eval-harness

Run reproducible LLM pipeline evaluations with test cases, rubrics, and pass/fail tracking.

42|9|Updated Mar 16, 2026
One-click install
npx skills add https://github.com/drvoss/everything-copilot-cli --skill eval-harness-drvoss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/drvoss/everything-copilot-cli/tree/main/skills/testing/eval-harness
Command: npx skills add https://github.com/drvoss/everything-copilot-cli --skill eval-harness-drvoss

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Evaluates and validates LLM-powered features with a repeatable harness, reducing ad-hoc QA and regression risk.

Core Features & Use Cases

  • Eval definitions with inputs, expected outputs, and scoring rubrics
  • Runner to execute the pipeline against all test cases
  • Scorer to apply rubrics and record results
  • Tracker to maintain pass/fail history across runs (via SQL session DB)
  • Use cases include regression testing for prompts, feature evaluations, and quality demonstrations for CI

Quick Start

Run the harness against your LLM workflow to produce baseline results and a traceable evaluation history.

Frequently Asked Questions about eval-harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build reproducible evals for LLM pipelines?▼

Build reproducible evals for LLM pipelines by defining test cases, scoring rubrics, and expected outputs. A runner executes the pipeline against all cases, while a scorer applies rubrics to record pass/fail results and track evaluation history.

What is an evaluation harness for LLM quality assurance?▼

An evaluation harness for LLM quality assurance is a structured framework that measures LLM pipelines using test cases and rubrics. It replaces ad-hoc QA by providing a repeatable runner, scorer, and historical tracker to reduce regression risk.

Can I use an eval harness for prompt-injection resilience testing?▼

Yes, you can use an eval harness for prompt-injection resilience testing by configuring optional adversarial tests. These tests run alongside standard evaluation cases to measure and track your pipeline's resistance against prompt-injection attacks.

How do I track LLM regression testing results across multiple runs?▼

Track LLM regression testing results across multiple runs using a tracker component backed by a SQL session DB. It maintains a persistent pass/fail history, allowing you to compare current evaluation outcomes against past baseline results.

Does the evaluation framework support testing agent workflows?▼

Yes, the evaluation framework supports testing agent workflows. It applies across various evaluation tasks including prompts, complex pipelines, and agent workflows, using a consistent configuration system for all test cases and rubrics.