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rag-architect SKILL.md skillA

rag-architect from jeffallan/claude-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned

mdr add jeffallan/claude-skills/rag-architect@v1.1.0

Pins v1.1.0 in mdr.lock. Pin the fingerprint instead.

Audit grade: A, 17 of 17 checks passed
Kind: SKILL.md skill
Repository: jeffallan/claude-skills
Path: skills/rag-architect/SKILL.md
Versions recorded: 5
Latest version: v1.1.0, committed Oct 3, 2026
First version recorded: Mar 6, 2026
Size: 8,049 bytes
Repository stars: 12,006
License: MIT

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.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# RAG Architect

## Core Workflow

1. **Requirements Analysis** — Identify retrieval needs, latency constraints, accuracy requirements, and scale
2. **Vector Store Design** — Select database, schema design, indexing strategy, sharding approach
3. **Chunking Strategy** — Document splitting, overlap, semantic boundaries, metadata enrichment
4. **Retrieval Pipeline** — Embedding selection, query transformation, hybrid search, reranking
5. **Evaluation & Iteration** — Metrics tracking, retrieval debugging, continuous optimization

For each step, validate before moving on (see checkpoints below).

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| Vector Databases | `references/vector-databases.md` | Comparing Pinecone, Weaviate, Chroma, pgvector, Qdrant |
| Embedding Models | `references/embedding-models.md` | Selecting embeddings, fine-tuning, dimension trade-offs |
| Chunking Strategies | `references/chunking-strategies.md` | Document splitting, overlap, semantic chunking |
| Retrieval Optimization | `references/retrieval-optimization.md` | Hybrid search, reranking, query expansion, filtering |
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint

mdr add jeffallan/claude-skills/rag-architect@sha256:36e21290e5df36ed

The line at the top of this page pins the label v1.1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

[![mdr](https://markdownregistry.com/badge/art_j53uulkgjkmsf2l4.svg)](https://markdownregistry.com/a/art_j53uulkgjkmsf2l4)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 5 versions of this file, between Mar 6, 2026 and Oct 3, 2026, about one every 53 days.

version	committed	commit	size	audit
v1.1.0 latest	2026-10-03	958e60f	8,049 B	A	view · diff
v1.1.0	2026-10-03	ec755b7	7,967 B	A	view · diff
v1.1.0	2026-08-07	d759b15	7,873 B	A	view · diff
v1.1.0	2026-04-29	efebc44	7,741 B	A	view · diff
v1.1.0	2026-03-06	c7afefb	7,651 B	A	view

Audit of the latest version

A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.

pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (8049 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag

Source

GitHub

jeffallan/claude-skills · 12,006 stars · license MIT · pushed 2026-10-21 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_j53uulkgjkmsf2l4
GET https://markdownregistry.com/api/v1/resolve?ref=jeffallan/claude-skills/rag-architect
GET https://markdownregistry.com/api/v1/blob/36e21290e5df36ed3eaf007d0e91781c7453dc46c6dedd6d31c1c382b28cf137

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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