kelly-agent-eval

Run local LLM-agent regression reviews against a baseline with mock test cases.

4|3|Updated Feb 25, 2026
One-click install
npx skills add https://github.com/mr-kelly/skills --skill kelly-agent-eval
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kelly-agent-eval
Source: https://github.com/mr-kelly/skills/tree/main/skills/kelly-agent-eval
Command: npx skills add https://github.com/mr-kelly/skills --skill kelly-agent-eval

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hono, @hono/node-server, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill solves the challenge of maintaining quality in LLM-agent workflows by providing a structured, deterministic way to catch regressions before a release. It prevents quality drift by comparing new agent versions against a established baseline using a fixed suite of test cases.

Core Features & Use Cases

  • Regression Detection: Automatically identifies cases where a candidate agent version scores lower than the baseline on a four-part rubric.
  • Review Workflow: Provides a local dashboard to triage regressions, record human decisions, and approve or block releases.
  • Use Case: Before deploying a new version of a support agent, use this board to verify that the new model maintains performance across support, reasoning, and safety categories without introducing new errors.

Quick Start

Use the kelly-agent-eval skill to open the local review board and triage the current candidate agent regressions.

Frequently Asked Questions about kelly-agent-eval

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

FAQPage Schema
How do I catch LLM agent regressions before a release?▼

Testing agent performance regressions requires running a fixed suite of mock test cases against a candidate version. It compares results against a baseline version using a four-part rubric to identify quality drift in support, reasoning, and safety categories.

How does a local review board manage human-in-the-loop release decisions?▼

A local review board manages human-in-the-loop release decisions by providing a dashboard to triage detected regressions, record human decisions, and approve or block releases based on deterministic test outcomes.

Do I need a local environment to run agent regression testing with Hono?▼

Yes, agent regression testing with Hono requires a local environment to execute the server. It relies on local execution to run the Hono-based server and manage handoff files for release reporting.

What is the best way to evaluate agent quality drift?▼

The best way to evaluate agent quality drift is to compare new agent versions against an established baseline. This approach automatically identifies cases where a candidate agent scores lower on a four-part rubric.

When should I use a baseline comparison for agent quality assurance?▼

You should use a baseline comparison for agent quality assurance before deploying a new version. This verifies that the new model maintains performance across categories without introducing new errors.