# Libeval

> libeval - RAG evaluation system. Evaluator orchestrates quality assessment using LLM-as-judge patterns. CriteriaEvaluator scores responses against rubrics. RecallEvaluator measures retrieval performance. TraceEvaluator analyzes execution traces. EvalStore persists results. Use for automated quality testing, RAG pipeline evaluation, and agent performance testing

- Skill: `majiayu000/libeval` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/libeval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/libeval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/libeval

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# libeval Skill

## When to Use

- Evaluating RAG agent response quality
- Measuring retrieval recall and precision
- Running automated quality assessments
- Benchmarking agent performance over time

## Key Concepts

**Evaluator**: Main orchestrator that runs test cases through the agent and
collects metrics.

**CriteriaEvaluator**: Uses LLM-as-judge to score responses against defined
criteria and rubrics.

**RecallEvaluator**: Measures how well the retrieval system returns relevant
documents.

**TraceEvaluator**: Analyzes execution traces for performance and correctness.

## Usage Patterns

### Pattern 1: Run evaluation suite

```javascript
import { Evaluator } from "@copilot-ld/libeval";

const evaluator = new Evaluator(config);
const results = await evaluator.run(testCases);
console.log(results.summary);
```

### Pattern 2: Criteria-based evaluation

```javascript
import { CriteriaEvaluator } from "@copilot-ld/libeval";

const criteria = new CriteriaEvaluator(llmClient);
const score = await criteria.evaluate(response, rubric);
```

## Integration

Configured via config/eval.yml. Run via `make eval`. Uses libllm for
LLM-as-judge.