Names the domain (search/retrieval) and the core action (combine vector and keyword search), but doesn't list multiple concrete actions like specific techniques, algorithms, or implementation steps.

Clearly answers both what ('Combine vector and keyword search for improved retrieval') and when ('Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall') with explicit trigger guidance.

Includes strong natural keywords users would say: 'vector search', 'keyword search', 'RAG systems', 'search engines', 'retrieval', 'recall' - these are terms practitioners naturally use when discussing hybrid search.

Has a clear niche (hybrid search combining vector + keyword) with distinct triggers like 'RAG systems' and the specific combination approach - unlikely to conflict with pure vector search or pure keyword search skills.

The skill is mostly efficient with good code examples, but includes some unnecessary elements like the ASCII diagram and the 'When to Use This Skill' section that explains concepts Claude would understand from context. The templates are comprehensive but could be more selective.

Provides fully executable Python code with complete implementations for RRF fusion, PostgreSQL hybrid search, Elasticsearch integration, and a custom RAG pipeline. All code is copy-paste ready with proper type hints and docstrings.

The HybridRAGPipeline template shows clear steps (embed → search → fuse → rerank), but lacks explicit validation checkpoints. No guidance on verifying search quality, handling failures, or debugging when results are poor.

Content is reasonably organized with templates and best practices sections, but the skill is monolithic with 400+ lines. The four large templates could be split into separate files with SKILL.md providing overview and navigation to detailed implementations.