This is a high-quality skill that provides clear, actionable, and well-sequenced instructions for adding similarity threshold guards to vector retrieval RPCs. The SQL is executable and complete, the workflow has explicit halt conditions and validation steps, and the content respects Claude's intelligence by avoiding unnecessary explanations. The only minor weakness is that progressive disclosure is limited to a single prerequisite reference rather than linking to supplementary materials, though for a skill of this size that's a minor concern.

Dimension	Reasoning	Score
Conciseness	The content is lean and efficient. It doesn't explain what vectors, embeddings, or RPCs are—it assumes Claude knows. Every section serves a purpose: preconditions, execution steps, SQL example, and verification. No filler.	3 / 3
Actionability	Provides a complete, executable SQL function with specific parameter names, types, defaults, and a guard clause. Migration steps, permission grants, and verification calls are all concrete and specific rather than abstract.	3 / 3
Workflow Clarity	Clear three-phase sequence (Create RPC → Apply Migration → Grant Permissions) with explicit HALT-on-failure checkpoints. The verification report includes specific test cases with expected outcomes (exception, empty set), forming a proper validation loop.	3 / 3
Progressive Disclosure	References the prerequisite `hybrid-search-rrf-pattern` skill clearly, but all content is inline in a single file. For this skill's complexity level it's borderline acceptable, but the SQL example and migration guidance could benefit from a separate reference file if the pattern grows. The structure is good but doesn't demonstrate one-level-deep linking to supplementary materials.	2 / 3
	Total	11 / 12 Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is an excellent skill description that clearly communicates a narrow, well-defined capability with specific actions, explicit trigger guidance, and strong natural keywords. It uses proper third-person voice throughout and provides enough detail to distinguish it from related but different skills in the RAG/vector search domain.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'adds hard similarity threshold guards to vector retrieval RPCs', 'returns empty results instead of weak matches', 'prevent downstream hallucination'. These are precise, actionable capabilities.	3 / 3
Completeness	Clearly answers both 'what' (adds similarity threshold guards, returns empty results instead of weak matches) and 'when' with an explicit 'Use when' clause listing specific trigger scenarios like implementing retrieval guardrails, minimum similarity thresholds, match_threshold parameters, or preventing RAG hallucination.	3 / 3
Trigger Term Quality	Includes strong natural keywords users would say: 'similarity threshold', 'vector retrieval', 'retrieval guardrails', 'match_threshold', 'RAG hallucination', 'low-confidence results'. Good coverage of both technical terms and conceptual phrases a developer would use.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive niche: specifically about similarity threshold guards on vector retrieval RPCs for RAG quality. Unlikely to conflict with general RAG skills, vector DB skills, or other retrieval skills due to the narrow focus on threshold-based filtering and hallucination prevention.	3 / 3
	Total	12 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Reviewed

2 months ago

Table of Contents

Discovery Implementation Validation