rag-architect

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

Quality

95%

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Quality

Content

90%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.

A well-architected skill body: executable code, explicit checkpoints and metric gates, and a clean reference table linking to real, appropriately-scoped detail files. Minor verbosity in the constraint and template sections is the only detractor.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with executable code and lean section structure, but the MUST DO/MUST NOT DO lists and Output Templates carry some redundancy with the workflow steps already shown.	4 / 5
Actionability	Fully executable, copy-paste-ready Python examples cover chunking, indexing, hybrid search, reranking, and evaluation, with concrete checkpoints and metric thresholds.	5 / 5
Workflow Clarity	Clear five-step sequenced workflow with per-step assertion-based checkpoints and a target-metric gate ("Target context_precision >= 0.7...") providing explicit validation and feedback loops.	5 / 5
Progressive Disclosure	Clean overview SKILL.md with a well-signaled reference table pointing one level deep to five real, topic-organized reference files (all verified to exist in references/), each with an explicit "Load When" trigger.	5 / 5
	Total	19 / 20 Passed

Description

100%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, well-crafted description that clearly states concrete capabilities and provides explicit, natural trigger phrases. It answers both what and when comprehensively and occupies a distinct niche with minimal conflict risk.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions ("chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality") with comprehensive coverage of the RAG pipeline.	5 / 5
Completeness	Explicitly answers both what ("Designs and implements production-grade RAG systems by...") and when ("Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring...") with concrete trigger phrases.	5 / 5
Trigger Term Quality	Comprehensive natural trigger terms including synonyms and extensions ("RAG systems, vector databases... semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing") that users would naturally say.	5 / 5
Distinctiveness Conflict Risk	Clear niche (RAG architecture) with distinct triggers and minimal overlap risk; the combination of retrieval, embeddings, and vector store design is unlikely to conflict with unrelated skills.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

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: jeffallan/claude-skills
Path: skills/rag-architect/SKILL.md
Commit: 882ef55

Reviewed: 3 days ago

Table of Contents

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