosure

Sections are well-organized with headers, no references are made to nonexistent files, and no bundle files exist to misalign with. Not 5 because the skill spans five databases and advanced topics (hybrid search, index tuning) with zero depth, where one-level reference files would be the appropriate split; not 3 because the inline content is short, not material that clearly belongs in separate files.

4 / 5

Total

11

/

20

Passed

Description

83%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 and explicitly answers both what the skill does and when to use it, with concrete trigger phrases (RAG, similarity search, recommendation systems) and five named databases. Trigger coverage is good but omits natural synonyms like "semantic search" and "embeddings". Overall a strong description with only minor gaps.

DimensionReasoningScore

Specificity

"Design vector storage, indexing, and semantic retrieval" lists several concrete actions and names five specific tools (Pinecone, Weaviate, Qdrant, Milvus, pgvector). It falls short of 5 because coverage has minor gaps (no mention of embedding, chunking, or hybrid search) and above 3 because more than 1-2 actions are specified.

4 / 5

Completeness

Both parts are explicit: the "what" ("Design vector storage, indexing, and semantic retrieval with Pinecone, Weaviate, Qdrant, Milvus, or pgvector") and the "when" ("Use when implementing RAG, similarity search, or recommendation systems") with concrete trigger phrases. Not 4 because the when-clause is already explicit and specific rather than needing more detail.

5 / 5

Trigger Term Quality

"RAG, similarity search, or recommendation systems" are natural phrases users would say when needing this skill. Not 5 because common variations like "semantic search", "vector database", and "embeddings" are missing from the trigger list.

4 / 5

Distinctiveness Conflict Risk

The vector-database niche is clear and the five named tools make it mostly distinct from other skills. Not 5 because "RAG" is a broad trigger that could overlap with general RAG or embedding-focused skills; not 3 because the named tools and focus keep conflict risk minor.

4 / 5

Total

17

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

rmyndharis/antigravity-skills

Path

skills/vector-database-engineer/SKILL.md

Reviewed

Table of Contents

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