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Agent Evaluation

Evaluate AI agent performance against a structured scoring rubric.

Updated Feb 13, 2026

Search Tags:#ai performance #performance review #scoring rubric #agent evaluation #agent assessment #improvement recommendations

Ready to run

npx skills add https://github.com/cdalsoniii/brightpath-coder --skill agent-evaluation-cdalsoniii

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Agent Evaluation
Source: https://github.com/cdalsoniii/brightpath-coder/tree/main/.cursor/skills/agent-evaluation
Command: npx skills add https://github.com/cdalsoniii/brightpath-coder --skill agent-evaluation-cdalsoniii

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of objectively measuring and improving the performance of AI agents by providing a structured framework for evaluation.

Core Features & Use Cases

Structured Scoring: Evaluates agents across predefined dimensions like architecture, security, operations, testing, and documentation.
Performance Tracking: Compares current performance against historical baselines to identify trends.
Improvement Recommendations: Generates actionable insights and suggestions for enhancing agent capabilities.
Use Case: A product team can use this Skill to regularly assess their AI coding assistants, ensuring they meet quality standards and identifying areas for targeted development.

Quick Start

Use the Agent Evaluation skill to evaluate the security-specialist agent.

Frequently Asked Questions about Agent Evaluation

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

FAQPage Schema

How do I evaluate AI agent performance using a structured scoring rubric?▼

To evaluate AI agent performance, this skill applies a structured scoring rubric across multiple dimensions including architecture, security, operations, testing, and documentation to objectively measure and score agent capabilities.

What is the best way to track AI agent performance trends over time?▼

Tracking AI agent performance trends is achieved by comparing current evaluation scores against historical baseline scores. This comparison highlights performance shifts and generates actionable improvement recommendations for the agent.

How do I assess my AI coding assistant against quality standards for security and architecture?▼

Assessing an AI coding assistant involves evaluating its outputs against predefined quality dimensions like security and architecture. The skill analyzes agent configurations and invocation history to generate a detailed evaluation report.

What data do I need to generate actionable improvement recommendations for an AI agent?▼

Generating actionable improvement recommendations requires read access to agent configurations, output logs, and telemetry data. The skill uses this data to evaluate operations and testing dimensions, writing final assessment reports.

Can I use an agent assessment tool if I only have output logs and no telemetry data?▼

Evaluating an agent without telemetry data is not recommended. The skill requires read access to agent configurations, output logs, and telemetry to accurately score operations, testing, and documentation dimensions for a complete evaluation.

Does the agent evaluation process support automated scoring for security specialist agents?▼

Automated agent evaluation supports security specialist agents by scoring their performance across security, architecture, and operations dimensions. It analyzes invocation history and telemetry to ensure the agent meets predefined quality standards.