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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.

64

Quality

80%

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tessl review fix ./plugins/llm-application-dev/skills/hybrid-search-implementation/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%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.

An exemplarily lean, well-structured overview that correctly pushes concrete templates into a single one-level-deep reference file. Its weakness is that the body offers almost nothing executable on its own — no inline RRF formula, default parameters, or sequenced implementation steps with validation — so a reader must open the reference file before doing anything.

Suggestions

Inline one minimal copy-paste-ready snippet in the body (e.g. the RRF formula `1/(k + rank)` with a typical `k=60`) so the single most common action is executable without opening the reference file.

Add a short numbered implementation sequence (choose retrieval backends → fetch candidates from both → fuse → optionally rerank → evaluate recall/latency) so the pipeline diagram becomes an actionable workflow.

Include concrete default values or ranges for the tunables the do's reference (e.g. candidate depth per query, initial vector/keyword weight split), turning 'Tune weights empirically' and 'Don't over-fetch' into executable guidance.

DimensionReasoningScore

Conciseness

The ~50-line body is lean with zero padding: a one-line intro, a compact when-to-use list, an ASCII pipeline sketch, a four-row fusion-method table, a pointer to the reference file, and terse do's/don'ts. Every section adds non-obvious guidance (the 'Best For' column, the do/don't heuristics) and assumes Claude's competence, matching anchor 5 ("Lean and efficient; every token earns its place"). A 4 would require noticeable over-explanation, which is absent.

5 / 5

Actionability

The body itself contains no executable code, commands, formulas, or parameter values — guidance like "Use RRF for simplicity" and "Add reranking" is directional, and the concrete templates are deferred to references/details.md via an explicit pointer. That lands on anchor 3 ("Some concrete guidance but incomplete... missing key details"): better than anchor 2's minimal high-level hints because a real, executable template library is one clearly-signaled read away, but short of anchor 4's concrete code in the body covering the common case.

3 / 5

Workflow Clarity

The only sequencing is the conceptual pipeline in the ASCII diagram (Query → both searches → Fusion → Results) plus a when-to-use list; there is no numbered implementation sequence, no validation checkpoints, and no error-recovery guidance for cases the don'ts themselves raise ("empty results, single word queries"). This matches anchor 3 ("Steps listed but validation gaps; sequence present but checkpoints missing or implicit"). It is not a destructive/batch operation, so the workflow-clarity cap of 3 does not force the score — the content simply sits at that level on its own.

3 / 5

Progressive Disclosure

The body is a well-organized overview (~50 lines) that appropriately splits content: templates and worked examples live in the single bundle file references/details.md (verified to exist, 513 lines of executable templates), referenced exactly once and one level deep with a clear instruction — "Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates." This matches anchor 5 ("Clear overview with well-signaled one-level-deep references; content appropriately split").

5 / 5

Total

16

/

20

Passed

Description

78%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.

A strong description with an explicit and concrete 'Use when...' clause and natural trigger terms (RAG, search engines, recall). Its main weakness is thin capability coverage — it states one action (combining the two search modes) without naming supporting actions like fusion, reranking, or weight tuning.

Suggestions

Enumerate 2-3 more concrete capabilities in the 'what' clause, e.g. "Combine vector and keyword search, fuse result rankings (RRF or weighted), and rerank candidates for improved retrieval."

Add common synonyms and terminology to the trigger clause, such as "hybrid search", "semantic search", "BM25", or "full-text search", to catch more natural user phrasings.

Narrow the very broad "building search engines" trigger (which invites overlap with general search-engine skills) to search-retrieval contexts, e.g. "building search retrieval into an application".

DimensionReasoningScore

Specificity

The description names the domain and one concrete action — "Combine vector and keyword search for improved retrieval" — but offers little else; "implementing RAG systems, building search engines" are user contexts, not additional skill capabilities. This matches anchor 3 ("Names domain and 1-2 concrete actions, but not comprehensive"); it lacks the several specific actions (e.g., fusing rankings, reranking, tuning weights) needed for a 4.

3 / 5

Completeness

It explicitly answers both questions: the "what" is "Combine vector and keyword search for improved retrieval" and the "when" is a concrete "Use when" clause with specific triggers ("implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall"). This mirrors the anchor-5 example structure (clear what + explicit 'Use when...' with concrete trigger phrases); a 4 would require the when-clause to be less explicit or specific, which is not the case here.

5 / 5

Trigger Term Quality

Natural trigger terms are present and would plausibly be said by users: "RAG systems", "search engines", "vector and keyword search", "recall". However, common variations like "semantic search", "hybrid search", "BM25", "full-text search", or "embeddings" are missing, matching anchor 4 ("Good keyword coverage; a few natural terms missing") rather than the comprehensive synonym coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

"Combine vector and keyword search" carves out a clear niche with minimal conflict risk. The broad trigger "implementing RAG systems" could overlap with a general RAG-implementation or vector-search skill, which matches anchor 4 ("Mostly distinct; minor overlap risk with closely related skills") rather than the fully conflict-free anchor 5.

4 / 5

Total

16

/

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

wshobson/agents

Path

plugins/llm-application-dev/skills/hybrid-search-implementation/SKILL.md

Reviewed

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