Conciseness

Lean body that assumes Claude's competence — it never explains what RAG, embeddings, or vector DBs are, and every section (hard rules, tier table, workflow commands) earns its tokens with actionable guidance rather than concept padding.

Actionability

Provides fully executable copy-paste commands for all three tools with real flags and output paths, plus explicit required-keys schemas for the input JSON files and documented output fields.

Workflow Clarity

Four-step pipeline is clearly sequenced (corpus analysis → design → evaluation → verification) with explicit validation checkpoints (precision@5 ≥ 0.8, recall@10 ≥ 0.85) and a feedback loop ('change one variable... and re-run step 3. Repeat').

Progressive Disclosure

Body is a concise overview; detailed strategy comparisons, benchmark methodology, and metric definitions are split into three well-signaled one-level-deep reference files that all exist and are accurately described in the References section.

Specificity

Lists multiple concrete actions ('design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG)') with comprehensive coverage of the skill's scope.

Completeness

Explicitly answers both 'what' (design/choose/evaluate RAG components) and 'when' ('Use when the user asks...') with concrete trigger phrases and example utterances.

Trigger Term Quality

Includes literal natural user utterances as examples ('design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth') plus technical synonyms users would say.

Distinctiveness Conflict Risk

Clear RAG-design niche with distinct triggers and explicit boundary guidance ('NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub)') minimizing conflict risk.