eval-harness

Compare two variants in parallel with a model grader and structured report.

4|Updated Jan 9, 2026
One-click install
npx skills add https://github.com/baleen37/bstack --skill eval-harness-baleen37
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: eval-harness
Source: https://github.com/baleen37/bstack/tree/main/plugins/me/skills/eval-harness
Command: npx skills add https://github.com/baleen37/bstack --skill eval-harness-baleen37

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamlines and standardizes variant comparisons by running two options in parallel and using a model-based grader to produce an objective, comparable assessment.

Core Features & Use Cases

  • Parallel variant execution with two subagents and a single evaluation pass.
  • Supports variant types: code, llm, command, and custom workflows with a unified input specification (TASK, VARIANT_A, VARIANT_B, INPUTS, Evals).
  • Generates a structured report including per-criterion verdicts and an overall winner or tie, with anonymization for fair judging.

Quick Start

Provide a task and two variants with inputs to initiate automated parallel evaluation and grading.

Frequently Asked Questions about eval-harness

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

FAQPage Schema
How do I compare two code or prompt variants with automated parallel testing?

You can compare code, LLM prompts, CLI commands, or custom workflows by providing the TASK, VARIANT_A, VARIANT_B, INPUTS, and Evals fields to initiate automated parallel evaluation and grading.

What is parallel variant evaluation and how does it work?

Parallel variant evaluation uses two subagents to execute different options simultaneously with identical inputs, followed by a single model-based grading pass that produces an objective, comparable assessment with per-criterion verdicts.

Can I use this automated grading harness for LLM prompts and CLI commands?

Yes, the harness supports variant types including code, LLM, command, and custom workflows, using a unified input specification to ensure identical inputs across both variants for fair, apples-to-apples comparison.

What is the best way to run an apples-to-apples comparison between two implementations?

The best way is using a structured harness that anonymizes variants for fair judging, runs them in parallel with identical inputs, and applies a model grader to evaluate observable outputs across defined criteria.

Do I need to provide specific inputs to start automated variant comparison?

Yes, you must provide the TASK, VARIANT_A, VARIANT_B, INPUTS, and Evals fields to initiate the automated parallel evaluation and grading process for accurate variant comparison.