ai-rag-engineering

v0.1.3

AI / RAG Engineering specialist team — agents (rag-architect-lead, retrieval-eval-analyst, ingestion-chunking-specialist, llm-serving-cost-specialist), skills, templates, commands, an advisory hook, best-practice rules, and a research-grounded knowledge bank. A RAG team for an ML engineer or AI product lead accountable for answer quality and serving cost — it fixes retrieval before generation, treats chunking as a retrieval decision, evals before it ships, and reads context-window and token economics. Inherits ravenclaude-core protocols.

Claude Code5 Skills

By Matt CorbettLicense: MIT7 GitHub starsUpdated last week

Directory evidence

Runtimes: Claude Code
Parsed components: 5 skill or MCP entries
Source updated: Sep 15, 2026
Manifest status: Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install ai-rag-engineering for Claude Code

Installs for the current user

claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install ai-rag-engineering@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually

$ git clone https://github.com/mcorbett51090/RavenClaude

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/ai-rag-engineering/.

Plugin files

plugins/ai-rag-engineering/

├── .claude-plugin/plugin.json
├── skills/budget-tokens/SKILL.md
├── skills/build-rag-eval/SKILL.md
├── skills/diagnose-retrieval/SKILL.md
├── skills/ground-and-guardrail/SKILL.md
└── skills/tune-chunking/SKILL.md

Included Skills5

budget-tokensskills/budget-tokens/SKILL.md

Compute cost per request and right-size the context to fewest-high-precision chunks. Reach for this on a cost/context question.

build-rag-evalskills/build-rag-eval/SKILL.md

Build a judgment set and measure recall@k, precision@k, faithfulness, and answer-relevance with a baseline. Reach for this before shipping any change.

diagnose-retrievalskills/diagnose-retrieval/SKILL.md

Separate retrieval failure from generation failure by measuring recall@k before touching the model. Reach for this first on wrong answers.

ground-and-guardrailskills/ground-and-guardrail/SKILL.md

Add citations, refuse-on-empty-retrieval, and context-constraint to cut hallucination. Reach for this on a faithfulness question.

tune-chunkingskills/tune-chunking/SKILL.md

Tune chunk size, overlap, and structure-awareness against the eval and the context budget. Reach for this on a chunking question.

Plugin manifests1

plugins/ai-rag-engineering/.claude-plugin/plugin.json

{
  "name": "ai-rag-engineering",
  "version": "0.1.3",
  "description": "AI / RAG Engineering specialist team — agents (rag-architect-lead, retrieval-eval-analyst, ingestion-chunking-specialist, llm-serving-cost-specialist), skills, templates, commands, an advisory hook, best-practice rules, and a research-grounded knowledge bank. A RAG team for an ML engineer or AI product lead accountable for answer quality and serving cost — it fixes retrieval before generation, treats chunking as a retrieval decision, evals before it ships, and reads context-window and token economics. Inherits ravenclaude-core protocols.",
  "author": {
    "name": "Matt Corbett"
  },
  "homepage": "https://github.com/mcorbett51090/RavenClaude",
  "license": "MIT",
  "keywords": [
    "rag",
    "retrieval",
    "chunking",
    "embeddings",
    "eval",
    "hybrid-search",
    "llm-serving",
    "token-cost"
  ],
  "requires": {
    "plugins": [
      "ravenclaude-core@>=0.7.0"
    ]
  }
}