eval-pipeline

Generate code, grade with multiple evaluators, and compile reports for AI prompts.

3|5|Updated Mar 19, 2026

Search Tags:#evaluation #pipeline #review #ai-agent #report #grading

Ready to run

npx skills add https://github.com/ronniegeraghty/hyoka --skill eval-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: eval-pipeline
Source: https://github.com/ronniegeraghty/hyoka/tree/main/.agents/skills/eval-pipeline
Command: npx skills add https://github.com/ronniegeraghty/hyoka --skill eval-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The eval pipeline provides a structured, end-to-end workflow to generate, grade, review, and report on AI-agent prompts, enabling consistent evaluation and faster debugging of prompts and agents.

Core Features & Use Cases

End-to-end orchestration of generation, multi-model grading, reviewer feedback, and report generation for AI prompts.
Provides detailed action timelines and workspace isolation to ensure reproducibility and auditability.
Use Case: Run a full evaluation for a given prompt to identify strengths and weaknesses across generation quality, adherence to guidelines, and safety considerations.

Quick Start

Run an evaluation pipeline that generates code, grades it using multiple graders, and compiles a final report for a selected prompt.

Frequently Asked Questions about eval-pipeline

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

FAQPage Schema

How do I run an end-to-end evaluation pipeline for AI agent prompts?▼

An end-to-end evaluation pipeline orchestrates code generation, multi-model grading, and report compilation for AI agent prompts. It assesses quality, safety, and guideline adherence while capturing action timelines and isolating workspaces to ensure reproducible pass/fail experiment tracking.

What is multi-model grading for AI agents and how does it work?▼

Multi-model grading evaluates AI agent prompts using multiple evaluators to assess generation quality, safety, and adherence. The pipeline applies these graders in parallel with configurable limits, compiling their feedback into a comprehensive pass/fail report for faster debugging.

Can I use this evaluation pipeline to assess the safety and adherence of generated code?▼

Yes, the evaluation pipeline assesses both safety and adherence of generated code. It applies multiple evaluators across diverse services to grade outputs, capturing detailed action timelines to audit how well AI agents follow guidelines and safety considerations.

How do I ensure reproducible AI agent evaluation across different experiments?▼

To ensure reproducible AI agent evaluation, the pipeline implements workspace isolation and captures detailed action timelines. This structured approach guarantees that generation, grading, and reporting steps are tracked consistently across experiments for reliable debugging.

What is the best way to debug AI agent prompts using an automated grading pipeline?▼

The best way to debug AI agent prompts is using an automated pipeline that generates code, applies multiple graders, and compiles a comprehensive report. This structured workflow identifies strengths and weaknesses across quality and safety, enabling faster debugging.