Conciseness

The body is dense and purposeful — no concept-teaching prose and every section maps to a workflow step — but there are trims to make: the '## References' bullet list duplicates the link-definition block immediately below it, 'requires no normalization' is stated twice within Step 4, and the BEIR +12.08% statistic is repeated in the intro and Limitations. This fits anchor 4 (efficient with minor instances that could be trimmed) rather than anchor 3, since the inline nDCG/MRR code is a canonical harness fixing evaluation conventions, not an explanation of concepts Claude already knows.

Actionability

Provides a copy-paste-ready evaluate() harness, three concrete Elasticsearch retriever bodies, and a runnable pytest regression gate. However, the usage lines invoke bm25_retrieve_fn/knn_retrieve_fn/rrf_retrieve_fn which are never defined (and the defined retrievers have signatures incompatible with retrieve_fn(query_text)), and query_vector and measure_latency_p95 are never shown — so the code does not run end-to-end as written. This places it at anchor 4 (mostly executable with minor gaps) rather than anchor 5 (fully executable).

Workflow Clarity

Seven steps are clearly sequenced (judgment set → metrics → baselines → fusion variants → reranker → regression gate), with explicit validation (the Step 7 CI threshold test), feedback loops (the weight sweep and the 'lift < 0.01 nDCG → reranker is not earning its cost' decision rule), and a checklist (the anti-patterns table). This matches anchor 5; the destructive/batch validation cap does not apply to a measurement skill.

Progressive Disclosure

Both referenced files (references/judgment-sets.md, references/weighted-fusion-and-reranking.md) exist, are one level deep, and are clearly signaled with descriptive lead-ins naming exactly what each holds; labeling recipes and engine-specific fusion/reranker code are appropriately moved out while the core workflow and metric harness stay inline. Matches anchor 5 (clear overview with well-signaled one-level-deep references).

Specificity

Lists four concrete capabilities — "judgment sets, nDCG@k and MRR on fused results, Reciprocal Rank Fusion vs weighted fusion vs single-stage, and whether the reranker earns its latency" — comprehensively covering the skill's scope with no evident gaps. Anchor 4 would require minor coverage gaps, which are not present.

Completeness

Explicitly answers both parts: the "Use when a search system combines keyword (BM25) and vector retrieval, with or without a reranker, and needs numbers" clause gives a concrete when, and the colon-delimited list gives a clear what. Anchor 5 matches; anchor 4 would require the 'when' to be less explicit or specific.

Trigger Term Quality

Strong technical keyword coverage ("BM25", "vector retrieval", "reranker", "nDCG@k", "MRR", "Reciprocal Rank Fusion"), but the most natural user phrases — "hybrid search", "semantic search", "RAG" — never appear in the description itself. This fits anchor 4 (good coverage, a few natural terms missing) rather than 5, and is well above anchor 3's 'some relevant keywords'.

Distinctiveness Conflict Risk

The trigger is scoped to fused BM25+vector systems needing numbers, which cleanly excludes single-stage vector-recall and BM25-only relevance skills. Clear niche with distinct triggers and minimal conflict risk, matching anchor 5.