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hybrid-search-implementation

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

58

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

57%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A clean, well-organized overview that delegates concrete templates to a single well-signaled reference file. Its main weakness is that the SKILL.md body offers direction and best-practice advice but no executable code or explicit step-by-step workflow, leaving the actionable detail entirely in the reference.

Suggestions

Add one minimal end-to-end code snippet in the body (e.g., a 6-line RRF fusion example) so the skill is actionable without opening the reference, keeping details.md for the full templates.

Replace the generic Do's/Don'ts truisms with concrete, non-obvious guidance (e.g., specific RRF k values, recommended weight starting points, or reranker thresholds) that Claude would not already know.

Add a short numbered workflow with a validation checkpoint (e.g., retrieve from both stores → fuse → optionally rerank → verify recall on a held-out query set) to make the multi-step process explicit.

DimensionReasoningScore

Conciseness

The body avoids explaining concepts Claude already knows and uses a compact table plus an ASCII diagram, but the Do's/Don'ts list contains generic truisms ("A/B test", "Tune weights empirically") that could be tightened.

2 / 3

Actionability

The fusion-methods table and best-practices list give concrete direction, but the body itself contains no executable code or commands; all runnable templates are deferred one level deep to references/details.md.

2 / 3

Workflow Clarity

The architecture diagram shows a clear query→vector+keyword→fusion→results sequence, but there is no explicit multi-step workflow with checkpoints or error-recovery guidance, leaving the sequence implicit.

2 / 3

Progressive Disclosure

The body is a concise overview with a single, clearly signaled one-level-deep reference ("Full template library and detailed worked examples live in references/details.md"), and the referenced file exists with no further nesting.

3 / 3

Total

9

/

12

Passed

Description

75%

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 well-structured description that answers both what and when with an explicit trigger clause and a distinctive niche. It is slightly held back by listing only one combined action and by missing a few natural search-term variations.

DimensionReasoningScore

Specificity

"Combine vector and keyword search for improved retrieval" names the domain and a concrete combined action, but it describes one fused capability rather than multiple distinct concrete actions as the level-3 anchor requires.

2 / 3

Completeness

It clearly states what it does ("Combine vector and keyword search for improved retrieval") and gives an explicit "Use when..." trigger clause covering RAG systems, search engines, and insufficient recall.

3 / 3

Trigger Term Quality

"RAG systems" and "search engines" are natural terms users would say, but common variations like "hybrid search", "semantic search", or "BM25" are absent, so coverage is partial rather than complete.

2 / 3

Distinctiveness Conflict Risk

The hybrid-search niche is specific, and the distinctive trigger "when neither approach alone provides sufficient recall" makes it unlikely to fire for a pure-vector or pure-keyword skill.

3 / 3

Total

10

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
wshobson/agents
Reviewed

Table of Contents

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