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rag-evaluation-harness

Evaluate retrieval and citation behavior for RAG pipelines from deterministic JSONL fixtures. Use when an agent needs offline Recall@K, reciprocal rank, context precision, citation coverage, citation validity, diagnostics, Markdown/JSON…

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What it does and how to use it

A skill that teaches Claude Code to do general tasks well. It is packaged knowledge the model uses whenever it is relevant.

  1. Install the skill (command above).
  2. In Claude Code, describe what you need - the skill turns on by itself when the context fits.
  3. No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/davepoon-subagents__rag-evaluation-harness && curl -fsSL https://doutorai.pt/data/lib/skills__davepoon-subagents__rag-evaluation-harness.md -o .claude/skills/davepoon-subagents__rag-evaluation-harness/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

---
name: rag-evaluation-harness
category: ai-ml
description: "Evaluate retrieval and citation behavior for RAG pipelines from deterministic JSONL fixtures. Use when an agent needs offline Recall@K, reciprocal rank, context precision, citation coverage, citation validity, diagnostics, Markdown/JSON reports, or threshold-gated evaluation in CI."
---

# RAG Evaluation Harness

Use this skill to measure a retrieval-and-citation contract without making model calls or network requests. The bundled evaluator compares explicit document IDs, so it is suitable for repeatable local checks and CI gates.


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