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agent-evaluation

Design evaluation systems for AI agents with benchmarks and grader rubrics.

Updated Dec 20, 2023
Search Tags:#quality assurance#ci/cd#llm-as-judge#benchmarking#transcript analysis#agent evaluation#grader design
One-click install
npx skills add https://github.com/Thiago-Cruz-eng/Hibrygame --skill agent-evaluation-thiago-cruz-eng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/Thiago-Cruz-eng/Hibrygame/tree/main/.claude/skills/agent-evaluation
Command: npx skills add https://github.com/Thiago-Cruz-eng/Hibrygame --skill agent-evaluation-thiago-cruz-eng

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the problem of reliably measuring and improving AI agent quality so you can catch regressions, compare versions, and ship safer performance.

Core Features & Use Cases

  • Grader Design: Create code-based graders, model-based rubrics (LLM-as-judge), and human grading workflows for different agent types.
  • Benchmark & Suite Planning: Build task suites with balanced positive/negative/edge cases, reference solutions, and outcome-focused success criteria.
  • Production Integration: Connect eval harnesses to CI/CD, run sampled real-time monitoring, and perform A/B testing to decide which agent version wins.
  • Debugging & Maintenance: Analyze transcripts for failure diagnosis, detect eval saturation, and keep graders and benchmarks calibrated over time.

Quick Start

Use the agent-evaluation skill to design a complete evaluation suite for your coding agent by selecting code-based graders, defining task outcomes, and wiring it into your CI pipeline for automated regression checks.

Frequently Asked Questions about agent-evaluation

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

FAQPage Schema
How do I build an evaluation system for AI agents to catch regressions?▼

Agent evaluation measures AI quality by combining code-based graders, model-based rubrics, and human grading workflows to assess task outcomes across single-turn and agentic workflows.

What's the best way to use LLM-as-judge for grading conversational agents?▼

LLM-as-judge grading uses model-based rubrics to evaluate conversational agent transcripts, providing a scalable grading mechanism that defines outcome-focused success criteria for complex interactions.

How do I integrate benchmark task suites into a CI/CD pipeline?▼

Benchmark integration connects your eval harness to CI/CD pipelines, running sampled task suites with reference solutions to automatically detect performance regressions and compare agent versions during deployment.

Can I use transcript analysis to debug failing AI agent tasks?▼

Transcript analysis diagnoses agent failures by reviewing interaction logs against grader rubrics, helping you identify failure patterns, detect eval saturation, and keep benchmarks calibrated over time.

Does agent evaluation support A/B testing for different agent versions?▼

Agent evaluation supports A/B testing by running sampled real-time monitoring across task suites, measuring outcome-focused success criteria to decide which agent version performs better in production.

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