agent-evaluation

Installation
Summary

Comprehensive evaluation framework for designing, building, and monitoring AI agent performance across coding, conversational, research, and computer-use agents.

  • Covers three grader types (code-based, model-based, human) with trade-offs and best practices for each agent category
  • Provides an 8-step roadmap from initial task creation through production monitoring, including environment isolation, outcome-focused grading, and saturation detection
  • Includes benchmarks for major agent types: SWE-bench for coding, WebArena for computer use, τ2-Bench for conversational agents
  • Offers CI/CD integration patterns, A/B testing templates, and production sampling strategies for real-time quality monitoring
SKILL.md

Agent Evaluation (AI Agent Evals)

Based on Anthropic's "Demystifying evals for AI agents"

When to use this skill

  • Designing evaluation systems for AI agents
  • Building benchmarks for coding, conversational, or research agents
  • Creating graders (code-based, model-based, human)
  • Implementing production monitoring for AI systems
  • Setting up CI/CD pipelines with automated evals
  • Debugging agent performance issues
  • Measuring agent improvement over time

Core Concepts

Eval Evolution: Single-turn → Multi-turn → Agentic

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Installs
10.1K
GitHub Stars
88
First Seen
Jan 24, 2026