Skylv Prompt Evaluation

SKILL.md

---
name: prompt-evaluation
slug: prompt-evaluation
version: 1.0.0
description: "Evaluate and benchmark AI prompts for quality, consistency, and performance. Triggers: prompt evaluation, prompt testing, prompt quality, prompt benchmark, prompt optimization."
author: SKY-lv
license: MIT-0
tags: [evaluation, prompts, quality]
keywords: [prompt evaluation, prompt testing, prompt quality, prompt benchmark, prompt optimization, LLM evaluation, prompt engineering, prompt scoring]
triggers: prompt-evaluation
---

# Prompt Evaluation

Evaluate and benchmark AI prompts for quality, consistency, and performance. Score, compare, and optimize your prompts systematically.

## Overview

A prompt evaluation framework that helps agents measure prompt quality across multiple dimensions: clarity, specificity, robustness, cost-efficiency, and output consistency. Compare prompt variants and find the optimal version.

## Capabilities

### 1. Quality Scoring

```bash
node evaluate.js score --prompt "Summarize the article" --dimensions clarity,specificity,robustness
node evaluate.js score --prompt-file ./prompts/ --output scores.json
```

Scores prompts on clarity (0-10), specificity (0-10), robustness (0-10), and cost-efficiency (0-10).

### 2. A/B Comparison

```bash
node evaluate.js compare --prompt-a "Summarize" --prompt-b "Write a 3-bullet summary" --trials 50
node evaluate.js compare --config ab-test-config.json
```

Run statistical A/B tests between prompt variants with significance analysis.

### 3. Consistency Check

```bash
node evaluate.js consistency --prompt "Translate to French" --runs 100 --variance-threshold 0.15
node evaluate.js consistency --temperature 0.7 --top-p 0.9
```

Measures output consistency across multiple runs to find the most stable prompts.

### 4. Regression Testing

```bash
node evaluate.js regression --baseline v1.0 --current v1.1 --test-suite golden-set.jsonl
node evaluate.js regression --fail-on-degradation 5%
```

Detects quality regressions between prompt versions using golden test sets.

### 5. Cost Analysis

```bash
node evaluate.js cost --prompt "Long prompt..." --model gpt-4 --estimate-tokens
node evaluate.js cost --compare-prompts --output cost-report.csv
```

Estimates token usage and costs for different prompt variants and models.

## Configuration

```json
{
  "evaluation": {
    "dimensions": ["clarity", "specificity", "robustness", "cost"],
    "scoringModel": "gpt-4",
    "abTest": {
      "trials": 50,
      "significanceLevel": 0.05
    },
    "consistency": {
      "runs": 100,
      "varianceThreshold": 0.15
    },
    "regression": {
      "degradationThreshold": "5%",
      "goldenSet": "./golden-set.jsonl"
    }
  }
}
```

## Use Cases

- Prompt Engineering: Systematically improve prompt quality
- Quality Assurance: Ensure prompts meet quality standards before production
- Cost Optimization: Find prompts that achieve goals with fewer tokens
- Version Control: Track prompt quality across versions
- Agent Tuning: Optimize agent system prompts for consistency