write-agent-benchmarks

What problem does it solve?

This Skill addresses the challenge of objectively evaluating AI agents' performance across various real-world tasks.

Core Features & Use Cases

• Evidence-Based Verification: Ensures trust in an agent's actions rather than just its claims.
• Strict Isolation: Ensures clean and reproducible test environments to prevent side effects.
• Black Box Protocol: Evaluates agents based on input/output without knowledge of their internal workings.
• Reproducibility: Enables consistent benchmarking through deterministic execution and fixed seeds.
• Universal Applicability: Supports any agent type, including CLI, API, and chat-based agents.
• Evaluation Modes: Offers quality evaluation, model selection, and version comparison.
• Interaction Strategies: Provides atomic request verification and user emulation for stable benchmarks.
• Architecture & Requirements: Includes environment, runner, simulated user, judge, and observability modules.
• Workflows: Guides users through creating new benchmarks and running them effectively.
• Mock Pitfalls: Helps avoid common pitfalls in mock scenarios for accurate benchmarking.
• Universal Result Schema: Ensures cross-platform compatibility of benchmark results.
• Configuration Principles: Emphasizes preset-based management, role separation, and reproducibility.
• Assets & References: Provides templates and configurations for scenarios and benchmarks.

Quick Start

Run the 'write-agent-benchmarks' skill to evaluate an AI agent's performance on a specific task.