Every premium and members-only resource for $9.99/month, as a no-commitment subscription. See the plans

PalateKit

Menu

Request a resource

Describe the resource you are missing in one sentence. We will email you when it is published.

0 / 280

Unlock PalateKit

Unlock everything

Plan

Paying means accepting our terms of sale.

Secure payment by Stripe. Receipts and invoices sent by email.

Welcome back

Sign in to install, download or save this resource. A free account is enough for free resources.

RAG evaluation: RAGAS, recall and gold sets v1.0.0

Use this resource to measure the retrieval and answer quality of a RAG system

New

  • 434 downloads
  • 203 likes
  • 184 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install rag-evaluation-metrics

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent evaluates retrieval (Hit@K, MRR, NDCG) and generation (faithfulness, relevance) separately with RAGAS, DeepEval or a custom LLM judge, and builds a gold dataset. You get reproducible metrics and an A/B test plan.

Mostly picked by

Updated November 2, 2026

Card for the “RAG evaluation: RAGAS, recall and gold sets” skill

Details

Generates with
Your agent's model
Licence
MIT

Similar resources

View all

What you get

  • SKILL.md: retrieval metrics Hit@K, MRR and NDCG
  • SKILL.md: RAGAS, DeepEval, TruLens, LLM judge and golden datasets

Good fit for

  • You do not know whether your RAG improves after a change
  • You want to evaluate the retriever separately from the model

Not for

    Tags

    • rag
    • evaluation
    • ragas
    • metrics

    About

    You say

    “Compute the Hit@10 and faithfulness of my RAG on a set of fifty reference questions.”

    Card for the “RAG evaluation: RAGAS, recall and gold sets” skill

    Skill published by claude-dev-suite under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to measure the retrieval and answer quality of a RAG system
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • SKILL.md10.1 KB

    Origin and licence

    Listed unmodified

    Author
    claude-dev-suite
    Source
    github.com/claude-dev-suite/claude-dev-suite/tree/e10ab644ba43abe2abc923399226df4af7337214/skills/rag/rag-evaluation
    Licence
    MIT

    Notice

    « rag-evaluation » (claude-dev-suite, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

    User reviews

    –

    No ratings yet

    Loading reviews…

    Delete your review?

    Your rating and comment will be removed from the page.